engineering physics - ingegneria fisica

1. Introduzione all'apprendimento statistico. Il tradeoff tra bias e varianza.
2. La maledizione della dimensionalità e la riduzione della dimensionalità. Analisi delle componenti principali.
3. Classificazione non supervisionata. Clustering gerarchico, clustering K-means.
4. Modelli lineari. Regressione lineare semplice e multipla. Adattamento del modello attraverso i minimi quadrati ordinari e la massima verosimiglianza, valutazione
dell'accuratezza delle stime dei coefficienti, valutazione dell'accuratezza del modello, intervalli di previsione. Predittori qualitativi e interazioni. Selezione del modello e
regolarizzazione: metodi di shrinkage (ridge regression e lasso), metodi di riduzione della dimensionalità.
5. Classificazione supervisionata. Regressione logistica e stima di massima verosimiglianza. Analisi discriminante lineare e quadratica.
6. Metodi di campionamento. Cross-validation e bootstrap.
7. Metodi basati sugli alberi. Alberi di classificazione e regressione. Bagging, random forests, boosting.

Nowadays, deep neural networks can outperform traditional hand-crafted algorithms, achieving human-level performance on many complex tasks, such as natural language processing, text modeling, gene expression modeling, and image recognition. The course provides a broad introduction to neural networks (NNs), ranging from traditional feedforward (FFNNs) and recurrent (RNNs) to the most successful deep learning models, such as convolutional neural networks (CNNs) and long short-term memory (LSTM).
The course's primary goal is to provide students with the theoretical background and practical skills to understand and use NN, and to become familiar with Deep Learning for solving complex engineering problems.
This goal is pursued in the course by
Presenting major theoretical results underpinning NN (e.g., universal approximation, vanishing/exploding gradient, etc.)
Describing the most important algorithms for NN training (e.g., backpropagation, adaptive gradient algorithms, etc.)
Illustrating the best practices on how to successfully train and use these models (e.g., dropout, data augmentation, etc.)
Providing an overview of the most successful Deep Learning architectures (e.g., convolutional networks, autoencoders for embedding, long-short term memories for sequence-to-sequence learning, etc.)
Providing an overview of the most successful applications with particular emphasis on models for solving visual recognition tasks.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn:
· What are neural networks and the fundamental methods and techniques used for neural networks training
· What are the fundamental architectures used in deep learning, such as convolutional neural networks and long-short term memories, and when to use them
· What are the neural network architectures used in the different visual recognition tasks
· What are the main differences between machine learning and deep learning
Applying knowledge and understanding
Given a specific data analysis problem, the student will be able to:
· Identify which paradigm better describes a given problem
· Identify which technique to start from with the analysis, apply it to model the data and evaluate its outcome
· Implement fundamental deep learning algorithms for image and text analysis autonomously
Making judgments
Given a complex data analysis problem, students will be able to:
· Identify the most relevant model to be applied in the specific problem
· Identify the occurrence of overfitting by the model under analysis
· Iteratively refine the selected model in order to balance performance, computational complexity and overfitting
· Compare and select different models for the problem under analysis
Communication
The student will learn to:
· Discuss in written form the pros and cons of different machine learning techniques for a specific problem
Lifelong learning skills
The student will learn to:
· Face a real-life data analysis problem with a sound and complete methodological approach
· Understand complex machine learning techniques beyond the fundamental ones presented during lectures
· Develop new machine learning pipelines adapting to the specific problem at hand

Neural networks are mature, flexible, and powerful non-linear data-driven models that have successfully been applied to solve complex tasks in science and engineering. The advent of the deep learning paradigm, i.e., the use of (neural) network to simultaneously learn an optimal data representation and the corresponding model, has further boosted neural networks and the data-driven paradigm. These topics will be described in the course according to the following detailed program:
From the Perceptron to Neural Networks and the Feedforward architecture
Backpropagation and Neural Networks training algorithms, e.g., Adagrad, Adam, etc.
Best practices in neural network training: overfitting and cross-validation, stopping criteria, weight decay, dropout, data resampling, and augmentation.
Image Classification Problem and Neural Networks
Recurrent Neural Networks and other relevant architectures, such as (Sparse) Neural Autoencoders
Theoretical results: Neural Networks as universal approximation tools, vanishing and exploding gradients, etc.
Introduction to the Deep Learning paradigm and its main differences with respect to classical Machine Learning
Convolutional Neural Networks (CNN) architecture
The breakthrough of CNN and its interpretation
CNN training and data-augmentation
Structural learning, Long-Short Term Memories, and their applications to text and speech
Autoencoders and data embedding, word2vec, variational autoencoders
Transfer Learning for pre-trained Deep models
Extended models, including Fully Convolutional CNN, networks for image segmentation (U-net) and object detection (e.g., R-CNN, YOLO )

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
The course foresees a written exam and some homework assignments in the form of coding exercises/challenges. The written exam is aimed at verifying the acquisition of theoretical knowledge of the course subject, while the coding exercises aim at acquiring practical experience with the most widely used tools in the field.
The homework assignments contribute between 0 and 10 points to the final grade. The written exam is compulsory and will contribute between 0 and 22 points. The final grade is the sum of the grades for the homework assignments and the written exam.
Homework assignments need to be delivered during the course on fixed dates. The grade obtained on these assignments is valid for the entire academic year and can be combined with the written exam grade from any call in the same academic year.
Type of assessment
Description
Dublin descriptor
Written test
Solution of numerical problems
· Computation of simple backpropagation derivation
· Computation of simple sizing problems on CNN architectures
Answer to theoretical questions
· Formal derivation of learning techniques and models
· Comparison of models in terms of complexity, overfitting issues, and applicability in different cases of data analysis
1, 2
1, 2, 3, 4, 5
Assessment of practical homework
Execution of practical homework exercises
· Application of deep learning techniques to small dataset
· Application of model selection and validation techniques on simulated and real datasets
2, 3, 4, 5

Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, Editore: MIT Press, ISBN: 978-0262035613 http://www.deeplearningbook.org/

The course introduces Data Mining and overviews the most important algorithms used in this field. It consists of three main parts. The first part introduces the basics of data mining, including the representation of data, their exploration, and preparation. The second part delves into the four main areas, association rules, clustering, regression, and classification. Finally, we focus on advanced topics such as time series, anomaly detection, explainability, and AutoML.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn to:

• Structure a data mining pipeline
• The fundamental characteristics of the most important algorithms used in all the major steps of the pipeline
• Identify architectural styles and patterns
  Applying knowledge and understanding
  Given specific data mining process, students will be able to:
• Analize and comment on specific architectural choices
• Highlight possible criticalities
• Identify existing biases
• Apply the theory to assess the reliability of the results produced
  Making judgements
  Given a data mining task, students will be able to:
• Analyze and understand the goals, assumptions and requirements associated with that task
• Select the best environment to implement each step of the data mining process
• Select the best infrastructure
  Communication
  Students will learn to:
• Analyze the design choices that a data analytics solution entails
• Present and critically discuss the results of a data mining process
  Lifelong learning skills
  Students will learn
• how to tackle problems in real-world scenario and
• how to critically analyze a proposed solution and the result it produced.

Introduction to Data Mining
Data representation
Text representation and embeddings
Data exploration and visualization
Association rules
Clustering

• Hierarchical
• Representation-based
• Density-based
  Regression
  Classification
• Logistic regression
• Naive Bayes and Bayesian Belief Network
• k-nearest neighbor
• Decision trees
• Ensemble methods
  Advanced Topics
• Time series
• Anomaly detection
• Explainability
• Blackbox optimization
• AutoML

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The assessment is based on a written, closed-book test at the end of the course, taken in person, in class. It typically consists of questions on the course topics. It can include conceptual questions and exercises requiring a significant modeling effort. The test is worth a maximum of 30 points; three additional bonus points are awarded based on the time taken to submit the exam. 30 cum laude is assigned when the total score is 32 or above. Students have approximately 2 hours to answer the test. Students can take the test at any exam session during the year. The written test can be taken only in person. The discussion of the graded exams is also in person only. Instructors can summon students who passed the written test for an additional (in-person) oral exam covering the entire course program.
Type of assessment
Description
Dublin descriptor
Written test

• Solution of numerical problems
• Computation of score functions used in data mining algorithms- Execution of fundamental algorithms using small datasets
• Interpretation of code fragments to understand what they compute
  Exercises focusing on design aspects
• Evaluation of trade-offs between different proposed solutions
• Design of data mining pipelines for a given scenario
• Critical comparison of existing methods
  2,3,4,5

Mohammed J. Zaki and Wagner Meira, Jr.,, Data Mining and Analysis: Fundamental Concepts and Algorithms (Second Edition), Editore: Cambridge University Press, Anno edizione: 2020, ISBN: 978-1108473989
Note: Freely available at http://www.dataminingbook.info/
Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Anuj Karpatne, Introduction to Data Mining, Global Edition (2nd Edition), Editore: Pearson, Anno edizione: 2016, ISBN: 978-0273769224 https://www.pearson.com/store/p/introduction-to-data-mining-global-edition/P200000005534/9781292456133
Christoph Molnar, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, ISBN: 979-8411463330 https://christophm.github.io/interpretable-ml-book/
Note: Free browsable version available online.

The aim of the course is to introduce skills for the contextualization, definition, requirements, and implementation -through system integration, data analysis, and algorithms- of a cyber-physical system. These systems, which are able to interact continuously with the physical environment in which they operate, combine the computational capacity, communication, and control capacity. Cyber-physical systems are typically based on microcontrollers, dedicated sensors and actuators, and communication means (wired and/or wireless). A cyber-physical system is a special-purpose system and is intended to be part of more complex systems. The context is the field of Ambient Intelligence (AmI) envisages a digital environment where humans interact with multiple smart electronic devices that are aware of the context they operate and are capable of adapting their response to the users' requirements, anticipating their behavior and responding to their presence. Processing power is embedded and distributed in all the devices located in the environment, in what is denoted as "ubiquitous computing" or "pervasive computing". Different application areas are currently being pursued depending on the specific environment considered: smart homes, smart buildings, smart cities, smart transportation systems, etc. Particularly interesting are the applications of AmI in such areas as people's well-being (services and support, assistive technology,... ), mobility, commerce and marketing, enterprises and handicrafts, fashion, leisure and entertainment, tourism, healthcare, environmental management, arts and culture, agriculture.
The course also includes the practical use of boards for embedded systems and related development environments (e.g. Arduino and STM32 - Core / MBED).

• Knowledge and understanding (DD 1)
  Students will learn to deal with the design process of a computer system (for example, the collection definition and refinement of requirements, the analysis of previous works, the definition of the system, the identification of sensors and actuators based on the needs of the project, the data analysis and the definition of algorithms).
  Students will learn how to design and program a computer system, with particular emphasis on interacting with the external physical environment.
• Apply knowledge and understanding (DD 2)
  Students will be able to independently design a digital system;
  Students will be able to evaluate and select the elements of the digital system;
  Students will be able to write small embedded programs that interact with the physical environment;
• Making Judgments (DD3)
  Given a specific problem and design cases, students will have the ability to analyse functional and extra-functional characteristics and to independently compare different choices in terms of HW and SW.
• Communication Skills (DD4)
  Students will be able to communicate the idea behind the developed project, and to justify the design choices made, to an audience made up of specialists and non-specialists.
• Lifelong learning skills (DD5)
  Students will be able to understand how to develop a real embedded system project;
  Students will be able to independently learn how to use and program state-of-the-art embedded computer systems;
  Students will be able to independently learn the new micro-architectural features of computer systems.

Introduction
Ambient Intelligence: structure and classification; life cycle; design constraints, examples
Embedded System Design and Programming
Embedded Programming: the concept of embedded C; introduction to PYTHON; interacting with the environment: usage of digital and Analog I/O, PWM, timers and Interrupts; communication protocols; debouncing and calibration.
Microcontroller Boards: Arduino and STM32 boards and development environments; EPS32 board; introduction to Raspberry; extension shields for managing sensors, actuators and communication networks.
Real-world perspectives and sensing: designing digital systems for controlling real-world devices: from the idea to the prototype. Overview of sensors and signal conditioning.
Sessions are performed in presence according to the teaching schedule. The course is composed of a mix of lecture sessions and design support. Students directly learn, apply and test their acquired knowledge on the design problem to be solved. Learning modes consist of critique analysis of previous/existing works, lecture presentations, own design exercise (programming, integration of systems, verification and test of the prototype, data analysis,... ), own work presentations and, the demo of the prototype. Projects with innovative and applied aspects are encouraged.
Prototyping consists of proving/showing the validity of the proposed idea - minimum viable prototype (MVP). An MVP is an "object" with sufficient features to validate the idea early in the product development cycle. The main goal of an MVP is to receive user feedback as quickly as possible in order to iterate and improve the "product".
The working group has to be from 2 to 4 students.

Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
Students are required to produce (note: the percentages are indicative):
33 points: Final Paper 40% – Pitch 10 % – Video Spot 10% - Prototype 40%
Final Paper (paper - format latex – from 6 pages)
TITLE ( ~ 100 char - spaces included) & Authors
Abstract ( ~ 1000 chars spaces included)
INTRODUCTION ( ~ 3500 chars spaces included)
RELATED WORKS ( ~ 4500 equivalent chars spaces - text, tables and pictures -)
METHODOLOGY ( ~ 5500 equivalent chars spaces included - text, tables and pictures -)
EXPERIMENTAL RESULTS
CONCLUSIONS
REFERENCES
Pitch (10 minutes – final presentation)
Prototype
Video Spot (60 seconds
The final presentation and video will also enable students to demonstrate their communication and technical skills.
NOTE: All sections in the document are “mandatory”— any missing section will invalidate the entire document.
NOTE: 30cum Laude will be assigned when the total score is higher or equal to 31.

Margolis, M., Arduino Cookbook: Recipes to Begin, Expand, and Enhance Your Projects
Hendr, I., ESP32 Development using the Arduino IDE (English Edition)
Cameron, N., Electronics Projects With the ESP8266 and ESP32: Building Web Pages, Applications, and Wifi Enabled Devices
Schwartz, M., Internet of things with Arduino cookbook
Monk, S., Programming Arduino Getting Started with Sketches
Igoe, T., Making things talk. Third Edition
Gulliksson, H., Pervasive Design, fourth edition (English Edition)
Osterwalder A., Value Proposition Design: How to Create Products and Services Customers Want

This is a course devoted to mastering techniques for signal recovery from noise in modern data acquisition systems. In a blend of electronics, physics and signal processing we will cover amplifiers, noise, and signal conditioning techniques, keeping an eye on both the physical/theoretical working principles and the real characteristics. Students are expected to gain skills on the design of relatively simple data acquisition systems and on the selection criteria of their various components.

Expected learning outcome
Dublin descriptor
On completion of this course, students will:
· understand the principles and operation of operational amplifiers (OAs) and their limitations;
· have a sound knowledge of linear circuits made with OAs and of the techniques used to analyze the circuits and assess their performance;
· know the problems related to resistive sensors and their connection in a Wheatstone bridge configuration, either local or remote;
· understand noise in circuits and acquisition systems, its sources and properties;
· understand the principles of signal recovery from noise and know the different techniques.
1) knowledge and understanding
Specific examples and case studies will be provided to make students able to:
· design simple linear circuits employing OAs;
· design a proper sensor connection and a suitable amplifier accounting for non-idealities in the signal path;
· choose the correct signal recovery technique based on the signal and noise characteristics.
2) applying knowledge and understanding

Operation amplifiers (OAs): voltage and current amplifiers, input and output impedances. Negative feedback and application to amplifier design. Elementary linear stages and impedances. Real OAs: DC and AC parameters. Instrumentation amplifiers and CMRR. Frequency response of OA circuits, stability of the feedback loop and frequency compensation. Capacitive loading of OA stages.
Wheatstone bridge and connections: signal readout from resistive sensors: single-ended and differential signals, Wheatstone bridge and balancing, 2-, 3- and 4-wire connections. Temperature compensation.
Noise: signals and noise in time and frequency domains. Autocorrelation and power spectral density. Thermal noise in resistors, Nyquist derivation. Shot noise and Poisson random process model. Flicker noise. Noise in linear circuits and OAs, equivalent input noise. Noise factor and figure, signal-to-noise ratio (S/N). Feedback and noise.
Signal recovery: the case of white noise: low-pass and time-variant filters. Weighting functions. Gated integrators and improvement of S/N. Boxcar averagers. Discrete-time filters and their representation in time and frequency domains. Comparison between continuous- and discrete-time filters. Optimum filtering.
The case of low-frequency noise: high-pass filters and effect on pulsed signals. Baseline restorers. Amplitude modulation and synchronous detection. Lock-in amplifiers, analog and digital LIAs.

Prova scritta obbligatoria, senza prove in itinere
Students are required to pass a written, open-book exam, consisting of two problems with four questions each. Such parts refer to the main blocks of the course and are meant for the students to show
For the first part:

• An understanding of the operating principles of realistic circuits built using operational amplifiers;
• The capability to analyze such circuits and evaluate their performance;
• Some design capabilities, when modifications of the proposed scheme is asked in order to reach a specified goal;
• The capability to evaluate the circuits from the viewpoint of noise;
  and for the second one:
• An understanding of the basic principles of signal recovery;
• The capability to evaluate the signal and noise performance in a particular signal acquisition scheme;
• Some design capabilities, when they need to propose what signal recovery scheme is most suitable to solve a specific problems.
  Texts and solutions of previous examinations are available on the instructor’s website.

Lecture notes http://home.deib.polimi.it/spinelli/corsi/ele/ele.shtml
Sergio Franco, Design with operational amplifiers and analog integrated circuits, 4th Ed., Editore: Mc Graw Hill, Anno edizione: 2015, ISBN: 978-0-07-802816-8
http://home.deib.polimi.it/rech/Download/SignalRecoveryBook.pdf
A. S. Sedra, K. C. Smith,, "Microelectronic circuits", Editore: Oxford Univ. Press, Anno edizione: 2009, ISBN: 0195323033
T. H. Wilmshurst, "Signal recovery from noise in electronic instrumentation", Editore: Hilger, Anno edizione: 1990, ISBN: 0750300582

The goal of the course is to introduce the language of groups and their representations. After discussing basic notions of group theory, we will treat Lie groups, Lie algebras, and their representations, focusing on several examples of classical groups, following the elementary "matrix approach".

The student is expected to master the fundamental topics of group theory and their representations, in particular:
groups, subgroups, group actions
classical examples of finite groups and matrix groups
Lie groups and Lie algebras
exponential and logarithm for matrices
representations, operations with representations, irreducible representations
The student is expected not only to learn definitions and results, but also to be able to apply the knowoledge in a critical way to solve exercises and problems.
Explicit overview of relevant Dublin Descriptors for this course:
DD1- Knowledge and understanding. The lessons and exercise sessions aim to allow the student to develop a deep understanding of the fundamentals of group theory and representation theory, and to understand the connections among the different concepts treated in the course. The student will also learn to give a coherent exposition of the theory.
DD2- Applying knowledge and understanding. The lessons and exercise sessions aim to allow the student to develop problem solving skills, allowing them to solve exercises and problems related to the topics of the course.

General group theory. Definition of group. Abelian groups. Examples. Symmetric groups. Subgroups. Generators. Cyclic groups. Homomorphisms. Direct product. Cayley’s theorem.
Lie groups. Matrix Lie groups. The classical Lie groups. Lie group homomorphisms. Connected and compact groups. Analysis of some Lie groups of small dimension.
Matrix exponential. Matrix exponential. Matrix logarithm. Lie product formula. One parameter subgroups.
Lie algebras. The Lie algebra of a Lie group. Lie algebras of the classical Lie groups. The Lie group - Lie algebra correspondence. Geometric interpretation. The exponential map for connected groups.
Representation theory. Group actions. Representations of groups and algebras. Representations in the Lie group - Lie algebra correspondence. Irreducible representations. Complete reducibility. Representation theory of SL(2; C). Schur’s Lemma. Discussion of SO(2), SU(2), SO(3).

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
The final exam consists of a test in which the student is asked to answer questions in written form, and then discuss the answers orally with the instructor.

Brian Hall, Lie Groups, Lie Algebras, and Representations, Editore: Springer
Note: Freely available version on arXiv:
https://doi.org/10.48550/arXiv.math-ph/0005032
John Stillwell, Naive Lie Theory, Editore: Springer, ISBN: 978-0-387-78214-0
Note: Free online access provided by Politecnico di Milano
Peter Woit, Quantum Theory, Groups and Representations, Editore: Springer
Note: Freely available version on author's personal webapge:
https://www.math.columbia.edu/woit/QM/qmbook.pdf

The Aim of the course is threefold:
(i) to introduce the student to the theoretical foundations and main applications of integrated optics;
(ii) to provide the student with a set of advanced tools and methods for the design and quantitative description of photonic components and devices;
(iii) to give the student the theoretical basis for the understanding of highly innovative topics in modern photonics.

Knowledge and understanding (DD1)
The student understands the physical basis of guided wave optics and of the optical phenomena taking place at the microscale.
The student knows the guidelines for the modelling and design of integrated photonic devices.
The student is aware of the key functionalities implemented in i ntegrated photonics.
Apply knowledge and understanding (DD2)
The student is capable to retrieve the analytical solution of standard problems in integrated and telecom optics and guided-wave photonics
The st udent is capable to draft a quantitative design of integrated photonic devices (couplers, filters, multiplexers and demultiplexers) that fulfil given target specifications.
The student has basic experimental training to couple light into an optical fibre and to measure fibre dispersion.
Making judgements (DD3)
The student has the ability to understand the operating principles of integrated photonic devices and to discuss the validity limits of the models used for their description.
The student is aware of the key functionalities implemented in integrated photonics.
Given a specific optical functionality readily implemented in the market or under development, the student is able to identify the operating principles and evaluate the potential impact of integrated photonics technologies in the field.
The student is capable to perform comparative analysis of different approaches in Integrated Photonics to the achievement of a given optical functionality.
Lifelong learning skills (DD5)
The student knows the theoretical foundations and masters a set of analytical and numerical tools in advanced photonics, for the quantitative description and design of novel optical components in a variety of configurations.
The student is capable of understanding new trends in the high-tech market dealing with advanced photonic structures.
The student is capable of autonomously learning the operating principles of novel photonic devices based on guided wave optics configurations.

Introduction to guided wave optics. The symmetric dielectric slab waveguide. Analytical derivation of the propagation constants and electromagnetic field distributions of guided modes. The characteristic mode equation: physical interpretation and graphical solution. Orthogonality and completeness properties of guided and radiation modes. Extension of the analysis to channel optical waveguides.
Introduction to optical communication systems. Historical evolution. Introductory concepts: analog and digital transmission, multiplexing techniques, modulation formats. Layout of an optical communication system: topology, design criteria, integrated optical devices. Analysis of the bit-error-rate as a function of the main noise system sources. Communication systems limited by attenuation and dispersion.
Integrated optical devices: couplers and power splitters. Coupled mode theory. Transfer matrix of the directional coupler. Functional analysis of the directional coupler as a 3-dB coupler, as a frequency filter and as an optical switch. Other types of couplers and beam splitters: the bifurcation, the multimode interference coupler, the star coupler.
Optical filters in integrated optics. FIR (Finite Impulse Response) and IIR (Infinite Impulse Response) filters. General spectral properties of filters. Mach-Zehnder interferometer: spectral response and engineering guidelines. The Arrayed Waveguide Grating (AWG): operation principle, transfer function, engineering guidelines. Functional analysis of the AWG as a de/multiplexer, as a router and as a reconfigurable add-drop filter. Ring resonant filter: transfer function and main features.
Electro-optic intensity modulators. The electro-optic effect: basic principles. X-cut and Z-cut lithium niobate modulators. Lumped and travelling electrodes. Bandwidth limitations. Integrated optical intensity modulators: architectures (Mach-Zehnder interferometer, directional coupler, deltabeta-reversed directional coupler) and performance. Comparison with bulk modulators.
Recent advances in the field of optical communications. DPSK modulation formats: signal to noise ratio and architecture of transmitters and receivers. Coherent detection and digital signal processing for multi-level transmission formats and dispersion compensation.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
Written examination, optionally followed by an oral examination.
The written exam consists of 2 open questions to be addressed in 1 hour and 30 minutes. At the discretion of the student, the written text is followed by an oral test. The aim is to ascertain:
the understanding of the physical basis of guided optical phenomena (DD1);
the capability to illustrate in a logical, clear and exhaustive fashion the behaviour of the studied phenomena (DD1);
the knowledge of the definitions and general concepts dealing with optical waveguides and integrated optics telecom components (DD1 and DD5);
the capability to design and model a photonic device that fulfils given specifications by quantitative assessment of its main geometrical and optical parameters (DD2);
the capability to discuss, both qualitatively and quantitatively, the performance of prototypal integrated photonics devices as a function of their key parameters (DD3 and DD5).

Notes of the course https://webeep.polimi.it/course/view.php?id=14006
G. P. Agrawal, Fiber-Optic Communication Systems, Editore: John Wiley & Sons, (IV Ed.), Anno edizione: 2010, ISBN: 987-0-470-50511-3

The main objective of the course is to provide the students with the necessary tools for the design and assessment of machine components, both under static and fatigue loading. The basics of mechanical design are illustrated in order to include capability to develop simplified structural models, basic theory of elasticity, design methods for static and fatigue loads (multiaxial and variable amplitude loading) and tools for assessing the influence of notches on static and fatigue strength.

Once the students has passed the exam, the students will:
possess the knowledge of the main tools (analytic approach) and methods for modelling structural behaviour of simple machine components/systems; (DdD1)
understand assumptions and limitations of the beforementioned tools and methods; (DdD1)
perform design and assessment of machine components/systems subjected to static and fatigue loading; (DdD2)
analyse and critically discuss the results. (DdD2)
Thanks to the practices that are focussed on case studies (basic optimization technique, bolted and welding joints, basic of gears, pressure vessels, power transmission shafts, static design of a prothesis) the students will improve the skills necessary for practical design applications. Moreover, the theoretical concepts and the case studies will provide together a more general capability to approach an engineering solution of simple problems related to structural design in several fields. These competences are therefore of benefit for a wide range of study courses.

The course is organized in lectures and tutorials. Theoretical concepts will be applied during application in order to show an engineering approach to the design and assessment of actual and relevant components.
Internal forces: definition of slender members, definition of internal forces and equations of equilibrium, frames;
Axial and bending problem in beams: hypotheses and basic assumptions for the Euler-Bernoulli beam; state of stress due to normal force and bending moment;
Shear in beams: relationship between bending moment and shear force; shear stress distribution in case of solid section; torsion in beam: distribution of torsional stresses for a circular solid beam;
State of stress: definition of equilibrium for deformable bodies; Cauchy tetrahedron; principal stresses and directions; stress invariants; hydrostatic stress tensor and stress deviator tensor; Mohr’s circles for plane stress; three-dimensional description of Mohr’s circles;
Material behaviour: tensile test devices and specimens; engineering stress-strain curve; true stress-strain curve; ductile and brittle failure mechanisms; ideal material models;
Influence of notch for ductile and brittle materials: phenomenological aspects; definition of Kt and Ks parameters.
Stress-strain relations: general expression of the elastic tensor; elastic tensor for isotropic materials; derivation of the generalised Hooke’s law; stress-strain relationships for plane stress and plane strain.
Failure criteria for isotropic materials: Galileo-Leibniz-Rankine-Navier criterion for brittle materials; Guest-Saint Venant-Tresca criterion for ductile materials; Huber-Hencky-von Mises criterion for ductile materials; general considerations about the definition of the safety factor;
Fatigue: fatigue phenomena; fatigue testing; definition of fatigue cycles; Wöhler diagram; finite and infinite life regime; relationship between fatigue limit and tensile strength; influence of mean stress with Haigh diagrams; notch effect on fatigue; influence of surface finishing; size effect and influence of stress gradient; damage accumulation with Miner-Palmgren rule; Gough-Pollard and Sines criteria for multi-axial loading condition.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
The exam is based on a written test followed by a voluntary oral one. Written exam is compulsory and cover all the theoretical and applicative topics of the course. The written test will include the solution of numerical exercises, aimed at assessing the capability of application of the design tools to practical cases and questions aimed at assessing the students’ capability of application of the design tools to simple practical cases regarding machine components and systems and/or to test students’ knowledge and understanding of the theoretical topics included in the program. Oral exam is voluntary and possible only in case written exam has been positively passed; oral exam can modify the score of +- 3 points and have to be carried out in the same exam session of the written one. The oral test will prove also the capability of critically discuss the design tools and their limits and or applicability conditions. Practical exercises can be also requested during oral exam.

Lecture notes by the teacher (english) Beep
N. E. Dowling, Mechanical Behavior of Materials, Global Edition 5th Edition, Editore: Person, Anno edizione: 2019
AA. VV, Costruzione di Macchine, Editore: McGraw Hill
Note: (italian language)

1) Cinematica: richiami di cinematica, cinematica del corpo rigido; vincoli, coordinate libere e gradi di libertà; sistemi olonomi.
2) Statica e dinamica: quantità dinamiche per sistemi di punti materiali e per il corpo rigido; leggi della dinamica; statica dei sistemi olonomi; conservazione dell'energia meccanica; stabilità à la Ljapunov.
3) Meccanica Lagrangiana e Hamiltoniana: equazioni di Lagrange; coordinate cicliche, integrali primi del moto e metodo di Routh simmetrie e teorema di Noether; campi centrali; piccole oscillazioni; principi variazionali; Hamiltoniana e equazioni di Hamilton; struttura simplettica e teorema di Liouville; parentesi di Poisson.

The Aim of the course is threefold: (i) to introduce the student to the theoretical foundations and main applications of Photonic Crystals, Plasmonic Nanostructures and Metamaterials; (ii) to provide the student with a set of advanced tools and methods for the design of components and devices based on photonic metastructures; (iii) to give the student the theoretical basis for the understanding of highly innovative topics in modern Photonics.

• Knowledge and understanding (DD1)
  The student understands the physical basis of the optical phenomena taking place at the nanoscale.
  The student knows the guidelines for the modeling and design of metaoptics devices.
  The student is aware of the key functionalities implemented in metaphotonics.
• Apply knowledge and understanding (DD2)
  The student is able to retrieve the analytical solution of standard problems in dielectric periodic media, metal based (plasmonic) nanostructures, metamaterials and metasurfaces.
  The student is able to perform numerical analysis of prototypal metastructures, by resorting to a commercial tool for full-wave electromagnetic simulations.
• Making Judgements (DD3)
  The student is aware of the key functionalities and challenges in photonics at the nanoscale and in metamaterial optical structures.
  Given a specific optical functionality readily implemented in the market or under development, the student is able to identify the operating principles and evaluate the potential impact of metaphotonic technologies in the field.
• Lifelong learning skills (DD5)
  The student knows the theoretical foundations and the main applications of metaphotonics.
  The student is capable of understanding new trends in the high-tech market (from IT highways, to lab-on-chip and nanomedicine) dealing with advanced photonic structures.
  The student is capable of autonomously learn the operating principles of novel optical devices based on photonic nanomaterials.

1. Theoretical Foundations. Electromagnetism as an eigenvalue problem: electromagnetic harmonic modes, symmetries and classification of harmonic modes. Scaling properties of Maxwell's equations. Wave propagation in homogeneous and inhomogeneous media: angular spectrum representation, TE-TM decomposition. Evanescent waves and the diffraction limit.
2. Photonic Crystals. Generalities on periodic lattices. Bloch electromagnetic theorem. Photonic band structure. One-dimensional PCs: periodic layered media, band states and gap states, surface states and bulk defect states. Two-dimensional PCs: a polarization-indepenendent band-gap, point and line defects and application to PC cavities and waveguides. PC Interfaces: negative refraction and superprism effect.
3. Plasmonics. Optical properties of noble metals. Surface Plasmon Polaritons (SPPs): derivation of the SPPs dispersion equation, optical properties of SPPs, excitation and detection of SPPs, plasmon-polaritons in thin metallic films (SR/LR-SPPs). Introduction to plasmonic waveguides. Localized plasmons (LPs): quasistatic theory of localized plasmonic resonances in noble metal nanospheres, optical properties of metallic nanoparticles (resonant polarizability, absorption and scattering cross-sections, field-enhancement, resonance tuning). Introduction to plasmonic nanosensing (SPR and SERS).
4. Metamaterials and Metasurfaces. Negative electric permittivity media. Artificial magnetism and materials with negative magnetic permeability (stack of metal cylinders, split ring resonators). Negative refractive index materials (NRM) and the effects of a reversed wave-vector. Surface electromagnetic modes in NRMs and Pendry's Perfect Lens. Optical metasurfaces and generalized laws of reflection and refraction. Gradient metasurfaces.
Computational Laboratory. During the course, the student will be introduced to full-wave electromagnetic simulations with a commercial tool (Comsol Multiphysics). This training will enable the student to perform numerical analysis of key phenomena in Metaphotonics and to model 2D photonic crystals, plasmonic nanoantennas and simple metasurfaces.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
The exam is written, and consists of open questions (typically 2 questions to be solved in 1 hour and 30-45 minutes), aimed at ascertaining:
the understanding of the physical basis of the electromagnetic phenomena at the nanoscale (DD1);
the knowledge of the definitions, theorems and general concepts dealing with nanostructured photonic media, including photonic crystals, plasmonic structures, metamaterials and metasurfaces, and the ability to model such systems (DD2);
the capability to discuss, both qualitatively and quantitatively, the performance of photonic metastructures as a function of their key parameters (DD3 and DD5).
The written exam may be followed by an oral exam upon request by the student, provided they have passed the wrtitten exam.
The teacher reserves the right to summon the student for a supplementary oral exam should it be necessary to verify the work done in the written exam.

Notes and handouts edited by the teacher https://webeep.polimi.it
L. Novotny, B. Hecht, Principles of Nano-Optics, Editore: Cambridge University Press, (II Ed.), Anno edizione: 2012, ISBN: 978-0-511-81353-5
J. D. Joannopoulos, S. G. Johnson, J. N. Winn, R. D. Meade, Photonic Crystals - Molding the Flow of Light, Editore: Princeton University Press, (II Ed.), Anno edizione: 2008, ISBN: 978-0-691-12456-8
W. Cai, V. Shalaev, Optical Metamaterials: Fundamentals and Applications, Editore: Springer, Anno edizione: 2010, ISBN: 978-1-4419-1151-3

Course goals
The goal of the course is to provide students with the theoretical knowledge of basic numerical techniques for the approximation of solutions of ordinary and partial differential equations, as well as with the ability to apply this knowledge to relevant model problems. The course covers the basic finite difference techniques for the approximation of ordinary and partial differential equations. All topics are presented at a theoretical level and the corresponding methods are applied to the solution of numerical problems in computer laboratory sessions based on the use of Python scientific software.
Course organization
The course will be organized in theoretical lectures, in which basic numerical techniques for the approximation of solutions of ordinary and partial differential equations will be presented, and exercise/laboratory sessions, in which these numerical techniques will be applied to the solution of numerical problems in computer laboratory sessions based on the use of Python scientific software.

Knowledge and understanding (DD1): The students will acquire detailed knowledge and understanding of the basic numerical techniques for the approximation of solutions of ordinary and partial differential equations. The students will acquire the capability of understanding mathematical proofs presented in applied mathematics textbooks.
Apply knowledge and understanding (DD2): The students will be able to apply this knowledge to the solution of relevant numerical probems in ordinary and partial differential equations, making use of the resources of the Python scientific software.
Making judgements (DD3): The students will be able to make judgements on the most appropriate numerical method to be applied for the solution of different classes of problems in terms of accuracy and efficiency.
Communications skills (DD4): The students will be able to present the results of the numerical analysis of a differential equation problem with appropriate technical language.

1) Introduction to basic concepts of numerical analysis and scientific computing.
2) Floating point representation of real numbers: machine accuracy, cancellation of significant digits.
3) Finite difference approximation of first derivatives. Approximation of second and higher order derivatives.
4) Polynomial interpolation: Existence and unicity of the interpolating polynomial. Bounds on the approximation error.
5) Basic numerical integration methods: Midpoint and trapezoidal rules. Composite integration rules. Error estimates for simple and composite rules. Numerical computation of Fourier series coefficients (FFT).
6) Numerical methods for ordinary differential equations: Overview of basic existence and uniqueness theorems. Examples of simple numerical methods: forward Euler, Heun, second order Runge Kutta, higher order Runge-Kutta. Convergence of one step methods. A-stability of numerical methods. Extension to ODE systems.
7) Basic concepts on Fourier series Application of Fourier series to the numerical solution of linear PDEs by separation of variables.
8)The linear, 1d advection-diffusion equation: Existence and uniqueness of solutions, representation formulae. Boundary conditions.
9) Finite difference methods for the linear, 1d advection diffusion equation: Examples of basic methods. Consistency, convergence and stability. Analysis of truncation error: numerical diffusion.
10) The wave equation and hyperbolic systems: representation formulae and basic finite difference methods.

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
A set of scientific papers will be proposed at the end of the course, describing or employing numerical methods that the students are required to study and implement. The papers can be studied by groups of at most three students taking the exam in the same exam session. Computer programmes implementing the solution and a report describing the validation procedure will have to be uploaded in an exam folder two weeks before the exam session. The exam will consist in an oral discussion in which each of the project proponents will be required individually to explain the concept presented in the chosen paper, to describe in detail the applied methods and the quality of the solutions obtained and to justify the results obtained on the basis of the theory presented during the course.

Course lecture notes

The goal of the course is to provide advanced skills in machine learning and optimization for challenging real-world data-driven applications. More precisely, the course explores mathematical models, optimization algorithms, and machine learning tools. The course focuses on four real-world applications in which computational data science is crucial: pricing in e-commerce, digital advertising, matching, and non-stationary environments. For every real-world application, the goals of the course are: providing a description of the real-world scenario, providing mathematical models of the scenario, providing optimization algorithms for the scenario, providing machine learning tools to deal with uncertainty. The treatment of the topics will be both from a theoretical point of view, describing models/algorithms and their properties, such as approximation guarantees and computational efficiency, and from a practical point of view, applying the algorithm during some laboratory sessions.

DdD1. Knowledge and Understanding

• Analyze real-world applications through mathematical models derived from microeconomics.
• Examine and interpret the properties and assumptions of such models.
• Evaluate the performance of algorithms and compare them with theoretical lower bounds.
  DdD2. Applying Knowledge and Understanding
• Model real-world applications as learning problems.
• Identify the most suitable class of algorithms to solve a given problem.
• Apply existing computational tools to implement and test practical solutions.
  DdD3. Making Judgments
• Assess the performance and robustness of proposed solutions.
• Make informed decisions among alternative methods offering different theoretical or empirical guarantees.
  DdD4. Communication
• Present and discuss problem formulations, modeling choices, and obtained results using appropriate technical language.
  DdD5. Lifelong Learning Skills
• Design algorithms to address problems for which no existing methods are available.
• Analyze and critically discuss the theoretical properties of newly developed algorithms.

0. Introduction to online learning
0.1. Online learning setting
0.2. Learning with full feedback
0.2.1 Online gradient descent
0.2.2. Adversarial expert problem
0.3. Learning with bandit feedback
0.3.1. Stochastic multi-armed bandits
0.3.2. Adversarial multi-armed bandits
1. Pricing in e-commerce
1.1. Introduction to pricing
1.2. Learning in pricing
1.2.1. Learning with discrete prices
1.2.2. Learning the demand curve
1.3. Implementing learning algorithms for pricing
2. Matching and combinatorial bandits
2.1. Introduction to matching
2.2. Matching algorithms
2.3. Combinatorial bandits
2.3.1. Combinatorial bandits with UCB
2.3.2. Combinatorial bandits with TS
2.4. Implementing learning algorithms for combinatorial bandits
3. Advertizing and learning with budget
3.1. Introduction to advertizing
3.2. Online learning with budget constraints
3.2.1. UCB for online learning with budget constraints
3.2.2. Primal-dual methods for online learning with budget constraints
3.3 Implementing learning algorithms for online problems with budget
4. Contextual Bandits
4.1. Introduction to contextual bandits
4.2 Online learning in contextual bandits
4.2.1 Learning with a small number of contexts
4.2.2 Learning in linear contextual bandits
4.3 Implementing learning algorithms for contextual bandits
5. Non-stationary enviroments
5.1. Introduction to non-stationary environments
5.2. Sliding window
5.3. Change detection
5.4. Implementing learning algorithms for non-stationary environments

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The exam is structured as follows:

• Written exam (2 hours --- max 16 points). The exam is composed of 4 questions relative to all the topics. Here, the students are required to answer some theoretical questions about topics discussed during the course. The capabilities the students must show are:
• Understanding the theoretical properties of the algorithms (DdD1).
• Modeling real-world applications by means of mathematical models (DdD2).
• Identifying the most suitable class of algorithms to solve a problem, using the already available tools to solve a problem in practice (DdD2, DdD3).
• Project work (max 16 points). The project work can be carried out by groups of students, where each group is composed of at most 4 students. The students can exploit the lectures delivered by the teaching assistant to organize the project work. However, attending the lectures delivered by the teaching assistant is not mandatory. Here, the students are required to solve problems that are beyond the topics discussed during the course. The capabilities the students must show are:
• Design an algorithm for a problem not discussed during the course (DdD3, DdD5).
• Present orally the proposed algorithms and discuss their properties (DdD3, DdD4, DdD5).

Aleksandrs Slivkins, Introduction to Multi-Armed Bandits, Editore: Now Publishers, Anno edizione: 2019
Elad Hazan, Introduction to Online Convex Optimization, Editore: MIT Press, Anno edizione: 2022

Objectives
The goal of the course is to provide students with important mathematical tools widely used in other courses in mathematical analysis and in mathematical physics. The course also provides the rigorous mathematical foundations necessary for the critical use of the tools. We will also explore applications of spectral theory to quantum mechanics. Specifically, we will apply spectral theory to various quantum systems, such as the quantum harmonic oscillator and the hydrogen atom.

Expected Learning Outcomes
At the end of the course and after passing the exam, students will be familiar with the concepts of operator theory.
More specifically:

• Knowledge and understanding – DD1
  Students will acquire the knowledge and understanding of the main definitions, of the main concepts in operator theory introduced in the course, the reasons they were introduced as well as some applications of the theory mathematical physics.
• Applying knowledge and understanding – DD2, DD3
  Students will acquire knowledge and understanding of the statements of the theorems and their proofs; they will have the ability to produce examples and counterexamples that illustrate the validity or invalidity of implications between certain properties in operator theory. Additionally, students will be able to solve exercises on the topics of the course.
• Communication skills – DD4
  Students will be proficient in the correct and precise use of mathematical language and symbolism in operator theory.

Programme
Part (I) Linear Operators in Hilbert Spaces
Hilbert spaces, basis and orthonormal complete sets;
bounded operators, the holomorphic functional calculus for bounded operators,
Fredholm operators, closed operators, invertible operators, adjoint;
symmetric and self-adjoint operators;
isometries and unitary operators, projectors;
finite-rank, compact, Hilbert-Schmidt and trace-class operators;
spectrum: pure point, continuous and residual spectrum;
resolvent: first and second resolvent identities and Weyl theorem.
Part (II) Self-adjoint Operators and Spectral Theorem
Criterion of self-adjointness;
perturbation theory and Kato-smallness (Kato-Rellich theorem);
quadratic forms and self-adjointness (KLMN theorem);
deficiency spaces and Von Neumann theory of self-adjoint extensions;
spectral theorem for bounded and unbounded self-adjoint operators (multiplication operator version);
spectral theorem (functional calculus);
spectral theorem (spectral resolution and spectral measure);
discrete and essential spectrum and Weyl criterion;
Stone theorem.
Part (III) This part is devoted to modern aspects in operator theory, according to the students' interests. This portion of the program is intended to be informative and will not be among the topics required for the exam.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Assessment Methods
The exam consists of a written test, which includes both exercises on the program topics and theoretical questions on definitions, theorem statements and their proofs, presentation of examples and counterexamples on the program topics.
When answering questions on the exam program, the student must demonstrate knowledge and understanding of the definitions and of the concepts introduced in the course (Dublin Descriptor 1) and the reasons they were introduced; knowledge and understanding of the statements of the theorems and their proofs; the ability to produce examples and counterexamples that illustrate the validity or invalidity of implications between certain properties (Dublin Descriptor 2 and 3); proficiency in the correct and precise use of mathematical language and symbolism (Dublin Descriptor 4).
Additionally, the student must demonstrate the ability to solve exercises on the course topics.
The exam dates are set according to the School’s regulations, and there are no midterm exams.

K. Schmüdgen, Unbounded Self-adjoint Operators on Hilbert Space, Editore: Springer, Anno edizione: 2012
M. Reed, B. Simon, Methods of Modern Mathematical Physics, Editore: Academic Press, Anno edizione: 1981
E.H. Lieb, M. Loss, Analysis, Editore: AMS, Anno edizione: 2001

The course aims to provide students with the fundamentals for understanding the propagation of incoherent radiation fields and the measurement of their radiometric quantities using appropriate detectors. The physical principles of light-matter interaction and their applications in the most important ligth detectots will also be covered.
At the end of the course, students should be able to design a simple system that collects light from a primary source (e.g. an LED) or a secondary source (e.g. a fluorescent material) and measure its radiometric properties.
It is assumed that students take in the same semester the course "Semiconductor Photonics", which teaches the principles of radiation emission by semiconductor based devices.

• Knowledge and understanding – DD1
  Students will learn and understand the radiometric properties of radiation fields and the physical processes at the heart of photonics detectors.
• Applying knowledge and understanding – DD2
  Students will be able to transfer the physical concepts to applications requiring the propagation and detection of optical radiation.
• Making judgements – DD3
  Students will gain the capability of modeling photonics devices and their behaviour in engineering systems.
• Learning skills – DD5
  Students will develop the expertise to design photonics based applications and systems exploiting the knowledge acquired in the course.

1. Introduction to Radiometry
Definition of radiometric quantities
Theorem of conservation of radiance
Lambertian sources
Radiance and irradiance in optical images
Basic principles of photometry
2. Radiation detectors
Principles of radiation detection
Main types of detectors and their figures of merit
Noise in detectors
Thermal detectors: general description; pyroelectric detectors
Quantum detectors: general description; photoemissive detectors; junction detectors
Principles of coherent detection (homodyne)
Charged-Coupled Devices (CCD) and CMOS
3. Introduction to quantum optics
Quantization of the electromagnetic field in a cavity
Single mode quantum optics: the Number states and the Coherent states
Vacuum fluctuations and zero-point energy

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
The final assessment will be based on a written examination comprising two questions: one relating to the theory covered in the course; the other a numerical exercise designed to assess the ability to solve simplified practical problems. Past examination texts with detailed solutions are available on the course website. An oral examination may be requested, either by the students or by the teacher (for justified reasons), to complete the assessment process: this option is reserved exclusively for students who have achieved a mark of 18/30 or higher in the written examination.

This teaching program aims to equip students with the knowledge of production planning and control principles, methods, and techniques in the context of nano and micro production systems, covering both the fundamentals and the latest trends.
Upon completion of the program, students are expected to develop a comprehensive understanding of the core concepts and theories of production planning and control processes, to acquire capabilities in fluently applying basic and advanced methods to manage and optimize the planning and control processes in nano and micro production systems, and to effectively communicate of the planning decisions.

By the end of the course, students will have mastered the content and practices as set out in the teaching objectives.
In particular, students will be able to:

• understand the basics and principles of production planning and control in nano and micro production systems (ILO1)
• identify and analyze the problems associated with production planning and control in the context of nano and micro production systems (ILO2.1)
• apply appropriate production planning and control method and technique to the focal production planning and control problem (ILO2.2)
• design solutions and take decisions for the focal production planning and control problem (ILO2.3)
• communicate concepts, strategies, and solutions effectively in the context of production planning and control in nano and micro production systems (ILO3)

Teaching activities are carried out by means of frontal lectures, exercise sessions and other formats, with interventions from the industry.
The topics covered in the course are divided into the following modules:
Module 1: Introduction to Production Systems

• Introduction to Production Systems: Definition and terminologies
  Module 2: Basics of Production Systems (10%)
• Classification of production systems, typologies and characteristics; Classification of assembly systems, typologies and characteristics
• Basics of Nano and Micro Production Systems: Overview of nano and micro production systems, manufacturing technologies, and processes
  Module 3: Basics of Production Planning and Control (10%)
• Key concepts and principles of production planning and control;
• Relevant costs for production planning and control cycle
  Module 4: The Production Planning and Control Cycle for Nano and Mirco Production Systems (60%)
• Demand planning and management; forecasting methods and techniques
• Long/mid-term planning: aggregate planning, capacity planning and resource allocation; master production schedule
• Material management and Inventory management: material requirements planning; Inventory management methods and techniques
• Short-term planning: production scheduling and sequencing, optimization algorithms
  Module 5: Basics of Performance Measurement (10%)
• Performance measurement concepts and terminologies, operating conditions and internal/external performance, performance measurement scorecards
• Performance measurement basics for productive factor, warehouse and services
  Module 6: Advanced Topics and Trends in Nano and Micro Production Systems (10%)
  Note: The syllabus may subject to change at the discretion of the instructor – any changes will be timely communicated to the students. Please refer to the course WeBeep channel.

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The assessment of learning outcome will take the form of a combination of group assignment and oral test.

• Oral test, covering all contents introduced and discussed in the lectures, designed to evaluate ability of the students in understanding (ILO 1) and applying (ILO2.2) appropriate principles, method and technique in the area of production planning and control for nano and micro systems and aims to evaluate the ability of the students in communicating and discussing concepts, strategies, and solutions in the context of production planning and control in nano and micro production systems (ILO3).
• Group assignment, developed on specific themes addressed in the lectures, is set to assess the ability of the students in identifying and analyzing the problems associated with production planning and control (ILO2.1) and consequently in drawing up appropriate solutions and decisions based on informed analysis (ILO2.3). Together with the oral test, it also evaluates ability of the students in presenting and justifying strategies and solutions of production planning and control in the context of nano and micro production systems (ILO3).
  Note: The assessment methods may subject to minor changes at the discretion of the instructor. Please refer to the information presented at the beginning of the course.

All lecture materials will be made available on WeBeep WeBeep Channel
Andrea Sianesi, Production systems management: Planning, scheduling, control, measurement and improvement, Editore: Mondadori, Anno edizione: 2021, ISBN: 9788861848924
Toly Chen, Production Planning and Control in Semiconductor Manufacturing: Big Data Analytics and Industry 4.0 Applications, Editore: Springer, Anno edizione: 2022, ISBN: 9783031140655
F. Robert Jacobs, William Lee Berry, D. Clay Whybark, Thomas Vollmann, Manufacturing Planning and Control for Supply Chain Management, Editore: McGraw-Hill Education, Anno edizione: 2011, ISBN: 9780071750318

The aim of the course is to introduce the study of fundamental structural, morphologic and electronic properties of surfaces and interfaces of crystalline solids. The latter represent examples of low-dimensional systems, where the symmetry reduction may induce novel and exotic properties with respect to those that can be observed inside the bulk of a solid, and that can be understood only with the help of Quantum Physics.
Module I describes the main principles generally used in the analysis of low-dimensional systems. Elements of crystallography, surface thermodynamic potentials, elements of tight-binding and density functional theory, examples of confined systems in metal and semiconductor systems, hetero-junctions, hybrid structures, quantum wires and quantum dots, etc. represent some of the main topics discussed in this module.
Module II will be focusing on the experimental approaches that are needed to study the physical mechanisms and quantum effects acting at the low-dimensional scales. Some of the most widely employed methods will be presented in details.

-Knowledge and understanding (DD1)
The student will be able to implement concepts and theories learned in fundamental Physics courses (classical physics, quantum mechanics and solid state physics) to derive and discuss theories describing the physical properties and quantum phenomena of low-dimensional systems.
-Applying knowledge and understanding (DD2)
The student will be able to learn the working principles of state-of-the-art instrumentation suitable for the study of low-dimensional systems.
The student will be able to analyze and discuss the results of experimental measurements related to the discussed techniques.
-Making judgements (DD3)
The student will be able to identify the surface sensitive technique best suited to investigate the structural, morphologic and electronic properties of low-dimensional solid systems.
-Learning skills (DD5)
The student will be able to access the state-of-the-art scientific literature of the reference topics, in terms of understanding the employed experimental methodologies and the related scientific results.
The student will be able to analyze and discuss the results of experimental measurements related to the discussed techniques.

Real and ideal surfaces
Surface vs bulk.
2D crystallography
Advanced surface crystallography
Wood construction
Brillouin zones
Surface thermodynamics
Surface thermodynamic potentials
Equilibrium shapes of crystals
Surface energy and morphology
Surface bands
tight-binding.
Dangling bonds
Energy at surface
Bond braking and charge transfer
Metallic bonds
Confined systems
p - and Seiwatz chains
Dimers bridging groups
Adatoms and adclusters
Two particle elementary excitations.
Optical spectroscopy
Polarization function: excitons
Phonons
Excitations in reduced dimension
Defects.
Defects on surface
Vacances and impurities
Line defects: steps
Planar defects, stacking faults

Prova orale obbligatoria
Oral evaluation, aimed to probe the student preparation with respect to the objectives outlined above.

H. Lüth, Solid surfaces, Interfaces and Thin Films, ISBN: 978-3-319-10756-1
N. W. Ashcroft, N. D. Mermin, Solid State Physics, ISBN: 978-0030839931
S. Hüfner, Photoelectron spectroscopy: principles and applications, ISBN: 978-3-662-09280-4
H. Ibach, Physics of Surfaces and Interfaces, ISBN: 978-3-540-34710-1
J. C. Vickerman, I. S. Gilmore (editors), Surface analysis: the principal techniques, ISBN: 978-0-470-01764-7
K. Wandelt (ed.), Surface and Interface Science, Volume 1 - Concepts and Methods, Editore: Wiley, Anno edizione: 2012, ISBN: 978-3-527-41156-6
Moore, John H.; Davis, Christopher C.; Coplan, Michael A., Building Scientific Apparatus, Editore: Cambridge University Press, Anno edizione: 2009, ISBN: 9780521878586

Il corso fornisce una visione completa della tecnologia MEMS, dai principi fondamentali alle applicazioni pratiche, introducendo gli studenti a future prospettive di carriera nel settore della microelettronica. Accelerometri, giroscopi e sensori di pressione fungeranno da casi di studio. Gli argomenti trattati riguardano: le principali tecniche di fabbricazione nell'ambito dei semiconduttori e dei MEMS in particolare (fotolitografia, deposizione, etching, HF vapor release, wafer bonding), i principi di base di progettazione e simulazione, l'integrazione con l'elettronica di controllo e il "packaging", le modalità di test per garantire le prestazioni e l'affidabilità di un dispositivo. Il programma del corso esplora lo sviluppo di un prodotto MEMS dalla prima fase di "ricerca e sviluppo" fino alla produzione e alla qualifica finale. Teoria e pratica si mescolano: esercitazioni di simulazione al computer e attività sperimentali nei laboratori Polifab integrano e supportano le lezioni frontali. Due visite agli impianti industriali di STMicroelectronics offrono agli studenti la possibilità di vedere dal vivo i concetti e le teorie apprese in classe.

The course aims to introduce and explain important physical processes at the heart of the optical properties of semiconductor devices, such as light emitting diodes (LEDs) and semiconductor lasers.

• Knowledge and understanding – DD1
  Students will learn and understand the physical processes at the heart of photonics devices and the technological characteristics of semiconductor lasers.
• Applying knowledge and understanding – DD2
  Students will be able to transfer the physical concepts to applications requiring the generation and detection of optical radiation
• Making judgements – DD3
  Students will gain the capability of modeling photonics devices and their behaviour in engineering systems.
• Learning skills – DD5
  Students will develop the expertise to design photonics based applications and systems exploiting the knowledge acquired in the course.

1. Electrons in semiconductors
Bloch’s theorem, crystal momentum, effective mass.
Band structure of selected semiconductors.
Density of states in 3D, 2D, 1D and 0D semiconductors.
Carrier statistics: Fermi-Dirac distribution function.
Intrinsic semiconductors.
Mass-action law.
Doped semiconductors.
2. Basic concepts of quantum mechanics
Harmonic perturbation.
Transition probability. Fermi’s golden rule.
Density matrix formalism.
Charged particles in an electromagnetic field: interaction Hamiltonian.
3. Optical properties of semiconductors
Bulk semiconductors and Quantum Wells.
Transition selection rules.
Joint density of states.
Absorption and gain coefficient.
Intersubband absorption in quantum wells.
Strained quantum wells.
Spontaneous emission.
Radiative recombination dynamics: low and strong injection regimes.
Excitons in semiconductors.
4. Light Emitting Diodes (LED)
Internal quantum efficiency and photon flux.
Double heterostructure LEDs.
Carrier overflow and carrier leakage over barrier.
External quantum efficiency.
Blue LEDs.
5. Semiconductor Lasers
Quantum well lasers.
Distributed Feedback (DFB) lasers: coupled mode equations and threshold conditions.
Distributed Bragg Reflector (DBR) Lasers.
Quantum dot (QD) lasers: introduction and growing techniques of QDs.
General scheme of QD lasers.
Optical transitions in QDs.
Absorption and gain in QDs.
Quantum Cascade Lasers.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
The final assessment will be based on a written exam typically consisting of two questions. Generally, one question covers the theory taught in the course, while the second is a numerical exercise to evaluate the ability to solve practical, yet simplified, problems. Texts of previous examinations are available on the course website. An oral exam can be requested by either the students or the teacher to complete the evaluation process. This option is available exclusively to students who have achieved a grade of 18/30 or higher on the written exam.

Mauro Nisoli, Semiconductor Photonics: Principles and Applications, Editore: Societa' Editrice Esculapio, Anno edizione: 2016, ISBN: 9788893850025

The major goal of this course is to give the students the basic notions necessary in order to understand the physical properties of condensed matter. Since the topic is extremely vast, the course focuses on a particular aspect, i.e. the periodic structures and the physical consequences of the translational symmetry, starting from the Bloch theorem down to its various implications. These concepts are applied to the electronic states in a periodic potential and to the vibrational properties of periodic lattices. A brief introduction to the description of solids in terms of elementary excitations will also be given. Part of the course is dedicated to the methods of statistical physics that lie at the basis of the thermodinamical description of the properties of solids. The final part of the course is devoted to superconductivity: we will present the phenomenological aspects are discuss the occurrence of superconductivity in terms of the transition phase theory of Landau.

• Knowledge and understanding (DD1) The students will learn the fundamental physical laws, based on quantum mechanics and statistical physics, which regulate condensed matter phenomena. Due to the universal character of these laws the students will be exposed to their use also in other fields of physics such as quantum field theory and its applications to the investigation of fundamental particles.
• Apply knowledge and understanding (DD2) The students will acquire the correct perspective concerning the experimental techniques and the theoretical methods nowaday employed in the field of condensed matter physics. Since solid state physics is at the heart of the modern technological revolution the students will be made aware of the possible applications of the topics of the course and of their consequences on human life.
• Making Judgements (DD3) The students will develop a correct perception of the orders of magnitude of the physical quantities involved in the processes taking place in condensed matter. The students will be stimulated to learn how to implement a true research project starting from the basic physical ideas and making the proper strategic choices, with a particular emphasis on aspects such as the feasibility of the project as far as the human and economical resources are concerned.
• Lifelong learning skills (DD5) Students will acquire the capability to focus on the relevant aspects of a particular phenomenon and forecast the necessary steps, both on the experimental and on the theoretical ground, for understanding and exploiting it. All this in the perspective of a team work where the activity is split between team components but each of them will maintains an integrated vision of its specific duty.

The course focuses on the fundamental concepts of condensed matter physics and is mainly devoted to the study of periodic structures and their properties.
The topics covered are:

• Periodic structures and their description in real space
• Elements of the theory of symmetry
• Reciprocal space
• Electronic states in a periodic potential: the Bloch theorem
• Models with quasi-free electrons and strong bond
• Bands of insulators, metals and semiconductors
• Semiclassical dynamics of electrons in a solid
• Interaction between electrons
• Diffraction
• Classical theory of lattice vibrations: dispersion relations
• Quantum theory of lattice vibrations: the concept of phonon
• Elements of statistical physics
• Superconductivity
  The topics, many of them of considerable complexity, will be presented seeking to highlight their physical meaning and making sure to avoid it being overshadowed by the mathematical formalism. Although this is a course on the theoretical foundations of the theory of solids, the concepts will be exemplified from time to time by reference to experimental techniques actually used in the field of condensed matter.
  The course will consist of lectures and exercises during which the topics of the lectures will be discussed at a deeper level and in relation to their experimental implications.

Prova scritta obbligatoria, senza prove in itinere
The final exam will be in written form and will consist of a series of questions on a series of specific topics covered during the course. Each question will not simply involve the exposition of a specific topic but will be conceived in order to be multidisciplinary and aimed at verifying the knowledge acquired by the student on individual topics and also the comprehension of the discipline as a whole and the ability to cross correlate the various topics covered during the course.

Neil W. Ashcroft - N. David Mermin, Solid State Physics
H. Ibach - H. Lüth, Solid-State Physics, Editore: Springer
Ezio Puppin, Fisica dei solidi, Editore: Lecture notes on Solid State Physics

This course provides an in-depth exploration of the theoretical and computational tools used to analyze and predict the macroscopic behavior of complex systems. Beginning with a concise review of equilibrium statistical mechanics and the fundamental properties of microcanonical and canonical ensembles, the course introduces key models, such as the Ising model and its disordered variants (e.g., spin glasses), to study phase transitions and mean-field theories.
The curriculum emphasizes universal aspects of phase transitions and critical phenomena, including scaling laws, critical exponents, and the Renormalization Group framework.
A significant component of the course involves a hands-on numerical laboratory, where students will design and conduct numerical experiments using Monte Carlo simulations, providing practical experience in simulating and analyzing complex systems.

[DD1: Knowledge and insight]
Lectures and exercise sessions will allow students to:

• understand Statistical Mechanics. Students will improve their understanding of the collective phenomena emerging in interacting systems, with particular focus on the behavior of random and disordered systems
• gain insight on phase transitions. Students will explore the phenomenon of phase transitions and understand how Statistical Mechanics is able to explain the discontinuous changes in physical response that occur when a system undergoes a transition between different phases. We will identify critical points, draw phase diagrams, and analyze the concepts of order parameters and symmetry breaking.
  [DD2: Applying knowledge and insight]
  The numerical laboratories will develop the students' ability to build and run numerical simulations (and in particular Monte-Carlo simulations) to test, analyze and describe the behavior of complex mechanical systems.
  [DD3: Judgement]
  During the numerical lab and in the final project, students will develop their critical thinking and problem-solving skills by applying statistical mechanics concepts to analyze and solve complex problems emerging in real-world systems.
  [DD4: Communication]
  Students will be prepared to review critically, communicate and discuss the results of the simulations during the final exam.
  [DD5: Learning skills]
  A number of selected topics related to the course will be presented to invite the students to study in autonomy.

Programme
Introduction and Recap
Fundamentals of Equilibrium Statistical Mechanics
Ensembles and time averages: Microcanonical, Canonical, and Grand Canonical ensembles
Non-convex Helmholtz free energy and its role in phase transitions
Magnetic Systems and Mean-Field Theory
The Ising Model: Microscopic description and macroscopic behavior
Phase transitions: Classification, order parameters, and critical temperature
Mean-Field Theory: Concepts and applications
Spin Glasses: Introduction and methods
The Replica Method and its implications
Critical Phenomena and Universality
Scaling laws and universal behavior in phase transitions
Ginzburg-Landau Theory of critical phenomena
Critical exponents: Definitions, significance, and computation
Gaussian approximation for critical exponent analysis
Renormalization Group Techniques
Fixed points, flow diagrams, and scale invariance
Numerical Laboratory
Basics of Pseudo-Random Number Generators
Monte Carlo Simulation Techniques:
Importance sampling and the Metropolis algorithm
Simulated annealing: Optimization and applications
Direct Simulation Monte Carlo: Methods and challenges
Practical exercises in simulating phase transitions and solving optimization problems

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
Valutazione continua facoltativa (a scelta dello studente)
The assessment includes:

• a numerical lab test (splitted in two parts) in which the students will solve exercises related to the topics analyzed in the numerical labs; this part provides up to 12 points
• a written examination on the theoretical part of the course; this part provides up to 21 points
• an optional oral exam, either on the numerical lab or on the theoretical part of the exam, which modifies the grade obtained in that part.

K. Huang, Statistical Mechanics - 2nd Ed., Editore: Wiley, Anno edizione: 2008, ISBN: 978-8126518494
M.E.J. Newman, G.T. Barkema, Monte Carlo Methods in Statistical Physics, Editore: Clarendon Press, Anno edizione: 1999
Slides, and notes distributed by the Professor https://webeep.polimi.it/
Note: webeep platform of the course
S.K. Ma, Modern Theory of Critical Phenomena, Editore: Westview Press, Anno edizione: 2000
Wolfram Mathematical tutorial https://www.wolfram.com/broadcast/video.php?c=89&v=327&disp=list&p=1
Note: The software Wolfram Mathematica will be used during the course (licensed software provided by Politecnico di Milano).
No prior knowledge is assumed, but knowledge of matlab and/or programming languages (e.g., C) would help

The course provides the foundational concepts, methods, languages, and systems for ingesting, processing, and analyzing data that flows to enable real-time decisions. The course covers the topics of Streaming Data Engineering and Streaming Data Science.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn how to:
Identify problems that can be addressed with big data techniques tailored for velocity
apply the stream data analysis technologies for solving real-world problems
Applying knowledge and understanding
Given specific project cases, students will be able to:
Define and implement a streaming data analysis solution for the problem
Apply it on real data streams from smart IoT sensors
Making judgements
Given specific project cases, students will be able to:
Learn how to decide which streaming data analysis solution to apply and how to evaluate this decision
Communication
Students will learn to:
Write a report on a project describing and motivating the decisions taken and the results obtained
Present their work in front of their colleagues and teachers
Lifelong learning skills
Students will learn how to develop a realistic streaming data analysis project in all its phases

Streaming Data Engineering
This Streaming Data Engineering part of the course covers theoretical and practical aspects of Event-Based Systems (EBS), Data Stream Management Systems (DSMS), and Complex Event Processing (CEP). It uses the Event Processing Language (EPL) to illustrate typical DSMS and CEP operations. This segment equips students with essential skills for real-time data processing and analysis.
Event-Based Systems emphasizes efficient data collection and integration from various sources and redistribution of events to sinks, ensuring high throughput and fault tolerance. In particular, it presents Apache Kafka because it enables handling large-scale data streams, making it essential for robust data pipeline architectures.
Stream ingestion and Event-based Architectures
Apache Kafka as an essential building block of streaming data pipelines
hands-on producer-consumer APIs
Case study: ingesting data for a global retail
The Event Processing Language is a rich language that allows users to specify typical operations present in DSMS and CEP easily. It provides the ability to filter, aggregate, and transform data as it arrives. Moreover it allows identifying meaningful patterns and relationships in real-time data streams, enabling the detection of complex events from simple events.
Filtering, aggregating, and windowing data streams
Joining Data streams
Using the "every" clause and pattern guards to tame the torrent effect *
Hands-on with EPL on case studies in Retail, IoT, and Industry 4.0
Scaling stream processing with Apache Spark Structured Streaming focuses on the real-time processing and analysis of large datasets. Apache Spark's structured streaming capabilities provide scalability, fault tolerance, and high performance, enabling advanced analytics on streaming data.
Efficient DSMS operations
Hard to implement CEP operations
Hands-on practice with Spark Structured Streaming
Streaming Data Science
The Streaming Data Science part of the course covers the theoretical and practical aspects of time Series Analytics, Streaming Machine Learning, and Continuous Learning, equipping students with essential skills for real-time data analysis.
Time Series Analysis (TSA) focuses on developing models to analyze and forecast data points in chronological order. TSA approaches capture temporal dependencies and patterns, such as trends and seasonality, enabling accurate predictions and insights. TSA methods are also adept at handling non-stationarity in data.
⁠Stationarity and ⁠Decomposition
Time Series Forecasting baselines
⁠Temporal Dependence and SARIMAX forecasting models
⁠Advanced approaches to Time Series Forecasting
⁠Hands-on with the Statsmodels e Darts Python libraries
Streaming Machine Learning (SML) proposes models able to learn from data streams over-time. These models are incrementally updated as soon new data becomes available, avoiding retraining them from scratch, and adapting to many forms of non-stationarity (a.k.a. Concept Drift).
⁠Learning one sample at a time
⁠⁠Learning in the presence of Concept Drifts
⁠Streaming algorithms for classification and regression
⁠Hands-on with the River Python library
Continual Learning (CL) proposes strategies to address the problem of catastrophic forgetting when learning from a data stream using Deep Learning models. Forgetting happens when the model forgets what it learned before while learning a new concept after a drift. The goal is to achieve a balance between the ability to acquire new knowledge (plasticity) and the ability to remember past knowledge (stability).
CL Scenarios
Stability-plasticity dilemma
Evaluation metrics
⁠CL strategies
Hands-on with the Avalanche Python library

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i condizionato (a scelta del docente), individuale, senza revisioni periodiche, con presentazione/discussione finale
Valutazione continua facoltativa (a scelta dello studente)
The exam consists of a theoretical (written exam, with a possible oral discussion if necessary by the instructor) and an optional practical part (project work with oral presentation). Further contributions to the mark may come from optional continuous evaluations along the course using in-presence quizzes during the lessons and other interactive modalities in class (max two marks).
The written exam comprises a mix of theoretical questions regarding any course subjects and exercises regarding the technical content and how to apply it in practice. Students can get up to 30 in the written test. The whole exam is a closed-book evaluation. A minimum score on each part is requested.
The optional practical project requires using one or more of the technologies presented in the lectures. It solves a realistic streaming data analysis problem based on real or realistic datasets publicly available or provided by the teachers. Only students who get at least 27/30 in the written exam can opt for it. The maximum increment for the optional project is three marks.
The final grade is computed as follows: written text result + optional continuous evaluation result + optional practical project result. E.g., written text 27 + optional continuous evaluation 1 + optional practical project 3 = 30L
Type of assessment
Description
Dublin descriptor
Written test / continuous evaluation
Theoretical questions
Exercises focusing on streaming data analysis aspects
1,4
1, 2, 3
Assessment of project artefacts
Assessment of the design and implmenetation of the practical peoject work developed by the student
2, 3, 5
Oral presentation
Assessment of the presentation of the practical peoject developed by the student
2, 3, 4, 5

the event processing language (EPL) https://esper.espertech.com/release-9.0.0/reference-esper/html_single/
Kreps, Jay, I Love logs: Event data, stream processing, and data integration., Editore: O'Reilly Media, Inc., Anno edizione: 2014 https://books.google.it/books?id=gdiYBAAAQBAJ&lpg=PR3&ots=3yOdY3Vi7S&dq=I%20love%20logs&lr&pg=PR3#v=onepage&q=I%20love%20logs&f=false
Spark Structured Streaming https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html
George E. P. Box, Gregory C. Reinsel, Gwilym M. Jenkins, Greta M. Ljung, Time Series Analysis: Forecasting and Control, 5th Edition, ISBN: 978-1-118-67502-1 https://www.wiley.com/en-us/Time+Series+Analysis%3A+Forecasting+and+Control%2C+5th+Edition-p-9781118675021
Geoff Holmes, Ricard Gavaldà, Albert Bifet, Bernhard Pfahringer, Machine Learning for Data Streams: With Practical Examples in MOA, Editore: MIT Press, Anno edizione: 2018 https://www.google.it/books/edition/Machine_Learning_for_Data_Streams/0C9ZDwAAQBAJ?hl=en&gbpv=1
Lesort, T., Lomonaco, V., Stoian, A., Maltoni, D., Filliat, D., Rodríguez, N.D., Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges https://www.sciencedirect.com/science/article/am/pii/S1566253519307377

The course aims to provide the students with basic knowledge of the mechanical behavior of materials and structures

After succesfully completing the course, the students will:
have a solid knowledge and understanding of the mechanics of elastic and homogeneous solid bodies (DdD1)
be able to draw diagrams of internal forces in statically determinate beam structures (DdD2)
be able to compute stress and strain due to axial and shear forces, bending moments, and torsion (DdD2)
know and understand the fundamental principles behind the failure of materials (DdD1)
be able to apply the above described knowledge to the calculation of deflections of beam structures, under given load case scenarios (DdD2)

The course is organized in lectures and exercises. The covered topics are:

• Revision of vector algebra
• Kinematics, statics and internal forces of rigid beams in two dimensions, under concentrated forces and distributed loads
• Analysis of stress and strain in beam structures
• Principle of virtual work and deflection of beams
• Linear elastic mechanics of three-dimensional solids
• Failure criteria

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
The main exam will be a written test assessing:
the ability of the students to draw diagrams of internal forces for a given beam structure and loading scenario
the ability of the students to compute the distribution and intensity of axial and shear stresses in the cross-section of the baems
the knowledge gained by the students in any theoretical aspect delivered during the course
In addition to the written exam, the students may take an oral assessment covering any theoretical aspect delivered during the course, as well as their practical application through simple exercises

M.S. Williams, J.D. Todd, Structures: theory and analysis, Editore: Palgrave Macmillan

The course focusses on the analysis of nonlinear dynamical systems, that is, the set of techniques that can be used to understand and classify the behaviour of a nonlinear model, be it a nonlinear control system, a mechanical, electrical, environmental, social model, or any other set of nonlinear differential equations.
The goal of the course is to provide an introduction to nonlinear systems theory with particular focus on bifurcation analysis. We will use common tools to classify the behavior of a system, and learn to explain how its behaviour changes as a consequence of changes in parameters or control variables. The main bifurcations occurring in nonlinear systems will be discussed in detail: they will be first illustrated on simple examples, and then on case studies with the help of software tools.

Referring to the Dublin descriptors lectures and exercise sessions will allow the students to:
•(DdD 1) know and understand the basic results of nonlinear systems theory for the characterization of a system’s solutions;
•(DdD 2) apply the basic results of nonlinear systems theory to the characterization of a system’s sensitivity to model and control parameters;
The computer laboratory will allow students to:
•(DdD 2) use the Matlab toolbox Matcont to numerically analyze and characterize the nonlinear behavior of some relatively complex real-world case studies;

Introduction to nonlinear dynamical systems
Stability and phase portrait near equilibria and fixed points
Changes of variables and topological equivalence
Nonstationary asymptotic behavior of nonlinear dynamical systems: cycles, tori, chaos, multi-stability
Parameterized systems and structural stability
Bifurcations of equilibria and cycles, robustness analysis
Transitions from periodic to aperiodic oscillations.
Introduction to numerical bifurcation analysis and continuation methods.
The course is organized into: lectures, in which the theoretical notions are presented and illustrated with the help of simple examples; exercises, in which the theoretical notions are applied; computer labs, in which the students will be introduced to the Matlab toolbox Matcont and will work in small groups analyze a relatively complex nonlinear model.
All activities will be integral part of the course program.

Prova scritta obbligatoria, senza prove in itinere
A written exam at the end of the course, in which students will be asked to:

• solve simple numerical and analytical problems regarding the solutions of a given system and their sensitivity to model and control parameters (DdD1,DdD2);
• answer theoretical questions on all topics of the course (DdD1);
• answer practical questions on the computer labs (DdD2).

The goal of the course is to provide students with the ability to design and implement a conventional process flow for the fabrication of simple electronic and spintronic devices. The course covers the fabrication of films and heterostructures using different deposition techniques, as well as device fabrication by conventional and advanced lithography, with reference to examples of scientific or industrial relevance. The lectures are supported by an experimental activity session carried out at the Polifab laboratory of Politecnico di Milano.

DdD1 - Knowledge and understanding
Upon passing the exam, the student will have a basic knowledge of surface thermodynamics and the epitaxy of films and heterostructures. The student will know the physical and technological principles of physical vapor deposition (PVD), chemical vapor deposition (CVD), lithographic and etching techniques, as well as the principles of vacuum technologies. The student will understand the differences among the main deposition, lithography, and etching techniques.
DdD2 - Applying knowledge and understanding
Upon passing the exam, the student will be able to predict the chemical and morphological properties and growth dynamics of a film or heterostructure. They will be able to design a simple vacuum system and a simple lithographic process flow. They will be able to select the most appropriate deposition, lithography, and etching techniques depending on the application.
DdD3 - Making judgements
Upon passing the exam, the student will be able to design and implement the process flow for fabricating simple electronic or spintronic devices.

Part I - Deposition
Introduction: objectives, examples.
Vacuum technology: pressure, gas transport, vacuum systems.
Surface thermodynamics: surface properties, nucleation, and film growth modes.
Film epitaxy: mechanisms and the effect of strain.
Thermal evaporation: vapour pressure, deposition rate, and uniformity; electrical and e-beam evaporation, molecular beam epitaxy, pulsed laser deposition.
Sputtering deposition: plasma physics and basic processes, including DC, RF, reactive and magnetron sputtering.
Chemical Vapor Deposition: physical and technological aspects, with examples of processes
Part II - Lithography
Introduction to lithography: device scaling and Moore’s law.
Optical lithography: process flow and technology.
Physics of optical exposure and resolution enhancement technologies: diffraction, numerical aperture, illuminations, masks, lenses, and resists.
Advanced photolithographic techniques: maskless optical lithography, extreme ultraviolet lithography, and X-ray lithography.
Electron beam lithography: physical principles, process flow, and technology.
Other advanced lithographic techniques: soft and nanoimprint lithography, focused ion beam and scanning probe lithography.
Pattern transfer: dry and wet etching
Examples of lithographic process flows.
Laboratory activities
An experimental activity will be carried out at Polifab, the micro- and nanotechnology centre of Politecnico di Milano. This activity will introduce the student to the design and implementation of a conventional process flow for the fabrication of a simple spintronic device, such as a Hall bar or a GMR sensor, involving some of the deposition and patterning techniques discussed in the course.
Schedule
The course will be held in the second half of the first semester, from November to December.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The final examination will consist of a written test, in English, with exercises and open-ended questions. To pass the exam, students are required to demonstrate knowledge and a deep understanding of the physical and technological topics covered in the course, as well as the ability to think critically and connect the various topics of the course. The final assessment allows the student to demonstrate their ability to:

• design a simple vacuum system
• understand and predict the growth dynamics and the structural/morphological properties of simple films and heterostructures;
• design and discuss a conventional process flow for the fabrication of simple electronic and spin-electronic devices, from heterostructure/film deposition to patterning of the final device
• understand, select, and determine the most common parameters used in standard photolithographic processes;
• understand, select, and determine the most appropriate deposition, lithographic, and etching techniques for device fabrication.

Milton Ohring, Materials Science of Thin Films 2nd Edition, Editore: Academic Press, Anno edizione: 2002, ISBN: 978-0125249751
Note: (Part I of the course)
Marc J. Madou, Fundamentals of Microfabrication and Nanotechnology - vol. 2, Editore: CRC Press, Anno edizione: 2011, ISBN: 978-0849331800
Note: (Part II of the course)

Holography is a technology allowing the recording and re-projection of both the intensity and phase of a propagating wavefront of light. It has vast applications in metrology, biomedical research, and digital displays. However its requirement of sub-wavelength light detector and modulator, makes it one of the few technologies in which an analog implementation vastly outperforms its digital counterpart. As a consequence, further development of digital holography technologies is an important field of both academic and industrial research.
This course covers methods of holographic recording and projection of light. In the introductory part of the course, the physics of light propagation is discussed, with a particular focus on interference and diffraction phenomena. Traditional analog holography is then introduced, both in its theoretical interpretation and experimental implementation. The course then covers the acquisition of holograms using digital sensors, highlighting the existing advantages and limitations, and its applications in metrology and microscopy. Finally, existing methodologies for the calculation and experimental projection of arbitrary holograms are introduced, with particular emphasis on the potential of this technology in augmented reality applications.
The course is complemented by laboratory sessions. Most laboratory sessions consist in computer simulations of propagation of light, and in processing of existing digital holography datasets. In the final laboratory sessions, the students will prepare code for computer generated holography, and will have the opportunity to perform a practical experiments projecting computed holograms using phase and intensity spatial light modulators.

The lecture session will allow the students to fully comprehend the principles of analog and digital holography, including their features and limitations. They will be capable to accurately describe and discuss the working principle of any practical implementation of holographic technology, reasoning on the importance and the role of the design parameters of the systems (e.g. resolution, wavelength, time constraints).
Students will also be able to critically examine proposed real-world photonics applications and propose an appropriate design of an holographic recording or measurement experiment optimizing the performance of the system, and discuss the expected limitations in the results imposed by the physics of the system.
The laboratory sessions will allow the students to independently develop simple software for the simulation of holography systems, for the computer generation of holograms and for the data processing of digital holography datasets.
The exam modality is intended as an opportunity to practice soft skills such as preparation of presentations and public speaking

Lectures topics:
Analog holography (2 CFU):
Wave propagation of light
Interference phenomena
Hologram registration and reprojection
Practical implementations
Digital holography (1 CFU):
Hardware limitations
Data processing and reconstruction
Metrology applications
Microscopy applications
Computer generated holography (1 CFU):
Fourier transform based methods for image projection
Primitives based methods for structured illumination
Hologram design for phase modulators and intensity modulators
Applications in Microscopy
Applications in Lithography/manufacturing
Applications in Augmented reality devices
Laboratory topics (1CFU):
Simulation of wave propagation of light in three dimensions
Processing of recorded digital holograms
Computer generation of holograms and projection experiments

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, con presentazione/discussione finale
The students will be evaluated through an oral exam. The dates of the oral exams will be decided based on the teacher’s and the students’ schedule, at least one exam date will be made available for each of the exam sessions.
During the exams the students will be asked to provide a 15 minutes presentation regarding a topic of the course of their choosing. The students are encouraged to present a topic based on their literature review which should provide more detailed information and insight compared to what has been covered in the lectures. At the end of the presentation, the oral exam will continue with questions about the presentation topic and the rest of the course's program.
The oral exam is expected to last approximately 40 minutes per each student.

Kostuk, Raymond K., Holography: Principles and Applications, Editore: Taylor & Francis, Anno edizione: 2024

This laboratory course has the aim to let students practice the design and implementation of optical microscopy for biomedical and pharmacology applications, including methods for cell manipulation and live imaging of 3D biological samples at subcellular resolution.

The expectation is to provide students with applied and practical skills on optical microscopy methods to be used independently in the investigation of complex biological samples and cell models developed in the field of bioengineering and biophysics. During the course students will work in group.
At the end of the course will have students acquired the following ability.
Applying knowledge and understanding: Design and execute experiments to explore biophysical and bioengineering challenges, using specialized optical microscopy methods.
Knowledge and understanding: Gain a foundational comprehension of optical fluorescence microscopy and related techniques, and their applicability in bioengineering contexts.
Applying knowledge and understanding: Identify the critical parameters to be investigated and the best techniques to be applied for the investigation of biological tissues.
Communication: Articulate the principles, advantages, and performance metrics of fluorescence processing methods and associated techniques in both written and verbal forms.
Lifelong learning skills: Acquire a level of expertise that empowers communication about biophotonics with peers, academic professionals, and the broader community.

Topic lectures and laboratoury activity
Conventional optical microscopy methods (brightfield, darkfield, phase, DIC)
Fluorescence labelling (antibodies, gene reporter, live cell staining)
Processing of microscopy datasets
Image analysis quantification for biomedical applications
Acquisition of phase contrast images of cultured cells
Acquisition of cells and tissues with widefield, confocal and light sheet fluorescence and multiphoton microscopes.
Segmentation, tracking, classification
Drug testing, tissue formation/regeneration, embryonic
Imaging in optically-accessible culture devices (cell-seeded scaffolds, microfluidic bioreactors, etc.), in vivo imaging in bioengineered cell models (organoids, cellularised constructs, gene edited cells, etc.), advanced imaging technique (FRAP, FRET) for protein diffusive model generation and protein-protein interaction.

Prova orale obbligatoria
The final assessment consists of an oral presentation of a scientific paper related to the topics covered in the course. Students are expected to present and critically discuss the paper, demonstrating their understanding of both the specific work and its connection to the experimental apparatus utilized during the course.

To give examples of application of ultrashort light pulses to the study of dynamical processes in physics, chemistry and biology.
To discuss real-world applications of ultrashort pulses to microscopy and material processing.
The acquired knowledge will enable the student to work in advanced research laboratories as well as in high-tech companies developing laser sources or using them for applications in micromachining, telecommunications, sensing or biomedicine.

At the end of the course the student will acquire knowledge and understanding of the underlying physics and the technical working principles of ultrashort pulse laser systems (DD1).
The student will be able to apply this knowledge and understanding to different scientific and industrial systems using ultrashort pulse lasers (DD2).
The student will be able to make judgements on the design of such systems for applications to spectroscopy, imaging or material processing (DD3).
The student will acquire learning skills that will enable her/him to read andvanced, graduate-level textbooks on nonlinear optics and photonics as well as papers on peer-reviewed scientific journals (DD5).

Ultrafast spectroscopy
introduction to ultrafast spectroscopy techniques;
degenerate, two-colour and broadband pump-probe;
density matrix formalism for the description of light-matter interaction;
four-wave mixing and photon echo, 2D spectroscopy;
ultrafast processes in metals and semiconductors;
applications to femtochemistry and femtobiology.
Real-world applications of ultrashort pulses
Coherent Raman microscopy: Coherent Anti-Stokes Raman Scattering (CARS), Stimulated Raman Scattering (SRS)
Nonlinear microscopy techniques: second-harmonic and third-harmonic generation microscopy, multi-photon microscopy
Material processing with ultrashort laser pulses: high precision cutting and drilling
Micro- and nano-fabrication with ultrashort laser pulses: two-photon polymerization.
Large-scale laser facilities (free electron lasers…)

Prova scritta obbligatoria, senza prove in itinere
The evaluation will consist of a written exam with questions and/or problems covering the topics of the lectures.
The exam will test the depth of understanding by the student of the handled topics, the developed critical skills and the capabilities to effectively organize the presented material, using a proper lexicon and efficiency and linearity of the discussion.

Andrew M. Weiner, Ultrafast Optics, Editore: Wiley, Anno edizione: 2009, ISBN: 978-0-471-41539-8
Sandro De Silvestri, Rocio Borrego Varillas, Aurelio Oriana, Cristian Manzoni, Giulio Cerullo, Tunable few-optical cycle pulses and advanced ultrafast spectroscopic techniques, Editore: Societa Italiana di Fisica, Anno edizione: 2018, ISSN: 0393697X, Fascicolo: 41 http://dx.doi.org/10.1393/ncr/i2017-10143-7

This course explores some of the advanced technologies enabling development of modern quantum optics and physics. An introductory review of quantum theory provides the students with the necessary theoretical background. The following topics cover the tools and techniques that have been used for electromagnetic control of atomic motion and some relevant experimental applications.

Knowledge and understanding (DD1)
Attending the lectures and passing the exam will allow the student to:

• understand the physical basis of atom-light interactions at a quantum level;
• learn the fundamental tools and techniques for manipulation of atoms and molecules;
• discover the technology behind modern optical frequency standards;
  Applying knowledge and understanding (DD2)
  The student will be able to apply the basic knowledge described above to:
• explain the operating principles of some prototypal optical devices developed for quantum optics;
• design the basic layout of a laser cooling device and ions trap;
• transfer the physical concepts to applications requiring manipulation of atoms;
  Communication skills (DD4)
  The exam requests the students to communicate clearly what they have learnt, with rigorous argumentation.
  Lifelong learning skills (DD5)
  Students will be capable to autonomously follow with greater awareness the new trends in the high-tech market dealing with atom optics and quantum technologies (from quantum communications to quantum computing).

The course of "Atom Optics and Quantum Technologies" is the Part 2 of a larger integrated course called "Quantum Optics and Technologies"; Part 1 is called "Quantum Opics and Information". Lessons related to Part 1 and 2 are given in the first and second half of the semester, respectively. Hence, students registered only to the course of "Atom Optics and Quantum Technologies" will attend their lessons starting from the second half of the semester, just by the end of lessons related to Part 1. The following is the list of subjects covered by the course of "Atom Optics and Quantum Technologies".
Quantum theory of optical resonators and amplifiers
1. Linearized quantum noise description
2. Quantum equation of motion for the cavity mode
3. Optical amplifiers
4. Squeezed light generators
Quantum theory of atom-light interactions
5. Basic concepts and definitions
6. Optical Bloch equations
7. Force on two-level atoms
Optical cooling and trapping of atoms and molecules
8. General properties concerning laser cooling
9. Photon recoil and Doppler cooling
10. Optical molasses, sub-Doppler cooling
11. Cooling and trapping of ions
12. Molecular cooling: direct and indirect methods
Application
13. Atomic fountains clocks
14. Atom mirrors, atom laser
15. Optical tweezer
16. Zeeman slower
17. Magneto-optical trap (MOT)

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The exam is made of a test in written form. The test will will have a duration of 1.5 hours, and will typically consist of 3 open questions on different topics. An optional oral exam, requested by the student or by the teacher in specific cases, may complete the evaluation.
The aim of the exam is to ascertain:

• the understanding of the physical basis of quantum optics and information;
• the knowledge of the definitions, theorems and general concepts related to quantum optics and quantum information;
• the capability to discuss, both qualitatively and quantitatively, the quantum physical models and protocols relevant for the course.
• the comprehension of the experimental techniques for laser cooling and trapping of atoms and molecules
• the understanding of relevant applications of atom optics and quantum technologies
  Students can take the exam in the usual five dates along the year (two in summer session, one in autumn session and two in winter session). As the course of "Atom Optics and Quantum Technologies" is the Part 2 of a larger integrated course called "Quantum Optics and Technologies", each exam will provide students with the chance to give the test on Part 1 and/or Part 2, separately. To this end, each exam is scheduled with an overall duration of 3 hours and 15 minutes: it starts with 1.5 hours dedicated to Part 1, then 15 minutes resting, and finally 1.5 hours on Part 2. Students registered to the course of "Atom Optics and Quantum Technologies" can give their test in the second half of the exam after the resting.

Gilbert Grynberg, Alain Aspect, Claude Fabre, Introduction to Quantum Optics, Editore: Cambridge University Press
Hans-A. Bachor, Timothy C. Ralph, A guide to experiments in quantum optics, Editore: WileyVCH Verlag GmbH & Co. KGaA
Christopher J. Foot, Atomic Physics, Editore: Oxford Master Series in Physics
Wolfgang Demtröder, Laser Spectroscopy 1 - 2, Editore: Springer

Introduce the student to the fundamentals of biomedical optics presenting the physical basis, introducing the mathematical tools, discussing instrumental implementations and photonics advancements, and describing examples of applications. A seminar session to discuss scientific articles in the classroom will improve the critical analysis and team working capabilities.

• Knowledge and understanding
  The students will improve knowledge and understanding on light-matter interaction, physics of photon propagation in diffusive media, wave physics, photonics instrumentation, inverse problems and optical tomography in diffusive media for biomedical applications.
• Apply knowledge and understanding.
  The students will be invited to discuss scientific articles in the classroom, discussing its objectives, motivating the methodology and critically analyzing the results obtained.
  Making Judgements
  The students will be required to judge the plausibility of their own presentation as well as, in common discussions, of the ones of other teams.
• Lifelong learning skills
  Students will be faced with the key difference between theoretical models and practical implementation, as well as the ability of problem solving, team-working and workflow organization.

Interaction of light with biological tissue.
Basics of optical spectroscopy of biological tissue (techniques and instrumentations)
Diffuse optics, time-resolved techniques, frequency-resolved techniques.
Photon density waves.
Diffuse Optical Tomography in the linear (Born approximation) and non-linear regime.
Inverse Problems.
Photoacoustics tomography
Applications (e.g. functional imaging of brain activity, molecular imaging, optical mammography) and new perspectives.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The final assessment will be based on a written test with open questions on the topics of the course and seminar activity

Introduce the student to the fundamentals of optical imaging from the cellular to the tissue level, presenting the physical basis, discussing instrumental implementations and describing examples of applications, with particular emphasis on biophysics and biology topics.

• Knowledge and understanding
  The students will learn the fundamentals of bio-photonics imaging technologies, that operate from the molecular level up to the tissue scale. The student will learn the major applications of optical microscopy in biology and in clinical diagnostics.
• Apply knowledge and understanding
  The students will be able to design autonomously an optical system for microscopy and imaging. The students will be able to evaluate the resolution and contrast in optical microscopy and nanoscopy systems.
• Making Judgements
  Given a specific optical imaging system, the students will have the ability to analyze the point spread function and compare the performances of different optical setups.
• Lifelong learning skills
  Students will be capable to understand how to take into account, and how to overcome the diffraction limit in an optical system, with particular emphasis on optical microscopy. Students will be capable to learn autonomously how to use an optical microscope and how to assess its performances.

Optical microscopy and nanoscopy:
Propagation of light in an optical microscope: point spread function in an optical microscope and 3D imaging theory.
Classical microscopy techniques for cellular and tissue imaging: bright-field, darkfield, phase contrast, fluorescence.
Three-dimensional imaging of tissues: confocal microscopy, structured illumination microscopy, 2-photon microscopy.
Three-dimensional imaging with interferometric techniques and imaging of the eye: Optical Coherence Tomography.
Imaging at the molecular level with optical microscopy, circumventing the resolution: Single Molecule Localization and Stimulated Emission Depletion Microscopy.
Applications of microscopy and super-resolution microscopy in cellular imaging, biophysics and biomedicine.

Prova scritta obbligatoria, senza prove in itinere
The final assessment will be based on a written test with open questions on the topics of the course.

Jerome, Introduction to Optical Microscopy, Editore: Cambridge University Press, Anno edizione: 2019, ISBN: 9781108552660

The course aims at providing a picture of the electronic probe microscopy techniques as general as possible, starting from the basics of the major techniques and examples of prototypical applications up to state of the art scientific cases. The main goal is to provide the student with the ability of assessing the feasibility and effectiveness of microscopic measurements to tackle a given problem. The course will focus on the information that can be obtained (morphology, chemical contrast, short-and long-range structure, spectroscopy) and on the choice of the microscopic techniques that can be effectively employed to investigate a system in a given environment. Experimental and numerical modeling tutorials will give hands on experience of selected concepts.

DdD1 - Knowledge and understanding
Upon passing the exam, the student knows the fundamentals of electron and scanning probe microscopies: she/he is able to draw the layout of a microscope and to comment its main assembly in terms of fundamental Physics (main phenomena and their order of magnitude).
DdD2 - Applying knowledge and understanding
Upon passing the exam, the student is able to select the most appropriate microscopical approach, within the range of techniques discussed, to address a standard problem in material microscopy and to discuss its advantages and disadvantages with respect to the other techniques. The student has a basic understanding of the numerical approaches used to model selected microscopic technique and phenomena.
DdD3 - Making judgements, Communication, Lifelong learning skills
Upon passing the exam, the student is able to read critically scientific literature on microscopy: she/he is able to explain and discuss the techniques adopted by researchers and the evidences supporting their conclusions, eventually proposing a different approach to attain the same or and improved result.

Topics

• Probes with high spatial resolution and state of the art in research and applications.
• Interaction of free electrons and bound electrons with matter.
• Information obtained by using electronic probes.
• Numerical modeling of electron transport in beams and electron -matter interaction.
  a) Far field electron microscopy
• Introduction to electron microscopy (EM)
• Diffraction limit and ray optics
• The scanning electron microscope (SEM)
• Interaction between electron and matter
• Secondary electron contrast
• Depth of field
• Charging and other imaging artifacts
• Back-scattered electron contrast
• Basics of the transmission electron microscope (TEM)
• Sample preparation
• Bright and dark field imaging
• The mass-thickness contrast
• Direct and reciprocal space imaging
• The diffraction contrast (basic aspects)
• The phase contrast (basic aspects)
• The scanning transmission electron microscope (STEM)
• Spectroscopic and time resolved microscopy (basic aspects)
  b) Scanning probe microscopies
• Introduction to scanning probe microscopy (SPM)
• Elements of a scanning-probe microscope
• Tip-sample forces
• Atomic Force Microscopy (AFM) static operation: constant height and constant force
• Shift in the natural frequency of a harmonic oscillator under a force gradient
• Amplitude-modulation dynamic AFM operation
• Dynamic AFM operation: sensitivity in amplitude modulation, frequency-modulation techniques
• Noise and resolution in AFM
• Image analysis in AFM
• Electrostatic and Magnetic Force Microscopy (EFM and MFM)
• Imaging artifacts in SPM
• Scanning near-field optical microscopy (SNOM)
• Introduction to scanning tunneling microscopy (STM)
• Atomic resolution with STM
  Laboratory visits
  Students will attend hands on numerical modeling tutorials about selected features.
  Students will attend experimental laboratory tutorials in image analysis and practical use of research instrumentation, with possibly support of video tutorials.
  Schedule
  The course will be held in the first half of the first term, from mid-September to the beginning of November.
  Notes
  The range and detail of the course will be adapted to the level of the class during the course and may change significantly.

Prova orale obbligatoria
The exam consists of an oral test in two parts of about half an hour each, one about Electron Microscopy and the other about Scanning Probe microscopy.
In each part:

• The student asked to present selected topics of the course, starting from the fundamentals through increasing level of detail and difficulty.
• The student will be asked to select its microscopic technique of choice to address a scientific case and to comment about the limits of application, advantages, and disadvantages.
• The student will be required to briefly present a scientific paper, chosen in agreement with the teacher, where electron or SPM microscopies is the main investigation techniques. The presentation will be focused on the information attained by the use of microscopy and the technical approach adopted to obtain it. As an alternative to one of the two scientific papers, students may choose to incrementally develop and discuss a numerical model discussed within the numerical modeling tutorial.

R. F. Egerton, Physical Principles of Electron Microscopy: An Introduction to TEM, SEM, AEM, Editore: Springer, Anno edizione: 2008, ISBN: 978-0-387-25800-3
Note: textbook for the TEM and SEM section
C. Colliex, La microscopie éléctronique, Editore: Presses universitaires de France, Anno edizione: 1998, ISBN: 978-2-1304-8665-7 http://www.stem.lps.u-psud.fr/la-microscopie-electronique
Note: Introductory reading on TEM, in French.
I. M. Watt, The principles and Practice of Electron Microscopy, Editore: Cambridge University Press, Anno edizione: 1997, ISBN: 0-521-43591-9
Note: Advanced text on Electron Microscopy
Unknown, A practical guide to Scanning Probe Microscopy, Editore: VEECO, Anno edizione: 2005 http://www.veeco.com/pdfs/library/SPM_Guide_0829_05_166.pdf
Note: Introduction to Scanning Probe Microscopy by a well-known manufacturer
E. Meyer, H. J. Hug, R. Bennewitz, Scanning Probe Microscopy: The Lab on a Tip, Editore: Springer, Anno edizione: 2003, ISBN: 3-540-43180-2
Note: Examples of applications

LASER AD ALTA INTENSITA' PER APPLICAZIONI NUCLEARI E FISICHE II L'insegnamento presenta i processi di amplificazione ed esamina esempi di strutture laser ad alta energia approfondendo gli aspetti legati ad alcune applicazioni, quali la fusione nucleare a confinamento inerziale, l'accelerazione di particelle, la generazione di raggi X e gamma secondari, la generazione di impulsi XUV ad attosecondi. Argomenti (in sintesi): amplificazione degli impulsi e processi non lineari; sistemi laser ad alta intensità; sistemi laser per la fusione nucleare a confinamento inerziale, per l'accelerazione di particelle e per la generazione di raggi X e gamma; generazione di impulsi XUV ultrabrevi.

The course presents the two most important manifestation of ordering phenomena in solids beyond the crystal lattice: magnetism and superconductivity. The two are crucial for the understanding of the transport properties of crystalline materials, and can be exploited in a multitude of applications. Magnetism and superconductivity are governed by microscopic interactions, ultimately among electrons, but the macroscopic phenomenology is the result of interatomic interactions and dimensionality. Bose-Einstein condensation (BEC) and superfluidity are also presented as they share with magnetism and superconductivity the property of being macroscopic quantum phenomena.
The objective of the course is thus to provide a broad knowledge and understanding of ordering phenomena in solids. Phenomenology, experimental methods and theoretical models are considered and confronted. More broadly the goal is to introduce the students to the real world of advanced condensed matter research, with special emphasis on phenomenology and experimental insight.

Lectures and seminars will allow students to:
Know the fundamental concepts of ordering phenomena
Recognize the different types of magnetic order
Know the general phenomenology of superconductivity, superfluidity and BEC
Determine which experimental methods can be used in the study of magnetism and superconductivity and, in general, of ordering phenomena in solids
Read and understand original research papers in the fields of magnetism and superconductivity
Present a brief seminar on basic concepts and advanced examples on modern magnetism and superconductivity
Therefore, after passing the exam, the student will be able to:
DD1 - Understand the principles of magnetism and order phenomena, know the terminology related to them, employ the basic models applicable to them
DD2 - Recognize analogue of magnetism and superconductivity in other contexts and apply models ant techniques in new contexts of physics and technology
DD4 - Communicate their own perception of physics phenomena in a formal presentation and elaborate on them

Magnetism of isolated magnetic moments
Phenomenology: recalling diamagnetism, paramagnetism.
Weiss model for ferromegnetism and antiferromagnetism
Atomic moments.
Hunds rules
Crystal field: origin of CF, quenching of orbital moments, Jahn-Teller effect.
Exchange interaction
Examples of simple 3d transition metal oxides (NiO, MnO, Fe3O4)
Goodenough Kanamori rules
Perovskites
Manganites and cuprates
Magnetic order and magnetic structures.
Order parameters
Heisenberg and Ising models
Magnetism in metals
Pauli paramagnetism
Landau diamagnetism
Stoner model
RKKY interaction, Kondo effect
Excitations in magnetic systems
Spin waves
Stoner excitations
Magnetism at low dimensionality
Spin chains and spin ladders
2 dimensional magnets
Thin films
Bose-Einstein Condensation (BEC) and Superfluidity:
Bose-Einstein statistics
BEC principles
BEC of ultra-cold atomic gas
Macroscopic wave function and quantum fluids
Superfluid HeII
Superfluid flow and vortices
Superconductivity:
Phenomenology: transport, susceptibility, thermodynamics
London equations
Josephson effect
Ginzburg-Landau model
BCS theory
Cuprate superconductors (also as an example of 2D antiferromagnets).
Experimental techniques.
Magnetic resonances, Mössbauer spectroscopy, muon spin rotation.
Elastic and inelastic scattering of neutrons and x-rays for magnetic structure and magnetic excitations.
Synchrotron radiation based techniques for magnetism: selected examples of X-ray spectroscopy.

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, con presentazione/discussione finale
The exam consists of two parts held during a single interview, in English.
1) A seminar of 20-25 minutes, on a subject pre-assigned by the teacher. The student has to present the general concepts of the theme in a correct and complete way, providing some examples at the level of the lectures and of the text book. The student is also invited to look for original examples, applications, interconnections with other subjects, to demonstrate a higher level of understanding. A PowerPoint presentation is best suited for the seminar, but black/white board option is also possible.
2) Two or three questions on the core of the course as presented in the lectures. The student is requested to answer at the black/white board to show their understanding of the basic concepts and notions of the course. Theoretical and experimental aspects will be valued equally important.
A fully successful exam (27-30/30) will be deemed when a solid and broad mastering of the concepts of ordering phenomena in solids is demonstrated.
An average grade (22-26/30) will be the result of fairly complete understanding of individual themes but with limited interconnection among subjects.
A pass level (18-21/30) will correspond to a minimum knowledge of individual notions.

S. Blundell, Magnetismm in condensed matter, Editore: Oxford University Press, ISBN: 978-0198505914
James F. Annett, Superconductivity, Superfluids and Condensates, Editore: Oxford university Press, Anno edizione: 2013, ISBN: 978-0-19-860756-7
Harald Ibach, Hans Lüth, Solid-State Physics: An Introduction to Principles of Materials Science, Editore: Springer, ISBN: 978-3540938033

The aim of this course is to present the fundamentals of micro and nanomagnetism necessary to understand the recent developments in the field of spintronics, thanks to a mix of lectures, exercices, informatic and experimental laboratories. In particular, a platform for micromagnetic simulation (OOMMF/MUMAX) will be presented, in order to provide the students with the essential tools for designing and analysing magnetic nano-devices. Experiments dealing with the characterization of magnetic materials and spintronic devices will be also carried pout with the participation of students. Finally, the lectures of the last part of the course are intended to review the recent advances in the field.

Knowledge and understanding
Upon passing the exam, the student:

• knows the principles of micromagnetism
• knows the basic analytical and computational approaches to calculate the micromagnetic configuration of a system
• knows the foundations of spintronics
• understands the structure and behaviour of conventional spintronic devices
• understands a scientific paper dealing with nanomagnetism and spintronics
  Applying knowledge and understanding
  Upon passing the exam, the student:
• is able to solve analytical problems connected to magnetism and spintronics
• is able to use a micromagnetic software (OOMMF) for the computation of static and dynamic micromagnetic configurations
• is able to design the basic structure of a spintronic device
  Making judgements, Communication, Lifelong learning skills
  Students who decide to prepare a project on micromagnetic simulation, as alternative to the written test:
• are able to model a real micromagnetic problem, identifying the relevant parameters and factors to be taken into account
• are able to solve a realistic problem and draw a general conclusion on the optimization work carried out via simulations
• are able to present their work in a report mimicking a scientific paper
• are able to communicate their work during the oral examination

Nanomagentism is a modern discipline devoted to the study of magnetism in nanoscale objects. The "nano-world" opens unforeseen possibilities to develop new devices and paradigms exploiting the spin and orbital angular momentum of electrons and other quasi-particles (e.g. domain walls, magnons) propagating in engineered magnetic structures. Spintronics, in particular, is a branch of nanoelectronics aiming at developing new electronic devices taking advantage of the spin degree of freedom in addition to the charge of carriers.
The program will consist of lectures and exercises devoted to these topics:
Micro and NANO magnetism
Demagnetizing field, magnetostatic energy. Landau magnetic free energy and its contributions (exchange, anisotropies, magnetostrictions). Domain walls. Micromagnetic simulations (OOMMF). Coherent magnetization reversal (Stoner Wohlfart model) and reversal via propagation of domain walls. Magnetic nanoparticles. Domain wall conduits. Magnetic coupling in multilayers (Néel coupling, Exchange bias, Bilinear coupling)
Classical-Spintronics
Two currents model and spin dependent scattering. Giant magnetoresistance in CIP and CPP configurations. Spin accumulation and Valet-Fert model. Tunneling magnetoresistance and magnetic tunneling junctions. Non volatile magnetic memories (MRAMs) and magnetic sensors. Spin transfer torque. Magneto-electric coupling. Spin injection, manipulation and detection in semiconductors.
Spinorbitronics
Rashba based devices and Spin-FET. Spin currents. Direct and inverse spin Hall effect. Antiferromagnet spintronics.
LABORATORY ACTIVITIES
Laboratory instruction in specific techniques of magnetic characterization of materials and devices will be provided, at the laboratory of Nanomagnetism located within the facility Polifab. Informatic laboratories connected to the usage of the micromagnetic platforms OOMMF/MUMAX are also carried out woth the help of a tutor who will also assist students in the preparation of a project which can replace the written test.

Prova scritta facoltativa (a scelta dello studente), senza prove in itinere
Prova orale obbligatoria
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The examimation is made of two parts: (i) a written test (1 hour) with some exercises, (ii) an oral examination. The written test can be replaced with a project on the micromagnetic simulation and investigation of selected magnetic structures.

S. Blundell, Magnetism in Condensed Matter, Editore: Oxford Master Series in Physics, Anno edizione: 2001, ISBN: 978-0-19-850591-4
Note: Good textbook on the fundamentals of magnetism and micromagnetisms
Amikam Aharoni, Introduction to the Theory of Ferromagnetism, Editore: Oxford University Press, Anno edizione: 2001, ISBN: 0198508093
Note: Reference text for micromagnetism
Supriyo Bandyopadhyay, Marc Cahay, Introduction to SPINTRONICS, Editore: CRC Press, Anno edizione: 2015, ISBN: 9781482255591
Note: Reference text for Spintronics
J. M. D. Coey, Magnetism and Magnetic Materials, Editore: Cambridge, Anno edizione: 2010, ISBN: 9780521816144
Note: Handbook of magnetism

The key goal of the course is to provide a very successful example of a mathematical rigorous formulation of a physics theory - quantum mechanics -, starting from first principles (postulates) down to the discussion of concrete phenomena in atomic physics. In this respect, the course is meant to illustrate some typical features of modern mathematical physics and some insights on contemporary research lines in the field.
A self-contained presentation of quantum mechanics and its mathematics, suited in particular for students with mathematical background, is given. It is shown that a fully rigorous mathematical construction can lead to non-trivial physics results, so testifying that mathematical physics can in fact shed light on physics phenomena. Indeed, all the most intriguing and counter-intuitive phenomena of quantum mechanics (uncertainty, probabilistic behavior, interference etc.) as well as their mathematical formulation are discussed. On the other hand, several mathematical tools on functional analysis, operator theory, functional inequalities, etc., are introduced and discussed in details so that the mathematical content of the course is valuable as well for students more oriented towards abstract mathematics.
We denote by (*) the topics covered by the 5 CFU version of the course.

From the physics point of view, we expect that students without any previous knowledge of quantum mechanics acquire a basic understanding of the main features of the theory, while those who have already attended physics courses on the topic discover a more mathematically sounded presentation of quantum mechanics.
In addition, we expect that the students at the end of course

• know the first principles of quantum mechanics and are able to translate them in the mathematical language (*);
• are able to describe how the core physical aspects of quantum mechanics (uncertainty, probabilistic behavior, dispersion, interference, etc.) follow from the postulates of the theory (*);
• know the rigorous mathematical modelling of simple quantum systems as the free particle, the harmonic oscillator and the hydrogen atom (*);
• know the key features of the quantum description of atomic matter and many-body systems;
• understand the mathematical framework of non-relativistic scattering theory;
• know the basics of second quantization and quantum field theory;
• get an introduction to some research lines about the mathematical features of quantum mechanics.

1. Postulates of Quantum Mechanics (*)
2. Observables and States (*)
Brief recap of operator theory; Heisenberg uncertainty relation; self-adjointness criterion and self-adjoint extensions; spectral theorem and probabilistic interpretation of quantum mechanics; spectral decomposition; mixed states and density matrices; Stone theorem and existence of the dynamics.
4. Free Particle and Harmonic Oscillator (*)
Free particle: time evolution of wave packets, dispersion and interference, semiclassical limit; harmonic oscillator: spectrum, creation and annihilation operators, coherent states.
5. Hydrogen Atom (*)
Self-adjointness and stability; spectrum; angular momentum; eigenfuctions.
6. Perturbation Theory (*)
Perturbation theory in finite dimensions; analytic perturbation theory; perturbation of spectral projectors; variational methods: min-max principle; physical applications: Zeeman effect, anharmonic oscillator.
7. Many-body Quantum Mechanics & Atomic Matter
Spin and identical particles: bosons and fermions; reduced density matrices; non-interacting many-particle systems; atomic Hamiltonians and stability of matter of the first kind; Lieb-Thirring inequalities; stability of matter of the second kind; Bose-Einstein condensation, superfluidity and superconductivity; nonlinear models for condensed matter physics.
8. Scattering
Scattering by a potential; time-dependent scattering theory; asymptotic completeness; Lippman-Schwinger equation and generalized eigenfunctions.
9. Quantum Field Theory
Fock spaces: the mathematics of second quantization; annihilation and creation operators; Fock space calculus; free and regular field theories: time-zero and spacetime fields, spin-boson and Nelson models; interacting field theories and renormalization.

Prova orale obbligatoria
Valutazione continua facoltativa (a scelta dello studente)
The exam consists of two components: a final oral exam (worth up to 27 points) and ongoing assessments (optional, worth up to 6 additional points).
The two assessment sessions (up to 3 points each), held halfway through the course and at its end, are optional and consist of exercises completed during the exercise classes. They may be replaced by a question session covering the entire syllabus during the final exam.
The final oral exam may take the form of a presentation on a topic chosen from a list provided by the instructor, followed by questions on that topic, or a traditional oral examination covering all the material discussed during the course.
The exam for the 5 CFU version of the course consists in a traditional oral examination.

lecture notes on WeBeep
Note: (handwritten)
lecture notes on WeBeep
Note: (in italian)
Alessandro Teta, A Mathematical Primer in Quantum Mechanics, Editore: Springer, Anno edizione: 2018, ISBN: 9783319778938
Alberto Galindo, Pedro Pascual, Quantum Mechanics I, Editore: Springer, Anno edizione: 1990, ISBN: 978-3-642-83854-5
Alberto Galindo, Pedro Pascual, Quantum Mechanics II, Editore: Springer, Anno edizione: 1991, ISBN: 978-3-642-84129-3
J. Blank, P. Exner, M. Havlícek, Hilbert Space Operators in Quantum Mechanics, Editore: Springer, Anno edizione: 2010, ISBN: 9789048180127
E.H. Lieb, R. Seiringer, The Stability of Matter in Quantum Mechanics, Editore: Cambridge University Press, Anno edizione: 2009, ISBN: 9780511819681
M. Lewin, Spectral Theory and Quantum Mechanics, Editore: Springer Cham, Anno edizione: 2024, ISBN: 9783031668777
A. Arai, Analysis on Fock Spaces and Mathematical Theory of Quantum Fields, Editore: World Scientific Publisher, Anno edizione: 2018, ISBN: 9789811288425
M. Reed, B. Simon, Methods of Modern Mathematical Physics vol. 1 - Functional Analysis, Editore: Academic Press, Anno edizione: 1981, ISBN: 9780125850506
M. Reed, B. Simon, Methods of Modern Mathematical Physics vol. 2 - Fourier Analysis, Self-adjointness, Editore: Academic Press, Anno edizione: 1975, ISBN: 9780125850025
M. Reed, B. Simon, Methods of Modern Mathematical Physics vol. 3 - Scattering Theory, Editore: Academic Press, Anno edizione: 2012, ISBN: 9780123958273
M. Reed, B. Simon, Methods of Modern Mathematical Physics vol. 4 - Analysis of Operators, Editore: Academic Press, Anno edizione: 2012, ISBN: 9780123958280

To give the students the knowledge of the main modalities for the functional investigation of the brain, ranging from the physical principles exploited for data creation and recording to methodologies for signal, image, and data analysis (feature extraction and classification). The students will be familiar with the main acquisition and processing tools and will be able to use them critically, according to different protocols and physiological/clinical needs.

• Knowledge and understanding: The students will acquire the theoretical tools to process signals and images with specific application to the problem of assessing the functionality of the central nervous system.
• Apply knowledge and understanding: The students will apply the tools for signal and image processing to real EEG, fMRI and fNIRS datasets.
• Making Judgements: The students will be able to judge the quality of an acquired dataset, to select the proper pipelines for signal and images analysis, and to critically evaluate the results of the processing.
• Lifelong learning skills: The students will face the key difference between theoretical models and practical implementation, as well as the ability of problem solving, team-working and workflow organization. The students will be invited to discuss scientific articles in the classroom, discussing their objectives, motivating the methodology and critically analyzing the results obtained. The students will be required to judge the plausibility of their own presentation as well as, in common discussions, of the ones of other teams.

Main processing methodologies for EEG signal (denoising, features in time, frequency and time-frequency domains; steady state and event-related responses; multichannel analysis; connectivity; source estimation). Main processing modalities for fNIRS signal (continuous wave and time domain modalities, photon sensitivity maps, estimation of oxygenated and de-oxygenated hemoglobin). Main processing modalities for fMRI analysis (analysis of the BOLD signal in resting state and event related). Examples of multimodal integration (EEG + fNIRS, EEG + fMRI).

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
The evaluation will be based on the creation and presentation of group project. The student has to demonstrate:
knowledge on theoretical tools to assess functional evaluation of the central nervous system.
Ability in using and managing EEG, fMRI, and fNIRS datasets.
Ability in processing EEG, fMRI and fNIRS datasets, and in critically discussing the results obtained.

The course aims to provide a general introduction to the computational methods which can be used to model the behaviour of molecular systems and materials. The lectures will introduce the theoretical concepts, first in qualitative terms and then in the form of equations and algorithms. The practical hand-on computational sessions will introduce the students to the usage of freely available programs for molecular simulation and visualization. The students will become aware of the meaning of "modelling", "simulation" and "computing", in a way which may be useful also in other, unrelated contexts.

The students will learn some theoretical principles underlying current molecular modelling methods. This will allow them to read an understand the scientific literature emplying these methods. (Dublin descriptor 1: Knowledge and insight).
They will learn how to use some common codes for the calculation and interpretation of the properties of molecules, liquids and materials, and specifically: VMD (visualization), ORCA (quantum chemistry), Tinker and LAMMPS (molecular dynamics and Monte Carlo). They will also learn some basics of scientific data analysis, using Python-based tools: Jupyter notebooks, numpy, scipy, matplotlib. (Dublin descriptor 2: Applying knowledge and insight).
They will be expected to gain some critical understanding of the methods, in order to choose the right method for a specific problem and assess the results of their calculations. (Dublin descriptor 3: Judgement).
The students will be expected to present the results of a computational project, both as a written report and during a subsequent oral discussion. (Dublin descriptor 4: Communication).

Introduction to molecular modelling: overview of problems and approaches.
Elements of molecular quantum mechanics: Schroedinger equation, Born-Oppenheimer approximation, electronic wavefunctions, independent-particle model, molecular orbital (MO) theory, basis sets. Molecular properties: the electron density and the molecular electrostatic potential. Electron correlation and the bond dissociation problem. Concepts from density functional theory (DFT): Hohenberg-Kohn theorem, Kohn-Sham scheme, exchange-correlation functionals. Semiempirical molecular orbital methods.
Empirical models: force fields for organic and biological molecules, shell model for ionic polarization, the embedded-atom model for metals, empirical "reactive" potentials for semiconductors and carbon-based systems. Coarse-grained models.
Exploring potential energy surfaces: stationary states, energy minimization and saddle point location methods. Conformations of large molecules.
Introduction to simulation methods: elements of statistical mechanics, ensembles and calculation of thermodynamic properties, boundary conditions, treatment of short- and long-range interactions.
Molecular dynamics (MD) simulations: integration algorithms, introduction of constraints. Choice of initial conditions. Controlling temperature and pressure in MD. Analysis of trajectories: pair distribution functions, analysis of molecular conformations, time correlation functions. Evaluation of transport properties. Non-equilibrium methods. Enhanced sampling for "rare" events.
Monte Carlo (MC) simulations: general principles (Markov processes, transition probabilities, detailed balance) and Metropolis Monte Carlo for the NVT ensemble. Generalization to other ensembles. Example: the Ising model.
Application examples:

• calculation of molecular energies, geometries, vibrational spectra, electron densities, interactions, etc.
• structure and properties of solids, liquids, soft materials, interfaces, etc.

Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The students will be evaluated on the basis of written reports on the practical modeling problems, followed by an oral examination covering the reports and the theoretical concepts underlying the empoyed methods. Projects can be carried out individually or in groups of two.
The final grade will be based on:

• understanding of the theoretical principles of the chosen computational model(s) (Dublin descriptor 1: knowledge and insight).
• capability to run the numerical simulation codes, extract and analyze the results from them (Dublin descriptor 2: Applying knowledge and insight).
• soundness of the computational method(s) for a specific problem, capability to interpret the results and identify possible problems or limitations (Dublin Descriptor 3: Judgement).
• presentation of the results, both in writing (report) and orally (final discussion) (Dublin Descriptor 4: Communication).

Frank Jensen, Introduction to computational chemistry (3rd. edition), Editore: JohnWiley & Sons, Ltd, Anno edizione: 2017, ISBN: 9781118825990
M. P. Allen and D. J. Tildesley, Computer Simulation of Liquids, 2nd edition, Editore: Oxford University Press, Anno edizione: 2017, ISBN: 9780198803201
Andrew R. Leach, Molecular Modelling: Principles and Applications (2nd ed.), Editore: Prentice Hall, Anno edizione: 2001, ISBN: 0582382106
Peter Atkins, Julio de Paula, ATKINS' PHYSICAL CHEMISTRY, Editore: W. H. Freeman and Company, New York, Anno edizione: 2006, ISBN: 0-7167-8759-8

This course aims to equip students with the essential engineering methods needed for the design, fabrication, and testing of MEMS (Micro-Electro-Mechanical Systems) capacitive sensors and actuators. The primary focus will be on practical, hands-on laboratory activities conducted at PoliFAB, the cleanroom facility of Politecnico di Milano. Through a combination of lectures, exercise sessions, and project laboratory work, students will gain a thorough understanding of MEMS fabrication technologies and the skills required to create and evaluate MEMS devices.

Lectures and Exercise Sessions will allow students to:
develop a comprehensive understanding of the current status and advancements in MEMS fabrication technology. (Dd1)
learn the detailed fabrication steps necessary to realize MEMS capacitive devices, including various lithographic and etching techniques.
acquire skills in different techniques to electrically characterize MEMS devices using laboratory equipment such as lock-in amplifiers.
Project Laboratory will allow students to:
design MEMS devices using both analytical and numerical tools, applying theoretical knowledge to practical design problems.
learn to create layouts for MEMS fabrication, ensuring alignment with the fabrication steps covered in lectures.
directly engage in the realization of MEMS devices within the cleanroom, utilizing various fabrication techniques such as photolithography, chemical etching, reactive ion etching, and vapor etching (depending on tools availability).
become proficient in using instruments like the lock-in amplifier to measure and analyze the amplitude and phase response of MEMS devices.
use software tools such as MATLAB and Python to fit experimental results and interpret the performance of MEMS devices.
By the end of the course, students will have mastered the fundamental engineering techniques required for the successful design, fabrication, and testing of MEMS capacitive sensors and actuators, preparing them for advanced research or professional practice in the field of MEMS technology.

1. MEMS Basics:
Overview of MEMS Technology: Introduction to MEMS technology, covering the fundamental principles and historical development.
MEMS Sensors and Actuators: overview of various types of MEMS sensors and actuators, including their working principles and applications.
2. Fabrication of MEMS Devices:
Tools and Processes: Comprehensive coverage of the necessary tools and processes used for the fabrication of MEMS devices at PoliFAB, including photolithography, etching techniques, and deposition methods.
Impact of Non-Idealities: Analysis of how fabrication non-idealities can affect the performance of the final MEMS device and strategies to mitigate these issues.
3. MEMS Design and Simulation:
Analytical Modeling: Development of analytical models to describe the main electromechanical parameters of MEMS devices.
Multiphysics Simulation: Use of Multiphysics software for the simulation of MEMS devices in order to validate the main electromecanical parameters.
Design Constraints: Exploration of the relationship between fabrication constraints and design limitations, emphasizing the importance of integrated design and fabrication considerations.
4. Characterization of MEMS Devices:
Lock-in Amplifier: Instruction on using lock-in amplifiers to extract the amplitude and phase response of MEMS devices.
Electromechanical Parameter Fitting: Techniques for fitting the main electromechanical parameters of MEMS devices from experimental data, enhancing the understanding of device behavior.
Students will learn these topics through a combination of traditional lectures, interactive exercise sessions, hands-on laboratory activities, and independent work using tools provided during laboratory sessions. The course emphasizes experiential learning, ensuring that theoretical knowledge is reinforced through practical application.

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
Project Report (Group Work):
Analytical and Numerical Simulation: Detailed simulation of the target MEMS device.
Fabrication Flow Report: Comprehensive report on the fabrication process of the device, highlighting potential sources of non-idealities and their impact.
Electrical Characterization and Analysis: Detailed electrical characterization of the device and analysis of the main electromechanical parameters.
Oral Exam:
Theoretical Knowledge: Examination on the theoretical aspects covered in the course.
Project Report Discussion: In-depth discussion of the project report, including questions about the analytical, fabrication, and characterization processes.

The course is focused on the extraordinary physical properties of graphene and related layered 2D materials (e.g., MoS 2, WSe 2, hBN) and their application in nanoelectronics. The main goal of the course is to provide a comprehensive understanding of the physics of the state-of-the-art 2D nanoelectronic devices and realistically evaluate possible applications of atomically thin materials in modern electronics, especially considering a recent transition of the semiconductor industry to ultra-thin transistor channels.

Knowledge and understanding
The students will learn about the main physical properties of graphene and related 2D materials and how they can be exploited to realize 2D nanoelectronic devices.
Apply knowledge and understanding
The students will be able to extract important parameters and figures of merit of field-effect transistors (FETs) based on graphene and related 2D materials, both in DC and at high-frequencies.
The students will be able to evaluate the performance of the FETs made of 2D and conventional semiconductor materials.
The students will be able to understand advantages and disadvantages of 2D FETs with respect to conventional Si transistor technology.
Making judgements
The students will have the ability to compare different transistor technologies in terms of their applications in nanoelectronics.
The students will have the ability to select appropriate applications in electronics for FETs based on graphene and related 2D materials.
Lifelong learning skills
The students will be prepared for the incoming transistion of the semiconductor industry to novel device architectures and low-dimensional materials.
The students will be capable to autonomously learn how to use novel 2D materials in the development of transistors and electronic circuits.
The students will be prepared to begin a masters' thesis project in a semiconductor research laboratory.

Introduction to graphene and related 2D materials
Allotropes and hybridization of carbon
Band structure and massless Dirac fermions in graphene
Origin of high carrier mobility in graphene
Comparison with carbon nanotubes
Atomically thin semiconducting and insulating 2D materials
Relativistic phenomena in graphene and related 2D materials
Methods for the synthesis of graphene and related 2D materials
Assembly of artificial 2D van der Waals heterostructures
Graphene field-effect transistors (FETs)
The importance of high carrier mobility in electronic devices and circuits
Physics of graphene devices
Comparison between graphene and conventional FETs
Ambipolarity of graphene FETs
Electrostatic doping in graphene electronic circuits
Importance of drain current saturation and voltage gain
Impact of contact resistance on the properties of graphene FETs
Scaling and short-channel effects
Impact of band gap opening in graphene on properties of graphene FETs
2D transition metal dichalcogenides (TMDCs)
Crystalline structure of 2D TMDCs (MoS 2 and WSe 2 )
Band structure of 2D TMDCs
Electronic properties of 2D TMDCs
FETs based on 2D TMDCs
Short-channel effects and ballistic transport in 2D MoS 2 FETs
Small-signal model of FETs based on graphene and related 2D materials
Low-frequency AC small signal model of 2D FETs (hybrid-pi model)
Transconductance g m and output conductance g d
Definition of voltage and current gains
Intrinsic voltage gain g m / g d and intrinsic current gain h 21
Extrinsic high-frequency model of 2D FETs
Phasors in electronic circuits
Two-port networks
Parameter models ( h and Y ) of two-port networks
High-frequency figures of merit of FETs based on graphene and related 2D materials
Intrinsic and extrinsic cutoff frequency f T of 2D FETs
Comparison of 2D and conventional FETs in terms of f T
Power gain
Intrinsic and extrinsic maximum frequency of oscillation f max of 2D FETs
Comparison of 2D and conventional FETs in terms of f max
Scattering parameters of high-frequency (e.g., 5G) networks
Nanoelectronic circuits based on graphene and related 2D materials
Moore’s law
Static and dynamic power dissipation
Digital and analogue 2D electronic circuits: amplifiers, mixers, frequency multipliers, logic gates
Flexible electronics based on graphene and related 2D materials
Nanofabrication of 2D nanoelectronic devices and circuits

Prova scritta obbligatoria, con prove in itinere
Prova orale facoltativa (a scelta dello studente)
The students will be evaluated by written examination according to the calendar of the course.
The students will be expected to discuss the physical phenomena underlying applications of graphene and related 2D materials in electronics, solve very basic (i.e., single-transistor) electronic circuits with 2D FETs, extract transistor parameters and determine corresponding figures of merit.

Luis E. F. Foa Torres, Stephan Roche, Jean-Christophe Charlier, Introduction to Graphene-Based Nanomaterials - From Electronic Structure to Quantum Transport, Anno edizione: 2014, ISBN: 9781107030831
Juin J. Liou, Frank Schwierz, Hei Wong, Nanometer CMOS, Anno edizione: 2010, ISBN: 9814241083

The aim of this course is to present the fundamentals of micro and nanomagnetism necessary to understand the recent developments in the field of spintronics, thanks to a mix of lectures, exercices, informatic and experimental laboratories. In particular, a platform for micromagnetic simulation (OOMMF/MUMAX) will be presented, in order to provide the students with the essential tools for designing and analysing magnetic nano-devices. Experiments dealing with the characterization of magnetic materials and spintronic devices will be also carried pout with the participation of students. Finally, the lectures of the last part of the course are intended to review the recent advances in the field.

Knowledge and understanding
Upon passing the exam, the student:

• knows the principles of micromagnetism
• knows the basic analytical and computational approaches to calculate the micromagnetic configuration of a system
• knows the foundations of spintronics
• understands the structure and behaviour of conventional spintronic devices
• understands a scientific paper dealing with nanomagnetism and spintronics
  Applying knowledge and understanding
  Upon passing the exam, the student:
• is able to solve analytical problems connected to magnetism and spintronics
• is able to use a micromagnetic software (OOMMF) for the computation of static and dynamic micromagnetic configurations
• is able to design the basic structure of a spintronic device
  Making judgements, Communication, Lifelong learning skills
  Students who decide to prepare a project on micromagnetic simulation, as alternative to the written test:
• are able to model a real micromagnetic problem, identifying the relevant parameters and factors to be taken into account
• are able to solve a realistic problem and draw a general conclusion on the optimization work carried out via simulations
• are able to present their work in a report mimicking a scientific paper
• are able to communicate their work during the oral examination

Nanomagentism is a modern discipline devoted to the study of magnetism in nanoscale objects. The "nano-world" opens unforeseen possibilities to develop new devices and paradigms exploiting the spin and orbital angular momentum of electrons and other quasi-particles (e.g. domain walls, magnons) propagating in engineered magnetic structures. Spintronics, in particular, is a branch of nanoelectronics aiming at developing new electronic devices taking advantage of the spin degree of freedom in addition to the charge of carriers.
The program will consist of lectures and exercises devoted to these topics:
Micro and NANO magnetism
Demagnetizing field, magnetostatic energy. Landau magnetic free energy and its contributions (exchange, anisotropies, magnetostrictions). Domain walls. Micromagnetic simulations (OOMMF). Coherent magnetization reversal (Stoner Wohlfart model) and reversal via propagation of domain walls. Magnetic nanoparticles. Domain wall conduits. Magnetic coupling in multilayers (Néel coupling, Exchange bias, Bilinear coupling)
Classical-Spintronics
Two currents model and spin dependent scattering. Giant magnetoresistance in CIP and CPP configurations. Spin accumulation and Valet-Fert model. Tunneling magnetoresistance and magnetic tunneling junctions. Non volatile magnetic memories (MRAMs) and magnetic sensors. Spin transfer torque. Magneto-electric coupling. Spin injection, manipulation and detection in semiconductors.
Spinorbitronics
Rashba based devices and Spin-FET. Spin currents. Direct and inverse spin Hall effect. Antiferromagnet spintronics.
LABORATORY ACTIVITIES
Laboratory instruction in specific techniques of magnetic characterization of materials and devices will be provided, at the laboratory of Nanomagnetism located within the facility Polifab. Informatic laboratories connected to the usage of the micromagnetic platforms OOMMF/MUMAX are also carried out woth the help of a tutor who will also assist students in the preparation of a project which can replace the written test.

Prova scritta facoltativa (a scelta dello studente), senza prove in itinere
Prova orale obbligatoria
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The examimation is made of two parts: (i) a written test (1 hour) with some exercises, (ii) an oral examination. The written test can be replaced with a project on the micromagnetic simulation and investigation of selected magnetic structures.

S. Blundell, Magnetism in Condensed Matter, Editore: Oxford Master Series in Physics, Anno edizione: 2001, ISBN: 978-0-19-850591-4
Note: Good textbook on the fundamentals of magnetism and micromagnetisms
Amikam Aharoni, Introduction to the Theory of Ferromagnetism, Editore: Oxford University Press, Anno edizione: 2001, ISBN: 0198508093
Note: Reference text for micromagnetism
Supriyo Bandyopadhyay, Marc Cahay, Introduction to SPINTRONICS, Editore: CRC Press, Anno edizione: 2015, ISBN: 9781482255591
Note: Reference text for Spintronics
J. M. D. Coey, Magnetism and Magnetic Materials, Editore: Cambridge, Anno edizione: 2010, ISBN: 9780521816144
Note: Handbook of magnetism

Introduce the student to the fundamentals of optical imaging from the cellular to the tissue level, presenting the physical basis, discussing instrumental implementations and describing examples of applications, with particular emphasis on biophysics and biology topics.

• Knowledge and understanding
  The students will learn the fundamentals of bio-photonics imaging technologies, that operate from the molecular level up to the tissue scale. The student will learn the major applications of optical microscopy in biology and in clinical diagnostics.
• Apply knowledge and understanding
  The students will be able to design autonomously an optical system for microscopy and imaging. The students will be able to evaluate the resolution and contrast in optical microscopy and nanoscopy systems.
• Making Judgements
  Given a specific optical imaging system, the students will have the ability to analyze the point spread function and compare the performances of different optical setups.
• Lifelong learning skills
  Students will be capable to understand how to take into account, and how to overcome the diffraction limit in an optical system, with particular emphasis on optical microscopy. Students will be capable to learn autonomously how to use an optical microscope and how to assess its performances.

Optical microscopy and nanoscopy:
Propagation of light in an optical microscope: point spread function in an optical microscope and 3D imaging theory.
Classical microscopy techniques for cellular and tissue imaging: bright-field, darkfield, phase contrast, fluorescence.
Three-dimensional imaging of tissues: confocal microscopy, structured illumination microscopy, 2-photon microscopy.
Three-dimensional imaging with interferometric techniques and imaging of the eye: Optical Coherence Tomography.
Imaging at the molecular level with optical microscopy, circumventing the resolution: Single Molecule Localization and Stimulated Emission Depletion Microscopy.
Applications of microscopy and super-resolution microscopy in cellular imaging, biophysics and biomedicine.

Prova scritta obbligatoria, senza prove in itinere
The final assessment will be based on a written test with open questions on the topics of the course.

Jerome, Introduction to Optical Microscopy, Editore: Cambridge University Press, Anno edizione: 2019, ISBN: 9781108552660

The course aims at providing the concepts required for the description and characterization of molecular materials. This is essential since most applications of molecular materials in nanotechnology are based upon the specific features that emerge “at the bottom”. The course introduces the fundamental principles underlying the experimental techniques commonly employed for the characterization of molecular materials and discusses how these methods provide insight into their physical and chemical properties. The course will progressively explore molecular materials across multiple length scales, from collective molecular assemblies to the properties and behavior of individual molecules. Throughout the course, the concepts introduced will be discussed in the context of selected classes of molecular materials, emphasizing their relevance for nanotechnology applications.

The student is expected to acquire the fundamental concepts underlying the physical properties of molecular materials, with particular emphasis on their interaction with light (DdD1). The student will develop a solid understanding of the principles of spectroscopic and electronic techniques most widely used for the characterization of molecular materials and will be able to interpret the information provided by these methods (DdD1–DdD2). Through the analysis of experimental data, the student will gain the skills required to characterize molecular materials and to relate spectroscopic observations to their structural and functional properties (DdD2).

The course will introduce the fundamental physical concepts governing the electronic and optical pro perties of molecular materials. These concepts will then be applied to the interpretation of the main experimental techniques used for the characterization of molecular materials. The final part of the course will discuss selected classes of molecular materials, illustrating how characterization methods can be used to understand their properties and assess their suitability for applications in nanotechnology.
The course will cover the following topics:

• Fundamentals of molecular materials
  Energy levels and optical transitions in molecular materials, light-matter interaction and associated absorption and emission processes, relation between molecular structure and optical response.
• Characterization techniques for molecular materials.
  Introduction to the technique, interpretation of experimental data, and correlation with the physical properties of molecular materials.
  Techniques: absorption and fluorescence spectroscopies, scanning probe microscopies, electron microscopies, super-resolution and single-molecule techniques.
• Molecular materials for nanotechnology.
  Correlation between characterization results, structure, and functional performance.
  Bibliografia
  Risorsa bibliografica facoltativa Peter Atkins, Julio de Paula, Physical Chemistry, Editore: Oxford University Press, Anno edizione: 2006, ISBN: 9780198700722
  Note:
  Lecture notes and slides will be distributed during the course.

Prova orale obbligatoria
The examination will consist of an oral discussion on the main topics covered during the course. The examiner will typically propose two major topics, which will be discussed in detail following the structure and concepts presented during the lectures. The student is expected to describe and discuss the selected subjects clearly. Particular attention will be given to the ability to interpret spectroscopic and microscopic data, to identify the most appropriate characterization approach for a given material or application, and to establish connections between material properties and their use in nanotechnology.
The main Dublin descriptors tested during the exam are: Knowledge and understanding, Applying knowledge and understanding, Making judgements, Learning skills.

To give examples of application of ultrashort light pulses to the study of dynamical processes in physics, chemistry and biology.
To discuss real-world applications of ultrashort pulses to microscopy and material processing.
The acquired knowledge will enable the student to work in advanced research laboratories as well as in high-tech companies developing laser sources or using them for applications in micromachining, telecommunications, sensing or biomedicine.

At the end of the course the student will acquire knowledge and understanding of the underlying physics and the technical working principles of ultrashort pulse laser systems (DD1).
The student will be able to apply this knowledge and understanding to different scientific and industrial systems using ultrashort pulse lasers (DD2).
The student will be able to make judgements on the design of such systems for applications to spectroscopy, imaging or material processing (DD3).
The student will acquire learning skills that will enable her/him to read andvanced, graduate-level textbooks on nonlinear optics and photonics as well as papers on peer-reviewed scientific journals (DD5).

Ultrafast spectroscopy
introduction to ultrafast spectroscopy techniques;
degenerate, two-colour and broadband pump-probe;
density matrix formalism for the description of light-matter interaction;
four-wave mixing and photon echo, 2D spectroscopy;
ultrafast processes in metals and semiconductors;
applications to femtochemistry and femtobiology.
Real-world applications of ultrashort pulses
Coherent Raman microscopy: Coherent Anti-Stokes Raman Scattering (CARS), Stimulated Raman Scattering (SRS)
Nonlinear microscopy techniques: second-harmonic and third-harmonic generation microscopy, multi-photon microscopy
Material processing with ultrashort laser pulses: high precision cutting and drilling
Micro- and nano-fabrication with ultrashort laser pulses: two-photon polymerization.
Large-scale laser facilities (free electron lasers…)

Prova scritta obbligatoria, senza prove in itinere
The evaluation will consist of a written exam with questions and/or problems covering the topics of the lectures.
The exam will test the depth of understanding by the student of the handled topics, the developed critical skills and the capabilities to effectively organize the presented material, using a proper lexicon and efficiency and linearity of the discussion.

Andrew M. Weiner, Ultrafast Optics, Editore: Wiley, Anno edizione: 2009, ISBN: 978-0-471-41539-8
Sandro De Silvestri, Rocio Borrego Varillas, Aurelio Oriana, Cristian Manzoni, Giulio Cerullo, Tunable few-optical cycle pulses and advanced ultrafast spectroscopic techniques, Editore: Societa Italiana di Fisica, Anno edizione: 2018, ISSN: 0393697X, Fascicolo: 41 http://dx.doi.org/10.1393/ncr/i2017-10143-7

Photonics is a pervasive technology and the development of advanced optical materials represents an active and vibrant field of research, at the boundary between condensed matter and photonics, with impact on many industrial sectors. In this course, students will develop the capacity to critically assess the optical properties of materials and their perspective use as building blocks for optical elements and photonic devices, supported by the knowledge that they will also develop on the techniques that can be used to grow and characterize such materials.

The goal of the course is to provide students with:
The tools for the analysis of the optical properties of materials, as described by their dielectric function, and the knowledge about the techniques that can be used to grow and characterize them (Dublin Descriptor 1, Knowledge and understanding);
The ability to apply the acquired knowkedge to the analysis of experimental data regarding the optical and structural properties of optical materials (Dublin Descriptor 2, Applying knowledge and understanding);
The ability to critically assess (at least qualitatively) the optical and structural properties of a certain material and to choose the most appropriate optical material for a given application (Dublin Descriptor 2, Applying knowledge and understanding, and Dublin Descriptor 3, Making judgements).

1 - Optical response of materials
This module covers the physical description of the optical response of materials, focusing on the modeling of the dielectric function and on the different light-matter interactions that characterize different classes of materials

• Transmission, reflection, and absorption of light; Kramers-Kronig relations
• The Drude-Lorentz material response
• Dielectric materials: dielectric function, absorption processes and color centers, amorphous glasses and crystalline materials, birefringent/anisotropic materials
• Semiconductors: dielectric function, absorption and emission processes, doping
• Metals: dielectric function, absorption processes
• Polymeric materials for optics
  2 - Growth techniques
  This module covers the different technologies available for the growth of optical materials, highlighting the advantages, disadvantages, and fields of application of each individual technique.
• Sputtering
• Chemical vapor deposition & Atomic layer deposition
• Electron-beam evaporation
• Sol-gel techniques
• Polymeric materials
• Glass processes
  3 - Structural characterization
  This module covers the main and most widespread techniques that are used to assess the crystalline structure and the surface morphology of optical materials.
• X-ray diffraction
• Scanning electron microscopy
• Atomic force microscopy
  4 - Optical characterization
  This module covers the main and most widespread techniques that are used to quantitatively characterize the optical response of materials, with emphasis both on their experimental implementation and on the fundamental aspects of data analysis and modeling.
• Reflection/transmission spectroscopy
• Ellipsometry and other polarization-resolved techniques
• Optical microscopy
  5 - Engineering of the optical response
  This module represents a brief introduction on the effect of nanoscale structuring can affect the optical properties of materials and can be used as a degree of freedom to achieve specific optical functionalities
• Multilayer and nanostructured coatings
• Effective medium approximation
• Photonic crystals and metamaterials
• Structural colors
  6 - Optical elements
  In this module we will discuss typical applications of optical materials in the design and realization of optical elements and devices
• Reflective and refractive optical elements: lenses and mirrors
• Diffractive optical elements: gratings, beam shapers, beam splitters, diffusers, waveplates, polarizers
• Integrated photonic circuits
• Light modulation technologies: liquid crystals, electro-optic effect, acousto-optic effect, phase-change materials
• Materials for solar cells and photodetectors
• Lasing materials
  7 - International standards for the quantification of the optical properties of materials
  In this module we will briefly cover the content of international ISO&ASTM standards that include the quantitative definition of the optical properties of materials and the protocols for the respective evaluation

Prova orale obbligatoria
The final evaluation will be done with an oral examination in which the students will discuss specific topics of the course to address their acquired knowledge, their understanding of the content, and their ability to apply and critically assess them.

Mark Fox, Optical Properties of Solids: 2ND EDITION, Editore: Oxford University Press, Anno edizione: 2010
The whole content of the course will be covered by the class lectures with slides made available on the WeBee portal webeep.polimi.it

The course:

• Offers an introduction to the importance of symmetry in determining physical phenomena;
• Provides an algebraic frame for the description of symmetry;
• Describe how some of the physical properties of a classical or a quantum system can be deduced from its symmetry properties;
• Provides a means to derive the conserved quantities in classical or quantum systems.
• Offers a simple way to deduce selection rules for transitions in a quantum system.
• Provides a frame for the classification of solids and their electronic states.

Knowledge and understanding (DD1): the students will learn how to describe and classify symmetry in classical and quantum systems and derive physical properties directly from their symmetry properties.
Apply knowledge and understanding (DD2): the students will be able to apply the acquired knowledge to determine conservation laws, stationary states, and selection rules for transitions induced by external perturbations.
Making Judgements (DD3): given a specific problem and project cases, the students will have the ability to understand and formally describe how symmetry can determine some of the physical properties of the investigated system.
Lifelong learning skills (DD5): the students will understand how to take advantage of symmetry to deduce the laws governing classical or quantum systems that might be exploited for applications in a wide range of fields, form mechanics to spectroscopy, from crystallography to material science.

The algebra of symmetry
Discrete symmetry groups, classes, direct product between groups. Continuous symmetry groups and Lie groups, infinitesimal generators of a Lie group.
Analytical mechanics
Hamilton's variational principle, Lagrangian function, Euler-Jacobi equations, conjugated momenta. Hamiltonian function, Hamilton's equations of motion. Cyclic coordinates, Nother's theorem and conservation laws.
Recall of Quantum Mechanics
Schrödinger’s equation, observables, operators, eigenstates. Transformation properties of the Hamiltonian operator and conservation laws.
Theory of group representations and connection with quantum theory
Reducible and irreducible group representations, relation between the representations of the Hamiltonian symmetry group and its eigenvalue spectrum. Orthogonality theorems. Character of representations and character tables. Decomposition of reducible representations. Symmetry and the degeneracy of quantum states. Selection rules. Time-reversal symmetry and Kramer’s theorem.
Full rotation group
Orbital angular momentum. Spin and double groups. Wigner-Eckart theorem.
Symmetry in solids
Crystallography. Crystal systems and Bravais lattices. Point groups and Crystal classes. Space groups. Bloch’s theorem and the reciprocal lattice. Group of the crystal momentum. Classification of energy bands.

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
Written and oral evaluation aimed to probe the student preparation with respect to the pedagocical objectives outlined above:
Knowledge and understanding (DD1): the students should be able to describe and classify symmetry in classical and quantum systems and derive physical properties directly from their symmetry properties.
Apply knowledge and understanding (DD2): the students should be able to apply the acquired knowledge to determine conservation laws, stationary states, and selection rules for transitions induced by external perturbations.
Making Judgements (DD3): given a specific problem and project cases, the students should demonstrate their ability to understand and formally describe how symmetry can determine some of the physical properties of the investigated system.
Lifelong learning skills (DD5): the students should understand how to take advantage of symmetry to deduce the laws governing classical or quantum systems.

Michael Tinkham, Group Theory and Quantum Mechanics, Editore: Dover Publications, Anno edizione: 1964, ISBN: 978-0486432472
Charles P. Poole, John L. Safko, and Herbert Goldstein, Classical Mechanics, Editore: Perason, Anno edizione: 2014, ISBN: 978-0201657029

To provide the basic knowledge necessary for the understanding of the properties of ultrashort light pulses, their generation mechanisms, their propagation in linear and nonlinear media, their characterization techniques. The acquired knowledge will enable the student to work in advanced research laboratories as well as in high tech companies developing laser sources or using them for applications in micromachining, telecommunications, sensing or biomedicine.

At the end of the course the student will acquire knowledge and understanding of the underlying physics and the technical working principles of ultrashort pulse laser systems (DD1).
The student will be able to apply this knowledge and understanding to different scientific and industrial systems using ultrashort pulse lasers (DD2).
The student will be able to make judgements on the design of such systems for applications to spectroscopy, imaging or material processing (DD3).
The student will acquire learning skills that will enable her/him to read andvanced, graduate-level textbooks on nonlinear optics and photonics as well as papers on peer-reviewed scientific journals (DD5).

1. Properties of ultrashort laser pulses
introduction to ultrafast optics;
linear propagation equation for ultrashort pulses; dispersion and techniques for its compensation;
nonlinear ultrashort pulse propagation in second order media; three-fields coupled equations; second harmonic generation, sum and difference frequency generation;
nonlinear ultrashort pulse propagation in third order media; Kerr effect, nonlinear Schrödinger equation, solitons, self-phase-modulation;
ultrashort pulse characterization techniques: non-collinear and interferometric autocorrelation, FROG, SPIDER, 2DSI
2. Ultrashort pulse generation techniques
mode-locked lasers and passive mode-locking techniques;
ultrashort pulse amplification, chirped pulse amplification technique;
optical parametric amplifiers;
carrier-envelope phase and frequency combs.

Prova scritta obbligatoria, senza prove in itinere
The evaluation will consist of a written exam with questions and/or problems covering the topics of the lectures.
The exam will test the depth of understanding by the student of the handled topics, the developed critical skills and the capabilities to effectively organize the presented material, using a proper lexicon and efficiency and linearity of the discussion.

Andrew M. Weiner, Ultrafast Optics, Editore: Wiley, Anno edizione: 2009, ISBN: 978-0-471-41539-8
Sandro De Silvestri, Rocio Borrego Varillas, Aurelio Oriana, Cristian Manzoni, Giulio Cerullo, Tunable few-optical cycle pulses and advanced ultrafast spectroscopic techniques, Editore: Societa Italiana di Fisica, Anno edizione: 2018, ISSN: 0393697X, Fascicolo: 41 DOI: 10.1393/ncr/i2017-10143-7
Cristian Manzoni, Giulio Cerullo, Design criteria for ultrafast optical parametric amplifiers, Editore: Institute of Physics, Anno edizione: 2016 doi:10.1088/2040-8978/18/10/103501

The course ‘Quantum circuits and devices’ offers an overview of quantum hardware technology including qubits, quantum gates and full quantum processors. From the initial conceptualization of Richard Feynman in 1982, quantum computing has emerged in the last twenty years thanks to remarkable progress on both algorithms and qubit technology. Quantum computing can speed up the solution of hard problems, which are non-tractable by conventional computers, in various topics, such as cryptography, machine learning and the simulation of chemical molecules. Such a ‘quantum advantage’ has stimulated strong investments in research from major industries to explore the feasibility and the scaling of quantum computers. The course ‘Quantum circuits and devices’ will illustrate the state of the art of this ongoing technological race, addressing the main concepts, the opportunities and the challenges of quantum computing and providing the basic tools for understanding and designing quantum circuits.

Knowledge and understanding (Dublin Descriptor #1)
At the end of the course the students are expected to:
possess a good understanding of quantum computing in terms of basic concepts and main algorithms
possess a good understanding of qubits in terms of wavefunction, physical structure and operation including initialization, read and quantum gates
possess a basic knowledge of the main hardware implementation of qubits
possess a basic knowledge of quantum computer system architecture
Applying knowledge and understanding (Dublin Descriptor #2)
At the end of the course the students are expected to:
be able to analyze a quantum computer algorithm and architecture
be able to analyze a qubit structure and operation
be able to design an analogue control circuit for a qubit operation
Making judgements (Dublin Descriptor #3)
At the end of the course the students are expected to:
evaluate the most appropriate qubit technology for a set of application requirements
evaluate the most appropriate frequency and timing of operations for a given qubit technology

The course is organized in four phases. First, the basic principle of quantum computing will be explained. Then, the hardware implementation of quantum computing will be shown with reference to the two main quantum technologies, namely (i) the superconducting qubits and (ii) the spin qubits. Finally, the design, synthesis and operation of full quantum circuits will be described, including initialization, processing, read and quantum error correction. The following provides a detailed breakdown of the course topics:
Introduction (8 hours)
Qubits. Bloch sphere for representing the qubit state. Single qubit and multiple qubits. Superposition and entanglement. Relaxation and dephasing times. Quantum gates: NOT, Pauli, Hadamard, CNOT, CPHASE and SWAP. Universal gates and no-cloning theorem. Two-level systems: Hamiltonian, time evolution and Rabi oscillations. Avoided crossing. Quantum circuits: initialization, control, coupling and readout. DiVincenzo criteria for quantum circuits.
Semiconductor spin qubits (8 hours)
Quantum dots and donor impurities. Electron spin, Larmor precession, Zeeman effect and electrical tuning of the spin splitting. Spin-charge conversion for initialization and readout. Quantum point contacts and single-electron transistors for spin-charge conversion. Coulomb blockade. Electron spin resonance (ESR) and Rabi oscillations. Microwave transmission lines. Quantum gates for single and two qubits. Dephasing effects and silicon isotopic purification. Design/simulation tools. Technology state of the art and hardware demonstrators.
Superconducting qubits (8 hours)
Superconductivity: Bardeen-Cooper-Schrieffer (BCS) theory, Cooper-pair, superconductive energy gap. Flux quantization, Cooper pair box, superconducting quantum-interference device (SQUID). LC quantum oscillators. Circuit quantum electrodynamics. Josephson junction, effective inductance and Josephson relationships. Phase qubit, flux qubit, charge qubit and transmon. Quantum gates for single and two qubits. Superconducting qubit circuits: Initialization, readout and calibration. Flux line, drive line and read resonator. Design/simulation tools and hardware demonstrators.
Hardware quantum circuits (6 hours)
Quantum circuit design and synthesis: Clifford + T universal gate, summation and multiplication. Quantum fidelity and error correction. Randomized benchmarking and gate set tomography. Status of QC hardware and major developers. Alternative approaches: topological qubits, trapped ion qubits, trapped neutral atoms and adiabatic quantum computing. Cryogenic systems and CMOS electronic control.
Exercises (10 hours)
Numerical exercises will be illustrated on the following topics: quantum gates, quantum algorithms, spin qubits (Coulomb blockade, single electron transistor), superconducting qubits (Josephson junctions, phase/flux/charge qubits, transmons), analog control circuits.
Laboratory (10 hours)
Numerical laboratory sessions will be carried out on the design and simulation of superconducting quantum circuits with IBM’s Qiskit Metal ( https://qiskit.org/metal/ ). Experimental laboratory sessions will also be carried out remotely aimed at understanding the calibration and control of superconducting qubits.

Prova scritta obbligatoria, senza prove in itinere
The assessment will rely on a written exam with problems and questions covering the whole course, including theoretical lectures, exercises and laboratory experiments. The number of problems may vary from 5 to 10, with each answer contributing to the overall score with a specific weight, that will be shown in the text.

Ray LaPierre, Introduction to Quantum Computing, Editore: Springer Nature Switzerland AG, Anno edizione: 2021, ISBN: 978-3-030-69317-6
Daniel D. Stancil and Gregory T. Byrd, Principles of Superconducting Quantum Computers, Editore: Wiley, Anno edizione: 2022, ISBN: 9781119750727

Provide a basic understanding of the principles of quantum computing. Provide the notions required to understand if solving a problem may profit from quantum computing. Enable a hands-on experience on existing quantum computer simulators, to allow further experimentation by the candidate.

As modern technology moves ever further into the nanoscale, it becomes possible to realistically consider the development of quantum computing based on solid state technology. The course on Semiconductor Qubits will develop tools to understand charge transport in high mobility two-dimensional (2D) systems, such as semiconductor quantum wells and sheets of 2D materials, based on the behaviour of electrons in solids in electric and magnetic fields. By electrostatically confining the electrons into quantum dots, their quantum states can be manipulated and measured. The study of the rich physics seen in these systems at low temperatures and high magnetic fields allows us to design qubits and carry out quantum computations, on the way to the near-future development of quantum technology.

• Knowledge and understanding
  The students will learn about the main physical phenomena relevant to low-dimensional semiconductor structures and devices.
  The students will study the electronic properties of semiconductor materials, with particular emphasis on silicon, germanium, and the SiGe alloy system.
  The students will learn the formalism of single and multiple qubit state representation and manipulations.
• Apply knowledge and understanding
  The students will learn how to recognise relevant physical phenomena in terms of their manifestation in experimental results.
  The students will understand how to extract important parameters and figures of merit from experimental transport data.
  The students will understand how the physical realization of qubits in semiconductors relies on their electronic properties.
• Making judgements
  The students will understand how to design experiments which can measure relevant parameters and figures of merit.
  The students will understand the merits and drawbacks of different physical qubit systems.
  The students will understand how to design quantum circuits to carry out simple operations and algorithms.
• Lifelong learning skills
  The students will further develop some of the concepts introduced in the first part of the integrated course.
  The students will be prepared to begin a masters' thesis project in a research laboratory working on semiconductors and/or semiconductor-based qubits.
  The students will be introduced to the extremely relevant current topic of quantum computing.

Quantum transport:
2-dimensional carrier gases: high-mobility 2DCG in a semiconductor quantum well.
Linear transport theory and Boltzmann equation: scattering mechanisms and screening, quantum and transport relaxation lifetimes, electrical conductivity and Hall effect.
M agnetic fields at low temperature: Landau levels, Shubnikov-de Haas oscillations, and the quantum Hall effect. Weak localization and spin-orbit coupling.
Split gate quantum dots:
Electrostatically confined quantum dots: definition, quasi-bound energy levels.
Quantized conductance: Coloumb blockade, tunneling.
Coupled quantum dots: charge sensing, spin-to-charge conversion.
Qubits in a semiconductor quantum well: physical realization, properties.
Q ubits:
Qubit formalism: quantum mechanics of qubit states, single qubit operations.
Multiple qubit states: separable states, entanglement.
Physical qubits: qubits in different physical systems, qubit operations, entanglement.
Quantum computing:
Quantum circuits: programming a quantum computer.
Algorithms: Bernstein-Vazirani for interrogating a hidden function, quantum Fourier transform, Shor’s factoring algorithm, Grover’s search algorithm, Quantum phase estimation.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
The student will be evaluated by written and oral examinations according to the calendar of the course.
The student will be expected to discuss the physical phenomena which give rise to certain results or experimental effects, and what information regarding the characterization of the sample or device under test can be extracted from the experimental results. Emphasis is placed on the comprehension of real experimental data rather than calculation of simplified systems or reproduction of text-book derivations.
Students will be expected to understand and discuss the physical implementation of qubits and quantum gate operations.
Students will be expected to understand and design simple quantum circuits, discuss the advantages and disadvantages of quantum computation, and discuss the operation of the more well-known algorithms.

J. H. Davies, The Physics of Low-Dimensional Semiconductors, Editore: Cambridge University Press, Anno edizione: 1998
Thomas Ihn, Semiconductor Nanostructures: Quantum States and Electronic Transport, Editore: Oxford University Press, Anno edizione: 2010
Neil W. Ashcroft and N. David Mermin, Solid State Physics, Editore: Thomson Learning

This course provides an introductory overview on the basic principles of quantum optics, their use to understand the fundamental mechanisms of light-matter interaction and the possibility to exploit these concepts to manipulate information in a completely new way as in quantum communication and quantum computation protocols. The course is conceived as a starting point for more advanced studies.

Knowledge and understanding (DD1)
Attending the lectures and passing the exam will allow the student to:

• U nderstand the physical basis of quantum optical phenomena;
• Learn the fundamental mechanisms governing light-matter interaction;
• Learn the principles and the main protocols of quantum information.
  Apply knowledge and understanding (DD2)
  The student will be able to apply the basic knowledge described above to:
• explain the operating principles of some prototypal optical devices developed for quantum information;
• deepen the understanding of physical phenomena encountered in other courses with a new perspective;
  Communication skills (DD4)
  The exam requests the students to communicate clearly what they have learnt, with rigorous argumentation.

Prelude to Quantum Optics
1. Introduction: theoretical and experimental foundations for the quantization of the electromagnetic field
2. Photodetection statistics: semiclassical model, violation of the semiclassical model by sub-Poissonian light
3. Formalism of quantum mechanics: axioms, bra-ket notation, matrix notation, Schroedinger and Heisenberg pictures
Quantization of the electromagnetic field
4. Quantization of the electromagnetic field: normal modes and normal variables for the classical electromagnetic field, postulate of the Hamiltonian of the field, Fock states and their properties
5. Quasi-classical states of the radiation field: definition and decomposition in the Fock basis, Glauber displacement operator, properties of quasi-classical states
Quantum interferometry
6. Quantum description of the beam splitter and simple notions of quantum interferometry
7. Squeezed states: definition and properties, the homodyne detection technique
Atoms and photons
8. The Jaynes-Cummings model for the interaction of a two-level atom with a quantized field: analysis of the uncoupled Hamiltonian, dressed states of the full Hamiltonian
9. Rabi oscillations induced by n photons, collapse and revival of Rabi oscillations induced by a coherent field
10. Single photon sources: single photon purity, indistinguishability, brightness; qualitative discussion of different types of single-photon sources
Quantum information
11. Single-qubit states and transformations, observables in the qubit space, no-cloning theorem
12. Quantum cryptography: elements of classical cryptography, the BB84 protocol for quantum key distribution
13. Entanglement and Bell states, Bell inequalities, the quantum teleportation protocol
14. Fundamentals of quantum computing: two qubit gates, quantum function evaluation, examples of quantum algorithms (Deutsch, Grover,...)

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The exam is in written form, and typically consists of 3 open questions, each on a different topic. An optional oral exam, requested by the student or by the teacher, may complete the evaluation.
The aim of the exam is to ascertain:

• the understanding of the physical basis of quantum optics and information (DD1);
• the knowledge of the definitions, theorems and general concepts related to quantum optics and quantum information (DD1);
• the capability to present and discuss in writings, both qualitatively and quantitatively, the quantum physical models and protocols relevant for the course, with clear and rigorous argumentations (DD2, DD4).

Chirstopher Gerry and Peter Knight, Introductory quantum optics, Editore: Cambridge University Press, Anno edizione: 2005, ISBN: 978-0-521-52735-4
Serge Haroche, Jean-Michel Raimond, Exploring the Quantum: Atoms, Cavities, and Photons, Editore: Oxford University Press, Anno edizione: 2006, ISBN: 9780198509141
Giuliano Benenti, Giulio Casati, Davide Rossini, Giuliano Strini, Principles of Quantum Computation and Information, Editore: World Scientific, Anno edizione: 2019, ISBN: 9789813279995
Mark Fox, Quantum optics: an introduction, Editore: Oxford University Press, Anno edizione: 2006, ISBN: 978-0-19-856673-1

This course explores some of the advanced technologies enabling development of modern quantum optics and physics. An introductory review of quantum theory provides the students with the necessary theoretical background. The following topics cover the tools and techniques that have been used for electromagnetic control of atomic motion and some relevant experimental applications.

Knowledge and understanding (DD1)
Attending the lectures and passing the exam will allow the student to:

• understand the physical basis of atom-light interactions at a quantum level;
• learn the fundamental tools and techniques for manipulation of atoms and molecules;
• discover the technology behind modern optical frequency standards;
  Applying knowledge and understanding (DD2)
  The student will be able to apply the basic knowledge described above to:
• explain the operating principles of some prototypal optical devices developed for quantum optics;
• design the basic layout of a laser cooling device and ions trap;
• transfer the physical concepts to applications requiring manipulation of atoms;
  Communication skills (DD4)
  The exam requests the students to communicate clearly what they have learnt, with rigorous argumentation.
  Lifelong learning skills (DD5)
  Students will be capable to autonomously follow with greater awareness the new trends in the high-tech market dealing with atom optics and quantum technologies (from quantum communications to quantum computing).

The course of "Atom Optics and Quantum Technologies" is the Part 2 of a larger integrated course called "Quantum Optics and Technologies"; Part 1 is called "Quantum Opics and Information". Lessons related to Part 1 and 2 are given in the first and second half of the semester, respectively. Hence, students registered only to the course of "Atom Optics and Quantum Technologies" will attend their lessons starting from the second half of the semester, just by the end of lessons related to Part 1. The following is the list of subjects covered by the course of "Atom Optics and Quantum Technologies".
Quantum theory of optical resonators and amplifiers
1. Linearized quantum noise description
2. Quantum equation of motion for the cavity mode
3. Optical amplifiers
4. Squeezed light generators
Quantum theory of atom-light interactions
5. Basic concepts and definitions
6. Optical Bloch equations
7. Force on two-level atoms
Optical cooling and trapping of atoms and molecules
8. General properties concerning laser cooling
9. Photon recoil and Doppler cooling
10. Optical molasses, sub-Doppler cooling
11. Cooling and trapping of ions
12. Molecular cooling: direct and indirect methods
Application
13. Atomic fountains clocks
14. Atom mirrors, atom laser
15. Optical tweezer
16. Zeeman slower
17. Magneto-optical trap (MOT)

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The exam is made of a test in written form. The test will will have a duration of 1.5 hours, and will typically consist of 3 open questions on different topics. An optional oral exam, requested by the student or by the teacher in specific cases, may complete the evaluation.
The aim of the exam is to ascertain:

• the understanding of the physical basis of quantum optics and information;
• the knowledge of the definitions, theorems and general concepts related to quantum optics and quantum information;
• the capability to discuss, both qualitatively and quantitatively, the quantum physical models and protocols relevant for the course.
• the comprehension of the experimental techniques for laser cooling and trapping of atoms and molecules
• the understanding of relevant applications of atom optics and quantum technologies
  Students can take the exam in the usual five dates along the year (two in summer session, one in autumn session and two in winter session). As the course of "Atom Optics and Quantum Technologies" is the Part 2 of a larger integrated course called "Quantum Optics and Technologies", each exam will provide students with the chance to give the test on Part 1 and/or Part 2, separately. To this end, each exam is scheduled with an overall duration of 3 hours and 15 minutes: it starts with 1.5 hours dedicated to Part 1, then 15 minutes resting, and finally 1.5 hours on Part 2. Students registered to the course of "Atom Optics and Quantum Technologies" can give their test in the second half of the exam after the resting.

Gilbert Grynberg, Alain Aspect, Claude Fabre, Introduction to Quantum Optics, Editore: Cambridge University Press
Hans-A. Bachor, Timothy C. Ralph, A guide to experiments in quantum optics, Editore: WileyVCH Verlag GmbH & Co. KGaA
Christopher J. Foot, Atomic Physics, Editore: Oxford Master Series in Physics
Wolfgang Demtröder, Laser Spectroscopy 1 - 2, Editore: Springer

The course provides a conceptual framework for understanding the Physics of low-dimensional semiconductor systems, where quantum confinement and strain effects are exploited for tailoring electronic and optical properties.
The physical properties of a variety of semiconducting materials, including compound semiconductors and heterostructures, will be analysed on the base of their electronic bandstructure outlining their potentiality in micro/opto-electronics, spintronics and quantum computing.
The effect of quantum confinement in 2-dimensional (quantum wells), 1-dimensional (quantum wires) 0-dimensional (quantum dots) heterostructures will be analyzed also in view of their application in intersubband photodetectors, optical modulators, quantum cascade lasers and split-gate quantum dots.
Strain effects on the bandstructure will be addressed with a focus on strained-Si technology and strain effects on lasing both in III-V and group IV semiconductors.

• Knowledge and understanding
  During the course students will learn the basic principle of tight-binding and k-dot-p bandstructure calculations,how to extract the main electronic and optical properties of Group IV, III-V and II-VI semiconductors from their bandstructure, understand the relevant effects of quantum confinement on the optical and electronic properties of semiconductors, understand the key elements of strain-engineering for bandstructure modification.
• Apply knowledge and understanding
  Numerical models implemented in Python and the analysis of the working principles of devices relying on semiconductor nanostructures will be used to apply in real case studies the fundamental physical properties of semiconductor nanostructures.
• Making Judgements
  The students will learn which physical properties (bandgaps, effective mass, optical bandgap, band-alignment, compressive or tensile strain) are more relevant for a given application such as high mobility transitors, lasers, quantum well infrared photodetectors, resonant tunnel diodes, quantum cascade lasers ans spin-qubits.
• Lifelong learning skills
  Students will gain a broad overview of the physical properties of semiconductor nanostructures and their application in microelectronics, photonics, spintronics and quantum computing.

Semiconductor bandstructures
Bandstructure of group IV and compound (III-V, II-VI) semiconductors. Tight binding model of the bandstructure: implementation in Python. The k-dot-p model: bandgap dependence of the effective mass. Symmetry of conduction (valence) band minima (maxima) and their effect on selection rules for optical absorption: optical spin orientation. The effective mass approximation and the effective mass tensor. Density of states effective mass and conductivity effective mass. Case study: cyclotron resonance measurements.
Semiconductors alloys
Semiconductor alloys: the virtual crystal approximation. Case studies using the tight-binding model implemented in Python. Case study: use of semiconductors alloys in multi-junction solar cells.
Quantum confinement in semiconductor heterostructures
Band‑offset in heterostructures, type I, II and III band‑alignment. Experimental determination of band‑offsets by X-ray photoelectron spectroscopy. Theoretical calculation of the band offset using the Jaros model. Case study: Ge-on-Si photodetectors for telecom applications.
Quantum confinement effects in semiconductor heterostructures. The Schrödinger equation for the envelope function: energy levels and density of states in 2D (quantum wells), 1D (quantum wires) and 0D (quantum dots). Case studies: optical modulators based on the quantum confined Stark effect, the quantum cascade laser, split-gate quantum dots and spin qubits.
Strain engineering
Lattice mismatch in heterostructures. Elastic strain in cubic semiconductors. Deformation potentials for hydrostatic and uniaxial strain. Strained silicon technology and strain effects on lasing in III-V and group IV semiconductors. Case studies: reduction of the lasing threshold in strained QW lasers, lasers based on group IV elements.

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
Students will be evaluated by written and oral examination.
The written exam conists of two exercises which must be solved, also numerically, by the student in approximately one hour.
During the oral exam the student is expected to discuss the physical phenomena which give rise to certain optical/electronic properties. Emphasis is placed on the comprehension of fundamental physical phenomena, also in simplified systems.
The final mark will be composed taking in to account the results of the written and oral exams.

J. Singh, Electronic and optoelectronic properties of semiconductor structures, Editore: Cambridge, ISBN: 0-521-03574-0
J. H. Davies, The physics of low-dimensional semiconductors, Editore: Cambridge, ISBN: 978-0-521-48491-6

As modern technology moves ever further into the nanoscale, it becomes possible to realistically consider the development of quantum computing based on solid state technology. The course on Semiconductor Qubits will develop tools to understand charge transport in high mobility two-dimensional (2D) systems, such as semiconductor quantum wells and sheets of 2D materials, based on the behaviour of electrons in solids in electric and magnetic fields. By electrostatically confining the electrons into quantum dots, their quantum states can be manipulated and measured. The study of the rich physics seen in these systems at low temperatures and high magnetic fields allows us to design qubits and carry out quantum computations, on the way to the near-future development of quantum technology.

• Knowledge and understanding
  The students will learn about the main physical phenomena relevant to low-dimensional semiconductor structures and devices.
  The students will study the electronic properties of semiconductor materials, with particular emphasis on silicon, germanium, and the SiGe alloy system.
  The students will learn the formalism of single and multiple qubit state representation and manipulations.
• Apply knowledge and understanding
  The students will learn how to recognise relevant physical phenomena in terms of their manifestation in experimental results.
  The students will understand how to extract important parameters and figures of merit from experimental transport data.
  The students will understand how the physical realization of qubits in semiconductors relies on their electronic properties.
• Making judgements
  The students will understand how to design experiments which can measure relevant parameters and figures of merit.
  The students will understand the merits and drawbacks of different physical qubit systems.
  The students will understand how to design quantum circuits to carry out simple operations and algorithms.
• Lifelong learning skills
  The students will further develop some of the concepts introduced in the first part of the integrated course.
  The students will be prepared to begin a masters' thesis project in a research laboratory working on semiconductors and/or semiconductor-based qubits.
  The students will be introduced to the extremely relevant current topic of quantum computing.

Quantum transport:
2-dimensional carrier gases: high-mobility 2DCG in a semiconductor quantum well.
Linear transport theory and Boltzmann equation: scattering mechanisms and screening, quantum and transport relaxation lifetimes, electrical conductivity and Hall effect.
M agnetic fields at low temperature: Landau levels, Shubnikov-de Haas oscillations, and the quantum Hall effect. Weak localization and spin-orbit coupling.
Split gate quantum dots:
Electrostatically confined quantum dots: definition, quasi-bound energy levels.
Quantized conductance: Coloumb blockade, tunneling.
Coupled quantum dots: charge sensing, spin-to-charge conversion.
Qubits in a semiconductor quantum well: physical realization, properties.
Q ubits:
Qubit formalism: quantum mechanics of qubit states, single qubit operations.
Multiple qubit states: separable states, entanglement.
Physical qubits: qubits in different physical systems, qubit operations, entanglement.
Quantum computing:
Quantum circuits: programming a quantum computer.
Algorithms: Bernstein-Vazirani for interrogating a hidden function, quantum Fourier transform, Shor’s factoring algorithm, Grover’s search algorithm, Quantum phase estimation.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
The student will be evaluated by written and oral examinations according to the calendar of the course.
The student will be expected to discuss the physical phenomena which give rise to certain results or experimental effects, and what information regarding the characterization of the sample or device under test can be extracted from the experimental results. Emphasis is placed on the comprehension of real experimental data rather than calculation of simplified systems or reproduction of text-book derivations.
Students will be expected to understand and discuss the physical implementation of qubits and quantum gate operations.
Students will be expected to understand and design simple quantum circuits, discuss the advantages and disadvantages of quantum computation, and discuss the operation of the more well-known algorithms.

J. H. Davies, The Physics of Low-Dimensional Semiconductors, Editore: Cambridge University Press, Anno edizione: 1998
Thomas Ihn, Semiconductor Nanostructures: Quantum States and Electronic Transport, Editore: Oxford University Press, Anno edizione: 2010
Neil W. Ashcroft and N. David Mermin, Solid State Physics, Editore: Thomson Learning

As modern technology moves ever further into the nanoscale, it becomes possible to realistically consider the development of quantum computing based on solid state technology. The course on Semiconductor Qubits will develop tools to understand charge transport in high mobility two-dimensional (2D) systems, such as semiconductor quantum wells and sheets of 2D materials, based on the behaviour of electrons in solids in electric and magnetic fields. By electrostatically confining the electrons into quantum dots, their quantum states can be manipulated and measured. The study of the rich physics seen in these systems at low temperatures and high magnetic fields allows us to design qubits and carry out quantum computations, on the way to the near-future development of quantum technology.

• Knowledge and understanding
  The students will learn about the main physical phenomena relevant to low-dimensional semiconductor structures and devices.
  The students will study the electronic properties of semiconductor materials, with particular emphasis on silicon, germanium, and the SiGe alloy system.
  The students will learn the formalism of single and multiple qubit state representation and manipulations.
• Apply knowledge and understanding
  The students will learn how to recognise relevant physical phenomena in terms of their manifestation in experimental results.
  The students will understand how to extract important parameters and figures of merit from experimental transport data.
  The students will understand how the physical realization of qubits in semiconductors relies on their electronic properties.
• Making judgements
  The students will understand how to design experiments which can measure relevant parameters and figures of merit.
  The students will understand the merits and drawbacks of different physical qubit systems.
  The students will understand how to design quantum circuits to carry out simple operations and algorithms.
• Lifelong learning skills
  The students will further develop some of the concepts introduced in the first part of the integrated course.
  The students will be prepared to begin a masters' thesis project in a research laboratory working on semiconductors and/or semiconductor-based qubits.
  The students will be introduced to the extremely relevant current topic of quantum computing.

Quantum transport:
2-dimensional carrier gases: high-mobility 2DCG in a semiconductor quantum well.
Linear transport theory and Boltzmann equation: scattering mechanisms and screening, quantum and transport relaxation lifetimes, electrical conductivity and Hall effect.
M agnetic fields at low temperature: Landau levels, Shubnikov-de Haas oscillations, and the quantum Hall effect. Weak localization and spin-orbit coupling.
Split gate quantum dots:
Electrostatically confined quantum dots: definition, quasi-bound energy levels.
Quantized conductance: Coloumb blockade, tunneling.
Coupled quantum dots: charge sensing, spin-to-charge conversion.
Qubits in a semiconductor quantum well: physical realization, properties.
Q ubits:
Qubit formalism: quantum mechanics of qubit states, single qubit operations.
Multiple qubit states: separable states, entanglement.
Physical qubits: qubits in different physical systems, qubit operations, entanglement.
Quantum computing:
Quantum circuits: programming a quantum computer.
Algorithms: Bernstein-Vazirani for interrogating a hidden function, quantum Fourier transform, Shor’s factoring algorithm, Grover’s search algorithm, Quantum phase estimation.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
The student will be evaluated by written and oral examinations according to the calendar of the course.
The student will be expected to discuss the physical phenomena which give rise to certain results or experimental effects, and what information regarding the characterization of the sample or device under test can be extracted from the experimental results. Emphasis is placed on the comprehension of real experimental data rather than calculation of simplified systems or reproduction of text-book derivations.
Students will be expected to understand and discuss the physical implementation of qubits and quantum gate operations.
Students will be expected to understand and design simple quantum circuits, discuss the advantages and disadvantages of quantum computation, and discuss the operation of the more well-known algorithms.

J. H. Davies, The Physics of Low-Dimensional Semiconductors, Editore: Cambridge University Press, Anno edizione: 1998
Thomas Ihn, Semiconductor Nanostructures: Quantum States and Electronic Transport, Editore: Oxford University Press, Anno edizione: 2010
Neil W. Ashcroft and N. David Mermin, Solid State Physics, Editore: Thomson Learning

Il corso "Accounting Finance & Control" (AFC) ha l'obiettivo di fornire i quadri teorici e gli strumenti per comprendere e gestire i "numeri" e le "informazioni chiave" per le imprese.
Il corso si propone di mettere i futuri ingegneri in grado di:
-Capire quali sono le informazioni e i numeri chiave per supportare le aziende nel perseguimento del valore aziendale.
-Interpretare la sostenibilità dell'impresa attraverso i trend economici e finanziari.

• Misurare, analizzare e gestire le performance aziendali e il contributo delle diverse aree organizzative.
  aree organizzative.
  -Analizzare i fabbisogni finanziari a fronte delle tendenze presenti e future (previsioni), valutando le alternative per coprire tali fabbisogni (capitale proprio e debito).
  copertura di tali fabbisogni (capitale proprio e debito).
  -Essere in grado di interagire, attraverso i numeri chiave, con gli stakeholder aziendali.

The course presents advanced methods for the analysis of dynamical systems and for the synthesis of multi-input, multi-output controllers.
In the first part, the concepts and tools include Lyapunov stability theory for nonlinear systems, small gain theorem for nonlinear systems, structural properties of linear dynamical systems (poles, invariant zeros, principal gains), performance and robustness indices and basic robust control concepts, state feedback via pole placement, output feedback via state observers.
In the second part, particular emphasis is given to Model Predictive Control (MPC) techniques to deal with systems operating under state/input constraints and possibly with nonlinearities and integer decision variables, focusing on formulations that are well adopted in real-world industrial solutions.

Knowledge and understanding (Dublin Descriptor 1, DD1)
Upon successful completion of the course, students will be able to:
apply Lyapunov stability theory to the analysis of nonlinear systems
evaluate the structural properties of linear dynamical systems, as well as the performance and robustness of linear control systems
understand how the regulator structure affects both static and dynamic performance
design multivariable control systems using pole placement, LQ, LQG
design dynamic, possibly reduced-order, observers via pole placement and Kalman filtering
formulate model predictive control problems in state-space form and using input–output models
Applying knowledge and understanding (DD2)
Through the exercise and lab sessions, students will learn to:
perform stability analysis of dynamical systems and design nonlinear controllers using Lyapunov stability theory
analyze multivariable linear systems in terms of reachability, observability, gain, singular values, poles, and zeros, and understand the proper formulation of control design objectives
select an appropriate regulator structure to achieve the desired control objectives
apply advanced control design methods (pole placement, LQ, LQG, and model predictive control) to multivariable systems and tune the associated parameters
design dynamic observers based on pole placement and Kalman filtering for disturbance estimation and output-feedback control
use software tools to simulate and analyze dynamical systems, design controllers using the methods covered in the course, evaluate closed-loop performance, and assess the sensitivity of the results to design choices and tuning parameters
Making judgements (DD3)
Upon successful completion of the course, students will be able to
carry out a preliminary system analysis, identifying the control and controlled variables and selecting an appropriate control structure
assess the limits of achievable performance and define suitable control specifications
choose the most appropriate control design methodology
critically evaluate the obtained results and their sensitivity to the adopted design choices
Communication skills (DD4)
Through software labs and optional project work, students will learn to communicate clearly, concisely, and effectively the control synthesis problem addressed, the chosen solution method, and the results obtained.
Learning skills (DD5)
Through lab exercises and optional project work, students will learn to tackle real-world problems – both independently and as part of a team – using the advanced control methods covered in the course.

Nonlinear systems: equilibria, stability, Lyapunov theorems. Synthesis of the control law with control Lyapunov functions and the backstepping procedure. Norms and gain of dynamical systems. Small gain theorem.
Multivariable feedback control systems: Structural properties: reachability, observability, poles, and invariant zeros. Nominal and robust stability of multivariable feedback systems. Static and dynamic performance. Schemes with integral action. Pole placement control of single-input/single-output and multivariable systems.
State and disturbance observers: State observers and dynamic output feedback. Disturbance estimation with state observers.
Model Predictive Control (MPC) for linear systems: Unconstrained optimal control, linear quadratic control, optimal filtering and LQG control. Constrained optimal control via MPC; moving horizon estimation.
Industrial formulations of MPC: soft constraints, impulse and step response models, move blocking and input parametrization.
Advanced MPC schemes: MPC for systems with nonlinear dynamics and with integer decision variables.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
The final assessment will be a written exam consisting of numerical exercises and theoretical questions.
During the exam the student must prove to be able to design a controller for multivariable systems, and in particular to:
perform a preliminary analysis, specifying the control and controlled variables and the adopted control structure (DD1, DD2)
evaluate the limits to the achievable performance, define the control specifications (DD3)
apply modern control design techniques (DD2)
critically evaluate the achieved results and their sensitivity to the adopted design choices (DD5)
describe the achieved results in a clear and convincing way (DD4)
At the instructor's discretion, students may be required to take an oral examination to confirm their knowledge, if deemed necessary

L. Magni, R. Scattolini, Advanced and Multivariable Control, Editore: Pitagora Editrice, Anno edizione: 2014
H. K. Khalil, Nonlinear Systems, Editore: Prentice Hall, Anno edizione: 1996
J.M. Maciejowski, Multivariable Feedback Design, Editore: Addison-Wesley, Anno edizione: 1989
S. Skogestad, I. Postlethwaite, Multivariable Feedback Control, Editore: Wiley, Anno edizione: 1996
T. Glad, L. Ljung, Control Theory, Editore: Taylor & Francis, Anno edizione: 2000

Alternativa di Fredholm, spettro di un operatore compatto in spazi di Hilbert. Applicazione ai problemi variazionali astratti. Formulazione debole pre problemi di evoluzione. Metodo di Faedo-Galerkin. Teoremi di punto fisso di Shauder e Leray-Shauder. Equazione di Navier-Stokes stazionaria. Leggi di conservazione. Soluzioni generalizzate, condizioni di Rankine-Hugoniot. Onde di rarefazione e di shock. Condizioni di entropia. Invarianti di Riemann. Il problema di Riemann. Esistenza di soluzioni locali. Applicazione al sistema shallow waters, alla dinamica dei gas e al p-system. Problemi di controllo ottimo. Esistenza ed unicità per funzionali costo quadratici. Moltiplicatori di Lagrange e condizioni di ottimalità.

L'obiettivo di questo corso è fornire agli studenti conoscenze su argomenti avanzati, sia metodologici che tecnologici, rilevanti per l'ingegneria fisica.

The course aims at providing students with the critical and conceptual tools to reflect upon
some of the main ethical and social issues raised by the development of Artificial Intelligence
(AI). The course will be articulated in three modules, deeply interconnected to provide students
with a coherent narrative. The course will start with the analysis of the problems related to the
definition of "AI", starting from the first characterizations of AI dating back to the 1950s and
comparing them with the way AI is typically discussed nowadays. In the second module, the
topic of AI-related risk will be expanded so as to include aspects of uncertainty. In the last
module, the ethical framework of Trustworthy AI will be presented as a way to deal with AIrelated uncertainty. After a general presentation of the Trustworthy AI framework, specific
issues in AI ethics will be discussed, also depending on the students' interests.

Goals
The objective of the course is to present to the students, approaching the end of their M.Sc. educational carrier, an overview of the field of biochips and analytical lab-on-chip microsystems (whose recent advances are relevant both from the scientific and industrial point of view) with specific insights on the technological aspects of the interface between (micro)electronics and electrochemistry and biology, both at cellular and molecular levels. The content of the course reflects the multidisciplinarity of the nano-bio-tech field and is mainly focused on the different manipulation and detection approaches of biochemical target analytes on chip. The main goal is to provide the students with the fundamental theoretical tools for the design of active microfluidic systems (basics of fluid dynamics at the microscale), and of the engineering of the detection systems (optical and electrochemical transducers). At the same time, several practical examples are discussed in order to illustrate design choices and trade-offs. Starting from 2020, a part of the course will be taught by means of innovative learning techniques. The topics of microfabrication (hard and soft-lithography) will be addressed by means of MOOC videos recorded in actual clean-rooms and fabrication facilities. Finally, a "blended" and "flipped-classroom" approach will be adopted in the last part of the course including a project-based and a laboratory activities where student will fabricate real devices and boards: at first they will be grouped into small teams (3-4 persons max.), will design at home the layout of the microchannels and electrodes on a PCB including a transimpedance amplifiers, according to some design challenges posed by the teacher, and then will fabricate them in the lab with 3D printing and soft-lithography (PDMS elastomer by means of replica molding). A characterization with fluids, electrolytes. digital microscopes, pumps and virtual instrumentation (oscilloscope, network analyzer used for impedance measurements) will conclude this experimental activity. Prior to fabrication of the PCB, each team will present their design in the class and feedback from a general discussion will be collected.

Expected Learning Outcomes
Knowledge and understanding (Dublin Descriptor #1)
At the end of the course the students are expected to:
· possess a good understanding of lab-on-chip systems (needs, applications, advantages and limits) and bio-electronics convergence
· possess a basic knowledge of hard and soft lithographic microfabrication approaches
· possess a basic knowledge of the laws describing the flow of liquid in microchannels and the forces to actuate particles in liquid
· presses a good knowledge of the electrochemical interface and instrumentation for biosensing
· possess a good knowledge of the DNA macromolecule and the instrumentation for its analysis
· understand the potentials and challenges of CMOS/biochips integration
Applying knowledge and understanding (Dublin Descriptor #2)
At the end of the course the students are expected to:
· be able to analyze and design a microfluidic system in laminar regime (fluidic sizing, choice of components such as pumps/valves), starting from the sample preparation and manipulation
· be able to design an analytical assay (for instance for molecular recognition or DNA analysis)
· be able to design integrated detectors (both optical and electrochemical)
· be able to design current and impedance sensing circuits for biochips
Making judgements (Dublin Descriptor #3)
Students will be able to:
· evaluate the most appropriate approach (materials, fabrication) to address an analytical need
· apply the knowledge to different application areas
· apply the knowledge to design a novel micro-device
Communication (Dublin Descriptor #4)
During the work in teams for the experimental laboratory, students will be able to communicate among each other (in the first phase at home, when the microfluidic channels are designed upon technological specifications set in the classroom). Then, during the lab. and the oral exam their communication skills are deployed to motivate their design choices and comment the experimental results.

Topics
The course is articulated in theoretical lessons, combined with quantitative exercises and with case studies oriented to the analysis and design of microsystems. An experimental laboratory consisting of: a design phase at home in teams with a propor design software and two days of experimental activity in the lab. (fabrication and characterization of a PDMS microfluidic device). Experts in the biochip field are also invited during the course to lecture seminars on specific topics, such as micromagnetism and DNA analysis, or to present the point of view of companies carrying on R&D in the biochip field (STMicroelectronics, Flextronics, Dianax etc…). A visit to the renewed clean-room and microfabrication facility of Politecnico di Milano (PoliFab) concludes the course.
1. Introduction to biochips: architecture of lab-a-chip micro-analytical systems, motivations and impact, design paradigms, market projections, basic biological macromolecules used for as receptors in affinity biosensors.
2. Introduction to microfluidics: fluid dynamics, Reynolds number, electrical equivalent of hydraulic circuits (fluidic capacitance) and electro-osmotic flow. Actuation forces for particle manipulation within biochips: diffusion, electrophoresis, dielectrophoresis, electrowetting and droplet-based digital microfluidics. Active and passive microfluidic components: valves, mixers and pumps.
3. Introduction to microfabrication: review of Silicon CMOS-MEMS processes, lithography, thin film deposition and patterning, as compared to elastomer (PDMS) and plastic based soft lithography (replica molding).
4. Optical detection: fluorescence (strengths and limitations, Atomic Force Microscopy ), source-filter-detector architectures, integrated photonics for biosensing based on evanescent field (waveguides, Mach-Zehnder interferometers, ring resonators and contactless light monitors). Electrochemical detection: the electrochemical interface with physiological ionic solutions (charge transfer and induction).
5. Electrochemical instrumentation: analytical techniques (cyclic voltammetry, amperometry and impedance spectroscopy) and the potentiostatic workstations. Micro and nano-scale electrochemistry: effects of electrode scaling.
6. Low-noise current measurements: review of noise in electronic devices, the transimpedance amplifier (working principle, properties and limitations) and advanced circuit topologies (integrator-differentiator schemes with different CMOS-compatible bias resetting solutions).
7. Impedance measurements and instrumentation, comparison of sensing configurations: bridge, ratiometric, current sensing, lock-in detection, resonant circuits, FFT-based and time-domain approaches.
8. Detection of DNA: structure and properties, microarrays (spotting and probe synthesis), electrophoresis, amplification with Polymerase Chain Reaction and quantitative detection with real-time PCR and approaches for sequencing.
9. Biochips for molecular detection: resonating cantilevers for molecular weighing, amperometric detection of neurotransmitters, redox cycling, glucose sensors based on enzymatic mediators, and nanopores. Biochips for cellular detection: interface with electrogenic cells, equivalent model of passive cells, label-free impedance monitoring a cell colony dynamics and single cell counting and sizing with microfluidic impedance flow cytometry (design case study).
10. Sample preparation (cell lysis, sample mixing, concentration and purification). Microelectronics CMOS biochips: state of the art performance in CMOS detection, design criteria, advantages and limits, the critical role of the substrate, packaging and interfacing issues.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente) o condizionata (a scelta del docente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
Assessment
Exams are held at the end of the course (no intermediate tests). The exam consists of a written test (of a duration of 2 hours) followed by an oral exam. The written test (assessing Dublin Descriptors 1 and 2) encompasses the solution of numerical exercises focusing on 2 types. The first type concerns the microfabrication and the fluidics (and components) of micro-channels and devices. The second type mostly focuses on electrochemical detection. The students will be asked to analyze circuits and systems, as well as to design them. The maximum evaluation of the written test is 30. Two bonus points are added for those who attended the laboratory activity.
The outcome of the written tests is communicated to the students by email. Then, a meeting in the classroom is scheduled for the students to vision and discuss the correction of the written test. They can accept or refuse the evaluation, either by email or in-person communication. They can undertake the oral test in the following days, by agreeing a calendar with the teacher.
The oral discussion (assessing Dublin Descriptors 1,2,3,4) is mandatory for students achieving top marks in the ranking (> 27/30) in the written test and it is optional and open for all students with a mark above 17/30 in the written test. In the oral test (lasting 15 to 40 minutes) students will be asked discuss the ultimate limits of the different technologies, pros and cons, compare them, and to design complete systems to address novel design cases proposed during the oral exam. The maximum evaluation is 30 cum laude.

Patrick Tabeling, Introduction to Microfluidics, Editore: Oxford University Press, Anno edizione: 2010, ISBN: 9780199588169
Eugenio Iannone, Labs on Chip: Principles, Design and Technology, Editore: CRC Press, Anno edizione: 2014, ISBN: 978-1466560727
Albert Folch, Hidden in Plain Sight: The History, Science, and Engineering of Microfluidic Technology, Anno edizione: 2022

Questo corso si propone di introdurre gli studenti di ingegneria a questioni filosofiche ed etiche che emergono con l'uso delle tecnologie biomediche. Ad esempio, gli ingegneri spesso si avvalgono di simulazioni informatiche di modelli idealizzati dei loro oggetti di studio. Tuttavia, tali procedure sono affette da incertezze rappresentazionali e computazionali che sono ineliminabili. Si presentano quindi questioni filosofiche circa l'attendibilità delle predizioni risultanti, oltre che questioni etiche riguardanti la responsabilità morale dell'uso delle tecnologie utilizzate. Allo stesso modo, l'inedita abilità a trattare un'enorme quantità di dati apre scenari affascinanti, soprattutto nel campo della medicina di precisione. Eppure, anche qui si devono affrontare domande aperte sulla capacità predittiva degli algoritmi impiegati nel trattamento dei Big Data, oltre che problemi morali concernenti la privacy e il controllo delle informazioni personali.

L'obiettivo del corso è duplice:
1. Rendere gli studenti consapevoli delle complesse dinamiche della comunicazione e della persuasione, con un'enfasi speciale sulla componente personale (mittente/destinatario).
2. Equipaggiare gli studenti con abilità comunicative perché siano in grado di produrre comunicazioni orali, scritte e in forma multimediale che siano chiare e persuasive.
Il corso adotta tre strategie:

• Lezioni frontali
• Modalità "blended" e "flipped" (gli studenti accederanno in maniera autonoma a risorse online quali esempi di discorsi - in forma scritta o di video -, articoli, casi di studio, lezioni in video)
• Tutoring in presenza o in remoto (attraverso collaborazione "cloud") sui compiti da svolgere e sul progetto finale

L’insegnamento di Complementi di Meccanica Quantistica ha lo scopo di:
(i) approfondire gli elementi fondamentali della descrizione quantistica dei sistemi fisici basata sulla notazione di Dirac
(ii) evidenziare il legame tra questo formalismo e la meccanica quantistica ondulatoria trattata in corsi meno avanzati
(iii) fornire una sistematizzazione di base a concetti e tecniche quantistiche già incontrate in corsi precedenti
(iv) introdurre lo studente all’estensione dei concetti quantistici ad aspetti più avanzati

Conoscenza e capacità di comprensione (DD1): Lo studente conosce le origini classiche del formalismo quantistico
e comprende l’equivalenza tra la descrizione ondulatoria (Schrödinger) e quella matriciale (Heisenberg) della meccanica
quantistica.
Capacità di applicare conoscenza e comprensione (DD2): Lo studente è in grado di utilizzare il nuovo formalismo
(Dirac) per applicarlo alla descrizione quantitativa di fenomeni fisici tipicamente quantistici
Autonomia di giudizio (DD3): Lo studente è in grado di capire come l’uso delle proprietà di simmetria, già utile
nel trattare problemi fisici nel caso classico, si rilevi fondamentale nel caso quantistico, giustificando per esempio
l’esistenza dello spin
Capacità di apprendimento (DD5): Lo studente comprende come il formalismo appreso permetta di affrontare situazioni fisiche più complesse (meccanica quantistica relativistica, sistemi a molte particelle, quantizzazione dei campi)

Formalismo della Fisica dei Quanti: Dal formalismo hamiltoniano classico a quello quantistico: parentesi Poisson e commutatori. Misura: incertezza e perturbazione. Misure ideali e stati quantistici. Spazi vettoriali, osservabili e operatori. Ket e bra: spazi vettoriali nella notazione di Dirac. Operatori hermitiani e interpretazione statistica generalizzata Commutatore e principio di indeterminazione.
Teoria delle rappresentazioni: operatori con spettro discreto. Operatori innalzamento e abbassamento (oscillatore armonico e momento angolare). Sistemi a due livelli: spin e matrici di Pauli. Spinori ed equazione di Pauli. Sfera di Bloch.
Teoria delle rappresentazioni: operatori con spettro continuo. Spazi funzionali (Hilbert). Rappresentazione posizione e momento: funzione d’onda e funzione d’onda nello spazio dei momenti
Trasformazioni e simmetrie. Traslazioni e rotazioni. Operatore momento come generatore traslazioni. Rotazioni e momento angolare. Rotazione di spinori. Evoluzione temporale: operatore Hamiltoniano. Teorema di Ehrenfest. Schema di Heisenberg vs schema di Schroedinger
Sistemi composti. insiemi puri e miscele; cenni all’operatore densità
Meccanica quantistica relativistica. Richiami di dinamica relativistica. Equazione di Klein-Gordon. Equazione di Dirac. Proprietà dell'equazione di Dirac ed esistenza dello spin.
Sistemi a molte particelle. Schema con numeri di occupazione. Indistinguibilità e simmetria. Bosoni e fermioni.
Introduzione alla quantizzazione dei campi. Campo elettromagnetico: visione classica e quantizzazione. Cenni alla seconda quantizzazione

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
La verifica della preparazione avviene mediante esame che potrà essere sostenuto in uno degli appelli stabiliti dal calendario
di Scuola.
L'esame consiste di una prova scritta e orale, volta ad accertare:
la conoscenza del rapporto tra descrizione classica e quantistica di un fenomeno fisico (DD1, DD2))
la capacità di utilizzare un adeguato formalismo e la competenza nell’impiego del lessico specialistico (DD1, DD2)
la capacità di ragionamento critico di fronte ad un problema di carattere fisico e di organizzarne
la soluzione in modo lineare, logico ed efficace (DD1, DD3)
la capacità di collegare i risultati della soluzione formale con le proprietà fisiche del sistema (DD3, DD5)

J.J. Sakurai, J.Napolitano, Meccanica quantistica moderna, Editore: Zanichelli, Anno edizione: 2021, ISBN: 978-88-08-59991-9
Note: Testo di riferimento: contiene gran parte degli argomenti trattati nel corso, utile anche per chi fosse interessato ad approfondire la materia
J.J. Sakurai, Advanced Quantum Mechanics, Editore: Pearson, ISBN: 978-81-775-8916-0
Note: Testo di livello più avanzato, utilizzabile come approfondimento e consultazione. In particolare per quantizzazione dei campi (seconda quantizzazione) e meccanica quantistica relativistica
P.A.M. Dirac, I principi della meccanica quantistica, Editore: Bollati Boringhieri, Anno edizione: 2001, ISBN: 88-339-5161-8
Note: Testo "storico" scritto da uno dei padri della Fisica dei Quanti
J.M. Gillet, Application-driven quantum and statistical physics - Vol. 1, Editore: World Scientific, Anno edizione: 2019, ISBN: 9781786345547
Note: Ottimo testo da usarsi insieme agli altri 2 volumi. Contiene molti esempi e riferimenti storici. Non tratta la parte di meccanica quantistica relativistica
J.M. Gillet, Application-driven quantum and statistical physics - Vol. 2, Editore: World Scientific, Anno edizione: 2019, ISBN: 9781786345578
Note: Ottimo testo da usarsi insieme agli altri 2 volumi. Contiene molti esempi e riferimenti storici. Non tratta la parte di meccanica quantistica relativistica
J.M. Gillet, Application-driven quantum and statistical physics - Vol. 3, Editore: World Scientific, Anno edizione: 2020, ISBN: 9781786347886
Note: Ottimo testo da usarsi insieme agli altri 2 volumi. Contiene molti esempi e riferimenti storici. Non tratta la parte di meccanica quantistica relativistica

The course is intended to provide students with the fundamentals of the optical system design:
1. Learning and understanding the basic considerations of optical system design.
2. Analysing imaging tasks and finding appropriate solutions.
3. Using optical design software, setting up and analyzing simple optical systems.

Lectures and exercise sessions will allow students to:
1. know and understand the theory behind the optical system design (DdD 1)
2. transfer the thoretical concepts to practical optical design (DdD 2)
3. gain the capability of using optical design software (DdD 1 and DdD 2)
4. developing the expertise to modelling and designing real optical system exploiting the knowledge acquired in the course (DdD 2 and DdD 3)

1. Introduction to the optical design (t rigonometrical ray tracing at spherical surface, glasses, lens catalogs etc.)
2. Theory of paraxial approximation ( paraxial ray tracing or first order optics, lagrange theorem or optical invariant etc.)
3. Calculation of optical systems (cardinal points, relationship between conjugate distances, lensmaker equation, stops and pupils etc. )
4. Aberrations/defects in optical systems ( defocus, spherical aberration, coma, astigmatism, field curvature, distortion, chromatic aberrations etc.)
5. Basic considerations for lens design, evaluation and aberrations correction (lens bending, lens splitting, stop postion, symmetrization etc.)
6. Simple optical systems (Landscape lenses, Periscope lens, Celor lens, Achromatic doublets etc.)
In addition to theory, this course includes notable practical training using software for Optical System Design. Software is introduced and its use is practiced and exercised on the basis of selected use-oriented examples.

Prova orale obbligatoria
The assessment will be based on a oral exam. To pass the exam students are required to demonstrate the knowledge of the topics of course and the capibility to face problems dealing with optical design. Part of the exam will be conducted using the optical design software.

Rudolf Kingslake R. Barry Johnson, Lens Design Fundamentals 2nd Edition, ISBN: 9780123743015
Warren J. Smith, Modern Optical Engineering, Fourth Edition, ISBN: 9780819470966
M.J. Riedl, Optical Design Applying the Fundamentals, ISBN: 9780819477996
M. J. Kidger, Intermediate Optical Design, ISBN: 081945217-3
D. Malacara, Z. Malacara, Handbook of Optical Design Second Edition, ISBN: 0-8247-4613-9

The goal of this course is to present current and advanced methodologies for the control of robotic manipulators. The course covers selected topics ranging from kinematic and dynamic modelling of an industrial robot, to advanced motion planning and control, to control of the interaction of the robot with the environment based on force and vision feedback. The goal of the course is fully aligned with the overall goals of the Automation and Control Engineering Program, while being an excellent complement for students enrolled in other Programs (Biomedical Engineering, Computer Science and Engineering, Electronics Engineering, Engineering Physics, and others).
A mix of theoretical and industrially relevant topics characterizes the course, where extensive use of software for simulation and offline programming of robots will be made.

The expected learning outcomes of the course can be classified according to the Dublin descriptors.
Dd1 (Knowledge and understanding)
Students will understand:

• the role of industrial robots in the factory, why and where they should be used in the production systems;
• the dynamic model of a robot;
• the advanced tools for planning and control of the motion of the robots;
• the interaction of the robot with the environment;
• the role and importance of collaborative robots.
  Dd2 (Applying knowledge and understanding)
  Students will learn how to:
  -use mathematics to describe the motion of a robot, in terms of both kinematics and dynamics;
  -plan a suitable motion for the robot both in free environment and in presence of obstacles;
  -tune an industrial motion control system and understand the rationale and potentialities of advanced nonlinear model based control strategies;
  -master the control of the interaction of the robot with the environment, either with force or with vision sensors;
  -master software programs to simulate and to offline program the robots.
  Dd3 (Making judgement)
  Students will be able to decide which modelling, planning and tracking technique to adopt and how to apply them for the motion planning and control of industrial robotis

1. Introduction
Industrial robots: basic concepts and examples.
Market of industrial robotics.
Trends in industrial robotics.
2. Advanced robot kinematics
Review of direct, inverse and differential kinematics.
Kinematic calibration.
Kinematics of redundant manipulators: methods for redundancy resolution.
3. Robot dynamics
Dynamic models of robot manipulators.
Euler-Lagrange and Newton-Euler formulations: main properties.
Identification of dynamic parameters.
Direct and inverse dynamics.
4. Advanced motion planning
Review of motion generation in joint space and in operational space.
Path planning in joint space and in the operational space.
Path planning with obstacle avoidance.
Kinematic and dynamic scaling of trajectories.
Interpolation of points (splines).
5. Control of robot manipulators
Review of independent joint control methods.
Centralized model-based controllers: computed torque feedforward control, PD control with gravity compensation, inverse dynamics control, robust and adaptive control.
Operational space control.
6. Interaction with the environment
Force sensors.
Impedance and admittance control.
Hybrid position/force control.
7. Control with vision sensors
Image processing.
Camera calibration.
Image-based and position-based control systems.
Interaction matrix and image Jacobian.
8. Collaborative robotics (notes)
Human-robot interaction.
Collaborative applications.
Some of the practice sessions will make use of computer simulation tools and of commercial tools for robot offline programming.

Prova scritta obbligatoria, senza prove in itinere
The final assessment will be a written exam, consisting of both numerical exercises and theoretical questions.
In the written exam the student should be able to:
-compute the dynamic model of simple two degrees of freedom robots (Dd1, Dd2);
-discuss the properties of a kinematically redundant robot (Dd1);
-compute or rescale a trajectory in joint space or in the operational space (Dd1, Dd2, Dd3);
-discuss the main properties of centralized model-based controllers and solve simple numerical related design problems (Dd1, Dd2. Dd3);
-discuss the main properties of the control with force and vision sensors and solve simple numerical related design problems (Dd1, Dd2).

Bruno Siciliano, Luigi Villani, Giuseppe Oriolo, Alessandro De Luca, Foundations of Robotics, Editore: Springer, Anno edizione: 2025, ISBN: 978-3-031-85522-1 https://doi.org/10.1007/978-3-031-85523-8
B. Siciliano, L. Sciavicco, L. Villani, G. Oriolo, Robotica: modellistica, pianificazione e controllo, 3a Ed., Editore: McGraw-Hill Italia, Anno edizione: 2008, ISBN: 9788838663222 http://www.catalogo.mcgraw-hill.it/catLibro.asp?item_id=2317
G. Magnani, G. Ferretti, P. Rocco, Tecnologie dei sistemi di controllo, 2a Ed., Editore: McGraw-Hill Italia, Anno edizione: 2007, ISBN: 9788838672750 http://www.catalogo.mcgraw-hill.it/catLibro.asp?item_id=2141

The Critical Thinking course aims to provide, in an innovative manner, the methodological tools necessary to reason and argue soundly under conditions of risk and uncertainty in science and technology. The tools used will be those belonging to logic, philosophy, argumentation, and the psychology of thought, as applied to uncertain reasoning. The course content will be presented and discussed interactively with students. The course is intended to give students the opportunity to reflect critically on the cultural, social, and ethical impacts of science and technology

( Knowledge and understanding )
Students will:

• be acquainted with different forms of scientific reasoning
• acquire methodological skills in order to deal with risk and uncertainty
  ( Applying knowledge and understanding )
  Students will:
• foster their ability to solve problems and cope with uncertainty
• apply critical thinking to complex problems
• integrate their technical knowledge with humanistic, cognitive and ethical perspectives on science and technology
  ( Making judgements )
  Students will be able to:
• use methods and imagine scenarios in order to deal with (severe) uncertainty
• enhance creativity in thinking and reasoning
  ( Communication )
  Students will learn how to
• improve their skills in a writing an academic paper
• make academic presentations
• evaluate different forms of (risk) communication
  ( Lifelong learning skills )
  Students will
• be ready to evaluate and take decisions under conditions of uncertainty
• be better prepared to their future professional life in a socially responsible way
• be able to propose innovation solutions to complex and uncertain problems.

The course will cover different topics both from a theoretical and a more practical point of view.
The list of topics includes:

• scientific reasoning: deduction, induction, and abduction
• elements of logic and argumentation
• probabilistic reasoning: biases and paradoxes
• risk theory
• risk perception
• risk communication
• risk and moral argumentation
• decision making under severe uncertainty
• technological innovation and uncertainty
• scenario planning
• sustainability and scientific values

Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, con presentazione/discussione finale
An individual final essay
Students are required to develop an argumentative thesis in an individual essay in which the specific claims are justified by the use of critical thinking and the relevant literature.
Students are required to read and carefully study the suggested bibliography and to apply it to case-studies.
Collective presentations
Students are encouraged to give collective presentations in order to reduce the length of the individual final essay (this option is available only for some study programs). All detailed information and the pdfs of the scientific papers that we will be used will be made available.
The individual final essay will be evaluated considering the following criteria:
1) originality of the main thesis
2) knowledge of the scientific and technological background
3) argumentative skills
4) ability to integrate technical knowledge with critical reflection
5) compliance with academic standards

Damiano Canale, Roberto Ciuni, Aldo Frigerio, Giovanni Tuzet, Critical Thinking. An Introduction, Editore: Bocconi University Press - Egea, Anno edizione: 2021, ISBN: 9788899902858
Daniele Chiffi, Stefano Moroni, Uncertainty, Innovation and the City, Editore: Edward Elgar, Anno edizione: 2025, ISBN: 9781035342662 https://www.e-elgar.com/shop/gbp/uncertainty-innovation-and-the-city-9781035342662.html?srsltid=AfmBOoooZkaZdrxskmSY2ckahsDBQ2HOVkhHF7S-AU-24J5_HcpovGK-
Mattia Andreoletti, Daniele Chiffi, Philosophy of Risk, Editore: Cambridge University Press, Anno edizione: 2027
Note: If not yet published, an advanced draft of the book will be made available to students.

Data-driven and algorithmic technologies increasingly support or automate decisions in engineering, business, science, public policy and everyday life. Their outcomes, however, are not determined by technical performance alone: they also depend on how data are generated, selected and represented, on the objectives and constraints embedded in algorithms, and on the social contexts in which technological systems are designed and deployed.
The course provides students with an interdisciplinary framework for understanding, analysing and addressing fairness-related issues in data-driven technologies. It combines a sociological perspective on data, datafication and algorithmic processes with technical methods for identifying bias, evaluating different notions of fairness and designing fairness-aware technological and decision-making solutions.
The course is intended for students from different engineering backgrounds. It aims to show that data and algorithmic fairness is not a specialised topic limited to diversity and inclusion, but a central engineering challenge concerning the quality, reliability, legitimacy and societal impact of technological systems.
The sociological module introduces students to the relationship between data and society, considering the production and use of data as both a technological activity and a socio-political process. The technical module focuses on the mechanisms through which unfair outcomes may arise in data, algorithms and decision-making processes, and on methods and frameworks for evaluating and mitigating them.
Through lectures, discussions, case studies, exercises and a group project, students will learn to connect sociological interpretation, technical analysis and engineering design.

Upon successful completion of the course, students will be able to:
Explain and critically interpret how data and algorithmic systems are shaped by social, cultural, political and technological processes, and how their design and deployment may reproduce or transform existing inequalities.
Dublin Descriptor 1 — Knowledge and understanding
Analyse data-driven and algorithmic systems from a fairness perspective, identifying potential sources of bias across the different stages of the technological lifecycle and applying suitable concepts, criteria and methods to assess their outcomes.
Dublin Descriptor 2 — Applying knowledge and understanding
Formulate and justify fairness-aware design or policy choices, critically discussing alternative solutions, conflicting objectives, limitations and trade-offs, and clearly communicating the results of the analysis to audiences with different disciplinary backgrounds.
Dublin Descriptors 3 and 4 — Making judgements and communication skills
Class activities, independent study and project work will also support students in integrating technical and sociological perspectives, using feedback constructively and autonomously extending the course concepts to new application domains.
Dublin Descriptor 5 — Learning skills

The course is organised into two closely integrated modules.
Module A — Data and society: sociological perspectives
2 CFU
The first module introduces a sociological perspective on data and algorithmic processes. Data are considered not only as technical objects, but also as the result of social choices, classifications, institutional practices and power relations.
Topics include:
introduction to sociology and to the relationship between society and technology;
data, society and processes of datafication;
the social construction of data, categories and classifications;
identity, culture and technological systems;
the relationship between technology, power and inequality;
the role of gender, race and ethnicity, age, disability and socioeconomic conditions in the production and use of data;
historical and contemporary case studies concerning data-driven technologies;
data feminism and design justice;
epistemic injustice and its relevance to data production, technological design and decision-making;
the social, cultural and political implications of algorithmic systems in scientific, business, policy, healthcare and everyday-life contexts.
The module combines lectures with in-class discussions, exercises and case-study activities. This reflects the approach proposed for the sociological module, in which data production and application are treated as both technological and socio-political phenomena.

Prova scritta obbligatoria, senza prove in itinere
Valutazione continua obbligatoria
The course adopts an interdisciplinary and student-centred approach. Teaching activities combine:
lectures introducing sociological concepts, technical methods and analytical frameworks;
guided discussions on scientific literature, technological applications and controversial cases;
individual and small-group exercises;
analysis of real or realistic case studies;
activities requiring students to compare alternative definitions and approaches to fairness;
supervised development of a group project;
final presentation and discussion of project results.
The group project requires students to select or analyse a data-driven or algorithmic application and examine it from both a sociological and a technical fairness perspective. Depending on the selected case, students may analyse data generation, model assumptions, performance across population groups, decision rules, deployment conditions, affected stakeholders and possible mitigation strategies.
The project is intended to develop the ability to integrate perspectives from different disciplines and to formulate technically informed and socially aware design recommendations.
Indicative teaching mix
Teaching activity
Hours
Didattica trasmissiva/frontale
28
Didattica interattiva/partecipativa
12
Didattica laboratoriale
4
Didattica progettuale
6
Didattica valutativa
0
Total assisted teaching
50
Independent study and project work
75
Assisted teaching hours per CFU
10
This classification is consistent with the current distinction between transmissive teaching, interactive activities, laboratory work and project-based teaching. In particular, discussions, guided exercises and case-study activities fall within interactive teaching, while the supervised development and revision of the group assignment are appropriately classified as project-based teaching.

In the last decade, the notion of data has been associated with several changes, disruptions,
revolutions in various areas of our societies, as well as concerns, risks, and issues. Data
science, machine learning, artificial intelligence and big data have become central to every
aspect of social life. How can these complex and powerful technologies be managed and
governed for the benefit of all members of society now and in the future? In this course we will
address the growing attention to the embedding of data across different settings and the
challenges that such embedding brings for ethics and governance of data. The course equips
students with the knowledge and skills to reason on data governance and ethics. We introduce
key question tools on the use of data and associated technologies, reviewing ethical concerns
around data handling, and examining case studies from data collection, storage, and analysis.
Students are then encouraged to bring this increased awareness back to their own practice,

Professionisti e ricercatori riconoscono il ruolo centrale del design come motore di innovazione e cambiamento. Ciò che ha guidato la forte crescita dell'attenzione nei confronti del design da parte di manager ed imprenditori è un cambio di prospettiva: il design non è solo un driver estetico dell'innovazione, ma una pratica di gestione dell'innovazione, un nuovo insieme di processi, mindset, capacità e competenze organizzative. È qualcosa praticato non solo dai designer, ma da tutte le organizzazioni che cercano di innovare. Il Design Thinking, in particolare, si sta rapidamente diffondendo nell'interesse e nella pratica delle organizzazioni. La crescente attenzione dei professionisti al Design Thinking è evidente osservando le recenti acquisizioni operate da grandi società di consulenza e tecnologiche nei confronti di studi ed agenzie specializzate nell'utilizzo del design. Il Design Thinking è in piena espansione in quei settori in cui la trasformazione digitale richiede nuove competenze e capacità per sviluppare esperienze di consumo efficaci e piacevoli. Viviamo in un mondo in cui le opportunità tecnologiche si riversano sulla società a una velocità senza precedenti, un mondo inondato di tecnologie e informazioni. Ma gli esseri umani non usano tecnologie o dati; hanno bisogno di prodotti e servizi e il design può consentire alle persone di navigare in un mondo sovraffollato. Il Design Thinking è accettato come un approccio formale per promuovere l'innovazione. Guarda al valore e al cambiamento dal punto di vista delle persone; o, ancora meglio, dal punto di vista di ciò che è significativo per le persone. Analogamente a molti altri approcci, anche il Design Thinking combina tre fattori: (i) le tecnologie, come sono fatte le cose e le loro prestazioni; (ii) persone, come queste cose sono preziose per gli utenti; (iii) business, in che modo le organizzazioni possono trarre profitto dall'offrire soluzioni. Il Design Thinking si basa su una prospettiva unica: il Design Thinking parte dalle persone. L'approccio incorporato nel Design Thinking è centrato sull'uomo, abduttivo, iterativo e intrinsecamente orientato al cambiamento e alla visione di nuove possibilità. Un numero crescente di aziende, infatti, ha riconosciuto l'importanza del Design Thinking come mezzo per ottenere un vantaggio competitivo sostenibile. Il corso Design Thinking for Business mira ad arricchire le capacità di immaginare scenari innovativi e di guidare le trasformazioni.

To provide the main tools in dynamical systems and mathematical control theory, enabling students to address control problems of interest in applied sciences, by using the concepts learned during the course.
This course explores the fundamental mathematical principles and methods used to analyze and design control systems which are necessary tools in the design and management of systems that stem from sciences, technology, industry, or services.
Control theory analyzes properties of controlled systems, i.e., dynamic systems which can be acted upon by means of a control (or command).
The aim is to transform the system from an initial state to a particular end-state, respecting certain criteria, we might aim to stabilize the system in order to make it resistant to certain disruptions or to determine optimal solutions for a particular optimization criterion (optimal control).
Students of this course can profit of their training both in academic and in modern industry, in specialized fields such as aeronautics, aerospace, robotics, but also in life sciences, where the knowledge of the mathematical tools employed in control could help to better understand problems encountered to design, build, and optimize, or at least improve existing methods. From the mathematical point of view, due to the wide range of applications of control systems, both models involving ODEs and models involving PDEs are needed. The main part of the course deals with dynamical systems of ODEs. Control theory for PDEs is a wide field of study and research. Students will be introduced in this framework by means of some specific examples, as diffusion problems and vibrating phenomena, where they can exploit the main techniques employed to study controlled PDEs, largely used in applied sciences.

By the end of the course, students know classical problems of control theory governed by ordinary differential equations (ODEs). They also know examples of control problems involving partial differential equations (PDEs), of very large interest in various applied sciences.
Applications studied during the course include: fuel optimal landing of a space vehicle, minimum drag nose shape problem, boat guidance in a river, reproductive strategies in social insects (in the framework of controlled ODEs), heating or cooling a building in an optimal way, stabilization and controllability of a string (in the PDEs framework).
In reference to the Dublin indicators, the course emphasises:
DD1 – Knowledge and Understanding
The student will be able to clearly explain, using rigorous mathematical language, the definitions and theorems related to the course syllabus provided by the instructor.
DD2 – Applying Knowledge and Understanding
The student will be able to:
analytically use tools from control theory to various types of differential equations and systems;
set up an optimisation problem.
DD5 – Learning Skills
The nature of the subject, together with the structure of the course and the examination, is designed to foster and assess the student's learning abilities. In addition, the teaching approach adopted encourages students to develop independent learning skills and become more autonomous learners.

1. Dynamical systems. Linear systems of ODEs: general results and structure, exponential matrix. Asymptotic behavior of dynamical systems, stability (linearization, Lyapunov method), phase portraits.
2. Control Systems and asymptotic stabilization. Examples, reachable sets, linear systems, local controllability of nonlinear systems, bang-bang theorem. Stabilization of linear and nonlinear control systems.
3. Optimal control. Existence of Optimal Controls for Mayer problems and the problem of Bolza. Necessary conditions for the optimality: the Pontryagin Maximum Principle. Linear quadratic optimal control. Sufficient Conditions. Dynamic Programming. The linear quadratic case.
4. An introduction to control for partial differential equations. Controllability and stabilization of the wave and the heat equations. Optimal control for PDE’s, an introduction.
5. Further topics. Seminar of experts and students on recent advances in mathematical control theory, dealing e.g., with control problems for multiagent systems, traffic models, moving sets.
The 5cfu version of the course consists in the points 1, 2, 3, 5 of the above program.

Altro
In dependence of the number of students, several options are possibile:
Evaluation during lectures, depending on their participation. The students will be asked to contribute actively by preparing expositions and solving exercises.
Oral examination with, possibly, a preliminary presentation of some specific topic of the course or written exercises.
These options will be clarified during the first week of the course.

Bressan, Piccoli, Introduction to Mathematical Theory of Control, AIMS Book Series, 2007
Cannarsa, Gazzola, Dynamic Optimization for Beginners, EMS Press
Evans, An introduction to mathematical control theory, Berkley, Lecture notes
Li, Yong, Optimal Control Theory for Infinite-Dimensional Systems, Birkhäuser, Boston, 199

The course aims to present an in-depth analysis of the most important semiconductor devices used in modern electronics, with emphasis on the MOS Transistor and the Bipolar Junction Transistor. A main goal is to allow students to clearly understand the working principles and the electrical behavior of the devices starting from the careful investigation of their basic physics and pointing out the role played by their most relevant design parameters. This, in turn, aims to make students develop the skills and critical-thinking needed not only to address the operation but also to optimize the performance of a semiconductor device. Thanks to that, students will be able to understand what are the driving forces behind the successful trends of microelectronic technologies and why these trends are the outcome of relentless changes in device structure/geometry/materials, representing another important goal of the course. Through a series of sessions of simulation laboratory and two sessions of experimental laboratory, finally, students will also get some experience on the computer-aided analysis and on the electrical characterization of semiconductor devices. In summary, the course aims to provide students with the background and competencies needed not only to master the operation of electron devices within circuit applications but also to directly work on state-of-the-art semiconductor devices and technologies.

Knowledge and understanding (Dublin Descriptor 1)
After this course, the student knows and understands:

• the basic physics, the working principles and the electrical characteristics of the most relevant electron devices used in modern electronics;
• the dependence of the electrical behavior of the investigated electron devices on their design parameters;
• the guidelines ruling the evolution of semiconductor devices and technologies.
  Applying knowledge and understanding (Dublin Descriptor 2)
  After this course, the student is able to:
• apply suitable analysis methodologies to investigate the operation of semiconductor devices;
• determine the impact of changes in the design parameters of the investigated electron devices on their electrical behavior;
• master the operation of the investigated electron devices in circuit applications;
• discuss problems related to semiconductor devices and technologies with clarity, using technical terms and with a solid physical and mathematical background.
  Making judgements (Dublin Descriptor 3)
  After this course, the student is able to:
• determine the best solution to the trade-offs involved in the design of the most important electron devices in modern electronics;
• evaluate what should be required of the evolution of semiconductor technologies to prolong their success in the years to come.
  Learning skills (Dublin Descriptor 5)
  After this course, the student is able to:
• address the operation of electron devices different from those investigated in the course in autonomy;
• read and understand the scientific literature in the field of electron devices.

Review of basic properties of semiconductor materials

• Energy bands in a semiconductor material, dependence of the energy gap on temperature, density of states for the conduction and valence bands;
• Thermodynamic equilibrium: Fermi-Dirac and Maxwell-Boltzmann statistics, Fermi level, concentration of electrons and holes;
• Current transport by drift and diffusion of charge carriers, resistivity and sheet resistivity;
• Electrostatic potential in a semiconductor material: non-linear Poisson equation, Debye length;
• Non-equilibrium conditions: quasi-Fermi levels, continuity equation for electron and holes, dielectric relaxation time, Shockley-Read-Hall theory for carrier generation/recombination via defect-assisted processes.
  p-n junction
• Basic structure and d evice electrostatics under thermodynamic equilibrium: band diagram, built-in potential, electric field profile;
• Forward and reverse bias: band diagram, quasi-Fermi levels, potential drops and current components along the junction, Shockley ideal-diode equation, wide-base and narrow-base diodes;
• Generation/recombination currents in the space-charge region of the junction, high level of injection and parasitic resistances, Gummel plot, temperature dependence of the current-voltage characteristics of the device;
• Small-signal model of the p-n junction.
  Metal-semiconductor junction
• Basic structure and device e lectrostatics under equilibrium and non-equilibrium conditions;
• Current transport: Schottky's diffusion theory, Bethe's thermionic-emission theory, thermionic-emission-diffusion theory;
• Schottky effect;
• Tunneling ohmic contacts;
• Impact of interface states on device operation.
  MOS capacitor
• The MOS system: basic structure, working principles, band diagram under thermodynamic equilibrium and in the presence of a gate bias, working regimes of the device;
• Analysis of device electrostatics: calculation of the substrate charge as a function of the surface potential from the solution of the Poisson equation, surface potential and substrate charge as a function of the gate bias, threshold voltage;
• Small-signal capacitance: dependence on the gate bias (C-V curve), low-frequency, high-frequency and deep-depletion regimes;
• MOS capacitor with ring: split C-V, impact of the ring bias on device electrostatics, on threshold voltage and on the C-V curve;
• Oxide charge and interface states: impact on device electrostatics and on the C-V curve;
• Polysilicon gate: technological benefits, electrostatic drawbacks and impact on the threshold voltage of an n-MOS and of a p-MOS.
  MOSFET
• The long-channel MOSFET: basic structure of the device, working principles, gradual-channel approximation, electrostatic analysis and calculation of the depletion and inversion charge in the channel under subthreshold and strong-inversion conditions, general expression for the drain current;
• Current-voltage characteristics of the long-channel MOSFET above threshold: ohmic, parabolic and saturation regimes, pinch-off condition at the drain, band diagram and quasi-Fermi levels along the channel, small-signal model of the transistor, output resistance in the saturation regime and channel length modulation, carrier transit time in the channel, body-effect;
• Subthreshold conduction in the long-channel MOSFET: carrier diffusion from source to drain, subthreshold current, subthreshold slope, carrier generation in the channel, transcharacteristics of the transistor;
• Dependence of the electrical characteristics of the device on the oxide thickness, on substrate doping and on temperature, impact of oxide charge and interface states on the drain current, source/drain parasitic resistances and capacitances;
• Short-channel MOSFET: electrostatics, short-channel effect, DIBL, velocity saturation along the channel and its impact on the current-voltage characteristics of the device, early saturation of the drain current due to velocity saturation at the drain;
• MOSFET scaling: constant-field scaling and generalized scaling, scaling trade-offs and constraints, high-k dielectrics, metal gate, Tox-Wdmax space for the design of scaled MOSFETs, FinFETs and advanced MOSFET structures, relevant scaling issues in nanoscale devices, statistical variability, leakage currents.
  Bipolar junction transistor (BJT)
• Basic structure of the device, working principles, current gain, collector current of the prototype transistor;
• Electric field in the quasi-neutral base region: non-uniform doping, high injection, band-gap narrowing and engineering of the base material, expressions for the collector current;
• Base current: shallow emitter and deep emitter;
• Dependence of the current gain of the transistor on the collector current, Gummel plot, carrier recombination in the base/emitter depletion layer, parasitic resistances, Kirk effect, modulation of the base conductivity;
• Current-voltage characteristics of the BJT: saturation regime and forward-active regime, Early effect;
• Small-signal model of the BJT, frequency response, cut-off frequency and its dependence on the collector current, forward transit time;
• Evolution of device parameters, advanced device structures, polysilicon emitter, SiGe base.

Prova orale obbligatoria
The exam consists in an oral test. During the test, the student is asked questions related to all the topics debated in the course and has to prove not only his/her knowledge and understanding (see Dublin Descriptor 1) but also his/her ability to apply the acquired knowledge and understanding (see Dublin Descriptor 2). Demonstrating the skills needed to make correct judgements in the design of electron devices (see Dublin Descriptor 3) and the capability to extend what learned during the course (see Dublin Descriptor 5) is something relevant to achieve a high grade. The test may last up to, but not more than, one hour per student. The student is free to withdraw from the test at any moment; in that case, his/her test is considered as failed. At the end of the test, the student is told whether he/she passed the exam or not. In the former case, the student receives a grade from 18 to 30 (cum laude) out of 30. In the latter, the student has to repeat the test on another date, after improving his/her preparation.
The student who passes the oral test has his/her grade increased by up to two extra points depending on his/her successful participation in the laboratory activities carried out during the course. In particular, one extra point is given after each of the two sessions of experimental laboratory to the student who: i) took part in the session of experimental laboratory and performed the required electrical characterizations and analyses; ii) submitted, in due time, a report summarizing the activities performed and the results achieved during the session of experimental laboratory; and iii) had his/her report on the session of experimental laboratory positively evaluated. It is worth pointing out that, in order to perform the required analyses on the measurement data during the sessions of experimental laboratory, the student has to make use of Mathworks Matlab. Taking part in the sessions of simulation laboratory allows the student to develop the needed skills in that software.
The final outcome of the exam in terms of pass/fail and final grade (given by the sum of the points collected through the oral test and through the laboratory activities) is officially recorded in the report of the examination call.

Y. Taur, T. H. Ning, Fundamentals of modern VLSI devices, Editore: Cambridge Univ. Press, Anno edizione: 2021, ISBN: 9781108480024
Note: 3rd ed.

Aim of the course is to provide an in-depth knowledge on devices and systems for the acquisition and generation of sound and audio signals, specifically microphones and loudspeakers. The physical-mathematical modeling of the electroacoustic devices is studied in detail by means of the electro-mechano-acoustical analogies.

Dublin Descriptors
Expected learning outcomes
1) Knowledge and understanding
· know and understand the physical properties of the different electroacoustic devices for sound and audio signals acquisition and generation.
· know the characteristics and properties of the devices and systems for sound and audio signals acquisition and generation
2) Applying knowledge and understanding
· Analysis and application of microphones from their specifications.
· Analysis and application of loudspeakers from their specifications
5) Lifelong learning skills
· Students will learn how to analyze and design devices for sound and audio signals acquisition and generation.

Introduction on Electroacoustic Devices
Electro-Mechano-Acoustical Analogies
Mechanical and acoustical elements: mass, compliance, resistance, properties and equations. Impedance and admittance analogies. Electro-mechanical and mechano-acoustical transducers: physics and models.
Microphones:
Principle of transduction: moving coil, ribbon, piezoelectric and condenser microphones.
Sound Fields: Pressure, Free, Diffuse fields; Sound Pressure Level (SPL).
Sensitivity: definition, unit, measurement, dependence on microphone characteristics.
Pickup patterns: omnidirectional, bidirectional, cardioid, supercardioid, Hypercardioid; omnidirectionality limit; multiple pattern microphones; interference tube microphones.
Frequency response: diaphragm size dependence, proximity effect and electronic compensation, low‑frequency response control, static pressure equalization vent, wind screens.
Microphone noise: origin, physical-mathematical model, measurement, unit, typical values.
Dynamic range: measurement microphones, sound reinforcement microphones.
Microphone front-end electronics: architecture, preamplifier and output stage, output impedance. Wireless microphones.
Micro-Electro-Mechanical Systems (MEMS) microphones: silicon technology, MEMS process, fabrication steps, specifications. MEMS microphones and processing electronics integrated systems.
Pressure, Pressure-Gradient and Pressure/Pressure-Gradient Microphones: physical mathematical models; polar patterns, proximity effect, directivity factor.
Loudspeakers:
S tructure and principles of operation, modeling by equivalent circuits in electrical, mechanical and acoustical domains. Acoustic radiation impedances. Electrical impedance of a loudspeaker. Resonance frequency. Effect of the voice-coil inductance.
Irradiated acoustic power and SPL, sensitivity and efficiency. Dynamics of a loudspeaker, frequency response.
Response of a loudspeaker in finite and infinite baffles.
Loudspeakers in enclosure: closed box, air suspension, bass reflex.
Horns and compression drivers.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Type of Assessment
Description
Dublin descriptor
Written test
· Solution of numerical problems on electroacoustic devices
· Critical discussion on the data-sheet of a microphone or a loudspeaker
· Questions on topics in the course’s program
1, 2
1, 2, 5
Oral test
· Discussion on problems of the written test
· Questions on topics in the course’s program
· Analytical solution of problems on electroacoustic devices
1, 2
1
1, 2, 5

G. Bertuccio - Electronics and Electroacoustics for Sound Engineering https://beep.metid.polimi.it/
Note: available as pdf file in the course's website on BeeP
G. Bertuccio - electronic board notes https://beep.metid.polimi.it/
Note: available as pdf files in the course'w website on Beep
Leo L. Beranek, Tim J. Mellow, Acoustics: Sound Fields and Transducers, Editore: Academic Press - Elsevier, Anno edizione: 2012, ISBN: 9780123914217 https://www.elsevier.com/books/acoustics-sound-fields-and-transducers/mellow/978-0-12-391421-7

Goal of the course is to provide a basic knowledge of the most significant use of sensors, circuits, electronics systems in the biomedical instrumentation, starting from recalling the basic principles of the instrument/technique and then analysing and designing the sensors and electronics circuits employed in the instrument. The tipologies of instruments considered in the course are: conventional and advanced instrumentation for the analysis of signals of biological origin, systems for medical imaging based on the use of radiation (X and gamma rays). Special focus of the course will regard application of microelectronic circuits, as prosthetic devices (cochlear implant, artificial retina) and integrated readout circuits for signal processing in medical imaging. The course includes also two laboratories, one concerning the implementation of neural networks for processing detector signals in medical imaging, the second concerning the experimental measurements of gamma-ray coincidences (PET principle).

Knowledge and understanding
After passing the exam, the student:
knows the fundamental principles, concepts and basic implementations of sensors, circuits and electronic systems used in biomedical instrumentation
knows how to define specifications of sensors, circuits and electronic systems to be used in biomedical instrumentation
knows the main guidelines to design electronics circuits to be used in biomedical instrumentation, including attention to electronics noise performances, bandwidth and power requirements
knows the main potential and limitations of using microelectronics design to realize electronics circuits for biomedical applications
Applying knowledge and understanding
After passing the exam, the student:
is able to define the specifications of sensors, circuits and electronic systems to be used in biomedical instrumentation
is able to select technologies and methodologies to obtain solutions to problems regarding sensors, circuits and electronic systems to be used in biomedical instrumentation
is able to design electronics circuits or systems for processing signals of biological origin or for medical imaging
is able to evaluate measured performances of an electronic circuit or system used in biomedical instrumentation

I part – Electronics for processing biological signals
1) Origin of biological signals. Principle of electrocardiography (ECG) and requirements for the electronics. Electrodes.
2) Circuits for the amplification and filtering of biological potentials. Application to the electrocardiography: basic parameters and design issues (noise, insulation and protection techniques, common-mode and interferences rejection). The Instrumentation Amplifier: application to ECG.
3) The electronics of the pacemaker: basic functionalities, batteries, amplification circuits, pulses generators, telemetry. Generation of pulses larger than the battery voltage. Charge pumps. Amplifiers based on switched-capacitor topologies.
4) Design example of an ultra low-noise Instrumentation Amplifier for biosignals: architecture and noise analysis.
5) Microelectronics prostheses: cochlear implant and artificial retina.
II part – Electronics for medical imaging systems
1) Basics on medical imaging systems based on the use of X and gamma rays. Digital radiography, computed tomography (CT), SPECT and PET. Figures of merit: efficiency, resolution, signal/noise ratio. The PET time-of-flight (TOF).
2) Fundamentals of detectors for medical imaging. The Anger Camera. Scintillators coupled to photodetectors. Estimation methods for reconstructing the event in the detector (centroid, maximum likelihood, neural networks). The Silicon Photomultipliers (SiPMs) and their readout electronics. Gamma cameras for SPECT and PET. Potential and challenges of PET-TOF.
3) Integrated circuits for medical imaging. Electronics noise and its representation by means of the equivalent noise charge. The charge preamplifier and the optimization of the input transistor. The shaping amplifier. The peak stretcher. The baseline holder.
4) Timing techniques: leading edge, constant fraction discriminator, zero-crossing discriminator. Time-to-amplitude converter (TAC) and Time-to-digital converter (TDC).
5) Digital Radiography and Photon Counting Computed Tomography.
Laboratories
1) Design and optimization of neural networks for processing detector signals in medical imaging (3 hours)
2) Experimental measurements of gamma-ray coincidences (PET principle) (3 hours).

Prova orale obbligatoria
The examination is based on a oral exam only. The exam will consist in a discussion on two topics, one chosen about the first part of the course and one about the second part. After a general exposition of the topic requested, further questions may be more focused on specific details in order to better evaluate the depth of knowledge of the student about the topic under discussion. Schematics of the main circuits explained during the lessons are supposed to be described autonomously by the student.
A list of possible dates (about six per call) are provided in advance to the students to facilitate the choice of the day for the oral exam. Only one date can be chosen for each call (appello), but with possibility to modify the choice. The student can decline the proposed mark, if considered not satisfactory, without any penalty for the next calls. Tutorials to clarify course topics and individual student questions will be also organize during each call session.

J.G.Webster, Medical Instrumentation: Application and Design, Editore: John Wiley and Sons, Anno edizione: 2010
J.G.Webster, Design of Cardiac Pacemakers, Editore: IEEE
Smith and Webb, Introduction to medical imaging, Editore: Cambridge, Anno edizione: 2011
Krzysztof Iniewski, Medical Imaging: Principles, Detectors and Electronics, Editore: Wiley, Anno edizione: 2009
M.Nakhostin, Signal processing for radiation detectors, Editore: Wiley, Anno edizione: 2017
Angelo Rivetti, CMOS: Front-End Electronics for Radiation Sensors, Editore: CRC Press, Anno edizione: 2015

Il corso affronta le principali questioni teoriche relative all'innovazione socio-tecnica (quali i concetti di base dell'innovazione tecnologica, le dinamiche di diffusione delle innovazioni, i limiti del determinismo tecnologico, l'agency degli oggetti tecnologici, la flessibilità interpretativa della tecnologia, genere e tecnologia) e analizza esempi rilevanti di tecnologie d'avanguardia che hanno un impatto sulla società e sulla vita umana, come l'Internet delle cose, la fabbricazione digitale, la realtà aumentata e l'intelligenza artificiale. Anziché focalizzarsi sul loro funzionamento, le prestazioni e i problemi tecnici, il corso affronterà le questioni etiche che esse sollevano e gli impatti sociali che producono.

Technical issues have been playing a relevant role in forensic practice and represent an important aid to those who administer Justice. The typical engineering methods represent a large part of the forensic sciences, and technical experts' role has steadily gained importance in the last decades.
Justice has always sought certain means to assess the factual truth to render fair verdicts. To the investigators and triers of facts, science and techniques appear like the optimal tools to provide a certain representation of facts, based on scientific and irrefutable data.
Unfortunately, science and technique have their own limitations and they can only provide limited information on the events they model. This means that every decision taken based on a scientific judgment of facts has an intrinsic risk of being not fully correct, and it is important, from both the technical and ethical points of view, to consider such limitations.
The objective of this course is to frame the role of the technical experts inside the judicial proceedings and explain how the implicit doubts in the result of every piece of evidence collected through scientific and technical activities shall be quantified and presented to the triers of facts to explain them the risk of a wrong decision.
In this respect, forensic metrology is the branch of forensic science capable of providing a pondered answer to these issues not hiding the important, implicit limits of every technical assessment of the facts to the triers of facts, thus avoiding ethical and penal implications and will be covered by this course.
According to the topics covered by this course, it appears to be in line with UN SDG 16.

After attending this course, students are expected to gain basic knowledge of the legal proceedings, and in particular that required to serve as technical experts, both those appointed by the judge and those appointed by the parties.
They will also build competencies on how to report the results of their technical activities, so that they can be fully understood by the legal counterpart (judges, prosecutors, lawyers, jurors), including the implications related to the limited validity. This part will be covered from a theoretical perspective, providing the technical tools needed to identify the sources of uncertainty and quantify their effects on the validity of the provided expertise as well as the risk of issuing a wrong decision based on such expertise. Practical cases will be then presented and the students will be involved in simulating possible cases in team works.
The Dubliner descriptors associated with this course are DD1, DD2, DD3, and DD4.

The following main topics will be covered:
An overview of the legal systems and legal proceedings
The role of the technical experts inside the legal proceedings
The validation of the results returned by the scientific investigations (in the case of crimes) or scientific analysis of the issues that triggered the legal proceeding (n the case of litigations)
The concept of uncertainty in the expression of experimental results
Methods to evaluate the risk of wrong decision when the decision is based on experimental results
The method for ensuring scientific validity to forensic-science methods
All the above points will be covered also by referring to real cases that will be extensively discussed during classes, showing how a more correct evaluation of the scientific evidence might have avoided cases of miscarriage of justice.

Prova orale obbligatoria
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), individuale, senza revisioni periodiche, con presentazione/discussione finale
The evaluation consists of a final oral test, during which the students will be asked to present and discuss the topics covered during the course and show their ability to apply them to a hypothetical case study (DD1, DD2, DD3, and DD4).
If the students wish to prepare a written report on a case of their knowledge, analyzing and discussing how the scientific evidence contributed to the solution of the case, the discussion of such a report will be part of the exam and will replace one of the questions asked. The report can be prepared and submitted prior to the exam, but its submission is not mandatory. There is no penalty for not submitting such a report.
In general, the student should prove its ability to discuss the requirements scientific evidence must satisfy, the capability of identifying the origin of uncertainty contributions in the different steps in which scientific evidence is formed, evaluating them and presenting them to the trier of fact, in order to present and quantify a possible doubt about the validity of the collected evidence.

Alessandro Ferrero, Veronica Scotti, Forensic Metrology: An Introduction to the Fundamentals of Metrology for Judges, Lawyers and Forensic Scientists, Editore: Springer, Anno edizione: 2022, ISBN: 978-3-031-14618-3 https://link.springer.com/book/10.1007/978-3-031-14619-0

This course deals with the application of ethical theories to problems created, aggravated or transformed by technology. This course aims at showing how the decision to develop a technology (meant at large), the processes of its design, development, management, control, and production are inherently moral. It is intended to give students a chance to reflect on the ethical, social, and cultural impact of technological applications (in particular AI).
The course includes lectures by the instructor and invited lecturers; class participation will be expected, and students should apply what they learn through reading, lectures, and class discussions by looking at current events through an ethical lens.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will:
Acquire a broad perspective on the ethical and social impacts and implications of science and technology;
Be acquainted with normative ethics and normative argumentation;
Learn how to recognize and analyze ethical and social aspects and issues inherent in some technological applications;
Be able to understand how technical problems are inherently connected to a social dimension within a socio-technical perspective.
Applying knowledge and understanding
Students will:
Be able to use critical skills in clarifying and ethically analyzing case-studies involving technology;
Be able to apply ethical theories to problems created, aggravated or transformed by current technologies;
Be able to explore and assess possibilities for solving or diminishing existing and emerging ethical and social problems.
Making judgements
Students will be able:
To autonomously analyze the ethical issues of a technology and to develop a critical analysis on that;
To evaluate and select the appropriate knowledge in the effort of elaborating and justifying a philosophical argument on a topic autonomously selected.
Communication
Students will learn to:
Exercise and improve their skills in a critical writing;
Present in an effective way the results of their research, being able to justify their choices.
Lifelong learning skills
Students will:
Be better prepared to their future professional life in an ethically and socially responsible way;
Be able to analyze problems through an ethical lens.

The course will cover different topics both from a theoretical and a more practical point of view. We will start with a broad analysis of the concept of responsibility, in particular in an engineering perspective, and of normative ethics and its tools. We will discuss ethical questions in the design of technology with a focus on Design Ethics and its Social Ethics paradigm. Then ethics in IT-configured societies will be discussed and technology as the instrumentation of human action will be presented. Topics will include, for instance, the novel framework of Responsible Research and Innovation, Value-Sensitive design, the ethical aspects of technicolgical risks, the relationship among sustainability, ethics and technology, information flow, privacy, and values and uncertainty
Students will be supervised in class sessions in the development of the individual final essay in order to meet the standards required by scientific publications.

Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, con presentazione/discussione finale
Type of assessment
Description
Dublin descriptor
Collective presentations
Students are encouraged to:
Develop an argument in which the presented claims are justified by the use of ethical argumentation and the relevant literature;
To present this argument in an oral presentation in class in front of the instructor and their student colleagues.
1, 3
4, 5
Individual written essay
The individual final essay must show that the student has the capacity to read and carefully study the suggested bibliography and the ability to apply it to case-studies;
The individual final essay will be about the ethical issues associated to technologies that require to have developed critical skills in analyzing technical problems with the tools provided by ethical theories and argumentation.
1, 2
3, 4, 5

Ibo van de Poel, Lambèr Royakkers, Ethics, Technology, and Engineering: An Introduction (second edition), Editore: Wiley-Blackwell, Anno edizione: 2023, ISBN: 978-1-119-87943-5
Mattia Andreoletti, Daniele Chiffi, Philosophy of Risk, Editore: Cambridge University Press, Anno edizione: 2027
Note: If not yet published, an advanced draft of the book will be made available to students.
Behnam Taebi, Ethics and Engineering. An Introduction, Editore: Cambridge University Press, Anno edizione: 2021, ISBN: 9781316628409

The course Ferroic materials explores the scientific principles, material classes, synthesis, characterization, and engineering applications of piezoelectric, ferroelectric, ferromagnetic and multiferroic materials, from bulk to thin and ultrathin films.

Knowledge and understanding (DD1)
Students will understand the properties of ferroic materials and their microscopic physical origin within the existing classical and modern theories. They will be able to comprehend cutting-edge technological and scientific advances in the field.
Applying knowledge and understanding (DD2)
Students can transfer these physical concepts to applications requiring ferroic materials for sensing, mechanical actuation, information storage and electronic processing.
Students will be able to experimentally access the basic properties of known and unknown ferroic materials.
Make judgements (DD3)
Students will be able to identify the suitability of ferroic materials for each specific application/project making reasoned decisions.
Communication Skills (DD4)
Students can communicate information, ideas, problems and solutions regarding the characterization and exploitation of ferroic materials.
Learning skills (DD5)
Students will have developed those learning skills that are necessary for them to continue to undertake further study with a high degree of autonomy.

Introduction to ferroic materials
Definition, classification, and examples
Key properties of piezoelectric, ferroelectric and ferromagnets
Structure-property relationships
Synthesis of bulk material and thin films
Piezoelectric materials
History, basic concepts and principles
Physics of piezoelectricity for the response to mechanical and electrical stimuli
Classes of materials (piezoelectric ceramics, polymers, and lead-free alternatives to standard piezo materials)
Characterization techniques
Examples, applications, and integration
Ferroelectric materials
The classical theory of polarization
Switching and hysteresis loops in ferroelectrics
Material classes (perovskites, LiNbO 3, layered and other families, ferroelectric semiconductors, non-bulk ferroelectrics, ultrathin ferroelectrics, 2D materials)
The role of hafnium-oxide in electronics
Macroscopic characterization techniques (Sawyer-Tower method, displacive currents methods (PUND))
Microscopic characterization (piezo-response force microscopy)
Beyond the classical approach: the theory of polarization as an adiabatic flow of currents
Ferromagnetic materials
Overview of magnetic materials (diamagnetic, paramagnetic, and ferromagnetic)
Magnetic hysteresis, coercivity, and remanence
Characterization of magnetic materials
Soft and hard materials, permanent magnets
Integration in electronics and mechanical systems
Multiferroics
Bulk and layered multiferroics
The physical reasons behind their rareness
Applications, Future Trends and Challenges
Pyroelectric devices
Piezoelectric actuators (e.g. MEMS)
Ferroic memories (e.g. FeRAM, ferroelectric tunnelling junctions) and field-effect devices (e.g. FeFET transistors)
Magneto-electric spin-orbit devices beyond CMOS transistors
Neuromorphic computing with ferroelectrics
Actuators and sensors at the microscale in microelectromechanical systems
Laboratory activities (hands-on)
The course will include a hands-on session/laboratory that will show the students the tools used for the investigation of magnetic (magneto-optical Kerr effect and microscopy, vibrating sample magnetometry) and ferroelectric (Sawyer-Tower versus displacive current methods (PUND)) materials.

Prova orale obbligatoria
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), individuale, senza revisioni periodiche, con presentazione/discussione finale
The exam will consist of an oral examination designed to assess the student’s understanding of the course material. Both theoretical and experimental aspects will be given equal weight.
Students may begin the examination with a topic of their choice. They are also encouraged to present an in-depth study of a specific topic, supported by a short written report demonstrating their ability to apply the acquired knowledge to address a problem of interest in the field of ferroic materials (not mandatory, though appreciated).

Ashim Kumar Bain and Prem Chand, Ferroelectrics: Principles and Applications, Editore: Wiley, Anno edizione: 2017, ISBN: 978-3527342143 https://www.wiley.com/en-ae/Ferroelectrics%3A+Principles+and+Applications-p-9783527342143
K. Rabe, Ch. H. Ahn, J.-M. Triscone, Physics of ferroelectrics: a modern perspective, Editore: Springer, Anno edizione: 2007, ISBN: 978-3-540-34591-6 https://link.springer.com/book/10.1007/978-3-540-34591-6
S. Blundell, Magnetism in condensed matter, Editore: Oxford University Press, Anno edizione: 2001, ISBN: 978-0198505914 https://global.oup.com/ukhe/product/magnetism-in-condensed-matter-9780198505914?cc=it&lang=en&

FISICA DEI REATTORI A FISSIONE
La fissione. Sezioni d'urto. Equazioni di bilancio per neutroni termici. Criticita ad un gruppo. Rallentamento. Misure di lunghezze di diffusione e rallentamento. Equazione dell'eta. Trattamento delle risonanze. Criticita a piu gruppi. Reattori eterogenei. Coefficienti di reattivita di temperatura. Veleni. Evoluzione del combustibile. Cinetica puntiforme. Transitori operazionali. Il metodo Montecarlo.

Scopo dell'insegnamento è fornire allo studente gli elementi di base della robotica, con particolare riferimento ai robot manipolatori. L'inquadramento della robotica industriale e delle sue molteplici applicazioni motiva lo studio dei fondamenti della materia, dalla cinematica del robot, alla pianificazione e generazione delle traiettorie di movimento, al controllo in anello chiuso del moto.
Gli obiettivi dell'insegnamento sono del tutto allineati con gli obiettivi formativi del Corso di Laurea in Ingegneria dell'Automazione, esponendo lo studente a una delle tecnologie, la robotica, di maggiore rilievo nell'automazione.

I risultati di apprendimento attesi possono essere classificati secondo i descrittori di Dublino.
Dd1 (Knowledge and understanding)
Lo studente:

• conosce i principali elementi che caratterizzano un robot industriale
• dispone degli strumenti per caratterizzare posizione e orientamento di un corpo rigido
• comprende il modello cinematico di un robot
• conosce la pianificazione del moto e il controllo dei robot
  Dd2 (Applying knowledge and understanding)
  Lo studente:
• usa un approccio matematico strutturato per rappresentare la cinematica di un manipolatore robotico
• sa calcolare una traiettoria che il robot deve eseguire nello spazio dei giunti o nello spazio Cartesiano
• usa i più comuni dispositivi e algoritmi per misurare e attuare il moto dei robot manipolatori
• comprende l'architettura di un sistema di controllo di un robot industriale ed è in grado di eseguire la taratura dei relativi regolatori
• sa usare strumenti di simulazione e analisi numerica di manipolatori robotici
  Dd3 (Making judgement)
  Gli studenti saranno in grado di decidere quali tecniche di modellazione, pianificazione del moto e controllo adottare e come applicarle per la pianificazione e generazione del movimento dei manipolatori robotici

Introduzione alla robotica: Robot industriali, robot di servizio, mercato della robotica, trend in atto.
Caratterizzazione e selezione del robot: Specifiche tecniche del robot. Scelta del robot in base all'applicazione. Strumentazione.
Posizione e orientamento del corpo rigido: Formalismi per rappresentare la posizione e l'orientamento di un corpo rigido. Matrici di rotazione, angoli di Eulero, quaternioni. Matrici di trasformazione omogenea.
Cinematica del robot: Legami tra le posizioni dei giunti e dell'organo terminale. Cinematica diretta: convenzione di Denavit Hartenberg. Cinematica inversa.
Cinematica differenziale del robot: Legami tra le velocità dei giunti e dell'organo terminale. Lo Jacobiano del robot. Singolarità cinematiche. Cinetostatica.
Pianificazione del moto: Programmazione del moto del robot: linguaggi di programmazione. Pianificazione del percorso e pianificazione della traiettoria. Traiettorie nello spazio dei giunti. Traiettorie nello spazio operativo: traiettorie di posizione e orientamento.
Attuatori e sensori per la robotica: Motori DC e brushless. Sensori di posizione.
Controllo dei manipolatori robotici: Controllo indipendente ai giunti. Controllo del giunto robotico. Effetti della flessibilita' ai giunti.
Argomenti avanzati di robotica (cenni): Interazione con l'ambiente: sensori di forza e di visione. Robot ridondanti. Robot collaborativi

Prova scritta obbligatoria, senza prove in itinere
L'esame consiste in una prova scritta sull'intero programma dell'insegnamento, formata da esercizi e domande aperte sulla teoria. La prova scritta sarà integrata da un colloquio solo nei casi in cui il docente ne ravvisi l’opportunità.
Lo studente dovrà, in sede di esame:

• dimostrare di sapere operare correttamente con i formalismi per la caratterizzazione di posizione e orientamento di un corpo rigido (Dd1, Dd2)
• caratterizzare con gli opportuni strumenti metodologici la cinematica di un manipolatore robotico assegnato (Dd1, Dd2)
• calcolare cinematica diretta e inversa e determinare le singolarità di un manipolatore robotico assegnato (Dd1, Dd2)
• calcolare traiettorie nello spazio dei giunti e nello spazio operativo (Dd1, Dd2)
• discutere le proprietà di attuatori e sensori del moto (Dd2)
• discutere il sistema di controllo di posizione e velocità di un manipolatore robotico (Dd1, Dd2, Dd3)
• progettare i regolatori di posizione e velocità nel dominio delle frequenza (Dd2, Dd3)

Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani e Giuseppe Oriolo, Robotica. Modellistica, Pianificazione e Controllo, Editore: McGraw-Hill, Anno edizione: 2008, ISBN: 9788838663222 https://www.mheducation.it/9788838663222-italy-robotica-3ed
Gianantonio Magnani, Gianni Ferretti, Paolo Rocco, Tecnologie dei sistemi di controllo, Editore: McGraw-Hill, Anno edizione: 2007, ISBN: 978-8-83-867275-0 https://www.ilovebooks.it/tecnologie-sistemi-controllo-p-1450.html
Bruno Siciliano, Luigi Villani, Giuseppe Oriolo, Alessandro De Luca, Foundations of Robotics, Editore: Springer, Anno edizione: 2025, ISBN: 978-3-031-85522-1 https://doi.org/10.1007/978-3-031-85523-8

The course aims at providing knowledge about organic materials for electronics, optoelectronics and optics and related devices. The course deals with conducting materials, photochromic materials, liquid crystals, as distinct topics. For each class of materials, molecular structure-properties relationships are studied also considering the possible technological applications. Attention is focused on the design of devices such as LEDs, transistors, photovoltaic cells, LCDs, sensors and smart windows. Finally, techniques for the deposition of the organic materials into films are considered.

The expected learning outcomes are:
aquiring knowledge of the principles of molecular engineering of electronic materials, i.e. organic semiconductors, and optically-active materials (DdD1);
aquiring knowledge of the structure to property relationship, with specific focus on optical and electrical properties (DdD1);
aquiring ability to apply the principles of molecular engineering to design functional materials for specific applications (DdD2);
aquiring the ability to predict the properties of a functional material, given the molecular structure (DdD2);
aquiring knowledge of the working principles of the main electronic devices, their characteristics, components and architecture (DdD1);
aquiring ability to apply the above knowledge to a critical description and discussion of the devices and related materials properties, limitations and developments (DdD2).
Overall, these learning outcomes are expected to provide the student basic knowledge tools necessary to i) understand the technology trends and i) perform future activities aimed at the development of materials for electronics optoelectronics and smart optics.

Functional materials:
Basic principles.
Fundamentals on organic functional materials: molecular skeleton, side groups; the electronic effect of side groups.
Conjugated materials
Molecular design: from localized chemical bonding to delocalized chemical bonding (LCAO, the frontier energy levels)
Structure to properties relationship: the UV-vis spectroscopy
Polyacetylene: structure, synthesis, electrical properties; Shirakawa films
Doping: chemical and electrochemical processes; charge carriers in organic materials; fundamentals of charge transport
The deactivation from the excited state: the emissive properties (photoluminescence)
Applications of conducting polymers: OLED, photovoltaic cells, thin film transistors, smart windows, sensors
Liquid crystals
Fundamentals, mesogens, main LC phases; polymer liquid crystals; LC embedded in polymer matrix (preparation methods, drop configuration, smart windows).
Optical thermometers. Smart windows. Liquid crystal displays.
Photochromic and thermochromic materials
Fundamentals, main families
Applications: ophthalmic lenses, inks
See bibliography for suggested books (covering more than the programme of the course). Additional material or lecture notes are given by the lecturer.

Prova scritta obbligatoria, senza prove in itinere
A written examination, which consists in:
A numerical exercise on a device: OFETs, photovoltaic cells, OLEDs or smart windows and filters.
The exercise aims at applying knowledge to calculate the efficiency or the main characteristics of a device starting from given experimental data. Regarding this part, not only the correct calculation is assessed but also the proper use of significant decimals and units of measurement. Then, student is required to give a critical assessment of the performance with regards to the materials amployed as well as the manufacturing process. Similarly, student is required to assign specific devices to the corresponding materials on the basis of the materials structure and processing. The assignment has to be properly supported.
An open question of one of the main topics of the course
In the open question, the student is required to clearly describe and critically discuss the proposed topic. Not only the knowledge about the topic is assessed, but also the correct use of a proper scientific language and clarity in the description.
An exercise on the structure-to-property relationship
It aims at evaluating the ability of the student to apply the acquired knowledge on molecular design to a case study, finding the right key to solve the problem.
A series of multiple choice questions
It allows to assess the knowledge and comprehension throughout the subjets.

Functional Organic and Polymeric Materials, Editore: Ed. by T.H. Richardson - Wiley, ISBN: 0471987247
Handbook of Conducting Polymers, Editore: Ed. by T.A. Skotheim - Marcel Dekker inc., Anno edizione: 1986, ISBN: 0-8247-7395-0
Handbook of Conducting Polymers, Editore: Ed. by T.A. Skotheim, R.L. Elsenbaumer, J.R. Reynolds - Marcel Dekker, Anno edizione: 1998, ISBN: 0-8247-0050-3
S.O. Kasap, Optoelectronics and Photonics: Principles and Practices, Editore: Prentice Hall, Anno edizione: 2001, ISBN: 0-201-61087-6
Organic Photochromic and Thermochromic Compounds, Editore: Ed. by J.C. Crano and R.J. Guglielmetti - Plenum Press - New York, Anno edizione: 1999, ISBN: 0-306-45882-9

Il corso si propone di fornire conoscenze sui materiali funzionali, intelligenti e interattivi e relativi campi di applicazione. Si riprenderanno e saranno approfonditi i fondamenti dei materiali coniugati al fine di evidenziare le relazioni struttura-proprietà per ogni classe di materiali qui considerata, e si forniranno gli strumenti per la progettazione molecolare. Durante il corso saranno trattati i materiali conduttori e semiconduttori per l'elettronica e l'optoelettronica, i materiali fotocromatici e i cristalli liquidi, come argomenti distinti. Alcuni contenuti riguarderanno anche la progettazione di dispositivi come LED, transistor, celle fotovoltaiche, LCD, sensori e finestre intelligenti. Si confronteranno le prestazioni degli stessi dispositivi a base di materiali inorganici. Infine, saranno considerate le tecniche più importanti per la deposizione dei materiali organici in film sottili.

The course is conceived as an experiential-based learning process, where students acquire frameworks, models, and tools to develop their business mainly from the experience and advice of practitioners, i.e., entrepreneurs, venture capitalists, business angels, incubator/accelerator managers, with whom the course is designed. The faculty will provide a core of preliminary concepts through frontal lectures on high-tech start-ups’ creation and funding, after that the students will start to work in teams on their own business models, receiving inputs and feedback from practitioners, who participate as guest speakers, and throughout tutoring sessions.
The course fits into the overall program curriculum pursuing some of the defined general learning goals. In particular, the course contributes to the development of the following capabilities:
Understand and design solutions, applying a scientific and engineering approach, in relation to the setting up of a new business, the design of its business model, and the identification of the proper funding channels
Interact in a professional, effective and constructive way in teams and with external practitioners

At the end of the course, the students will be able to:
Comprehend the functioning of the entrepreneurial ecosystem (DdD1 - Capability of knowledge and understanding; DdD2 - Capability of applying knowledge and understanding)
Understand what is a business model and its building blocks (DdD1 - Capability of knowledge and understanding; DdD2 - Capability of applying knowledge and understanding)
Identify the key challenges for the creation of high-tech start-ups and the funding mechanisms available, and understand when it is appropriate to use each of them, according to the specific start-up’s industry, funding requirements and stage of the life cycle (DdD1 - Capability of knowledge and understanding; DdD2 - Capability of applying knowledge and understanding).
Through the project work, the students will also be able to:
Test and validate the business model in the real world with potential customers (DdD3 - Capability of making judgments; DdD4 - Capability of communication)
Write an effective business proposal to ask for funding (i.e., to business angels, venture capitalists, incubator programs..) (DdD3 - Capability of making judgments; DdD4 - Capability of communication)
Effectively work in teams (DdD3 - Capability of making judgments; DdD4 - Capability of communication)
Interact with practitioners of the financial sector (DdD4 - Capability of communication)
Summarize and present the results achieved in the business proposal in front of potential investors clearly and convincingly. (Capability of communication)

The course program includes the following topics:
The creation of high-tech start-ups. Entrepreneurship in high-tech industries and high-tech start-ups in Italy and in the world
The development of of high-tech start-ups, and their performances, including the success factors
The Business Model: what is a business model and how to fund it through equity capital, debt capital and public subsidies
How to design the Business Model: the value proposition and business model testing; the use of the Business Model Canvas framework; organizational structure and management; market strategy and market positioning; competitors’ analysis; how to protect competitive advantages; business model risk assessment
The financing of high-tech start-ups: a series of guest speakers, including entrepreneurs, venture capitalists, business angels, incubator/accelerator managers, will bring their direct experience through success stories regarding the funding (and creation) of high-tech start-ups (entrepreneurs) and explaining their decision-making and investment process (investors)
How to design and perform an effective elevator pitch.

Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
Valutazione continua condizionata (a scelta del docente)
Students must identify a new business idea based on an innovative technology (e.g., the results of current scientific research) or an innovative way of doing business in an incumbent industry (e.g., an innovative business model), form an entrepreneurial team, develop a document describing the business model, and prepare an elevator pitch.
The examination consists of the evaluation, by the faculty and a commission of practitioners (including guest speakers of the course), of both the business model document and its presentation before the same commission.
In the examination, students must demonstrate that:
They have a good understanding of the challenges involved in creating and funding a new start-up and of how financial intermediaries and other actors of the entrepreneurial and financial ecosystem may support the start-up’s development
(DdD1 – Knowledge and Understanding; DdD2 – Applying Knowledge and Understanding).
They understand the structure and components of a business model and are able to design and develop one tailored to their business idea
(DdD1 – Knowledge and Understanding; DdD2 – Applying Knowledge and Understanding).
They have effectively validated the proposed business model in the real world and are able to critically assess and select the most appropriate funding channels for the venture
(DdD3 – Making Judgements; DdD2 – Applying Knowledge and Understanding).
They are able to summarize, communicate, and defend the key elements of their business model clearly and convincingly in a public setting through an effective elevator pitch
(DdD4 – Communication Skills).
During the tutoring sessions, as well as in the final examination, students must also demonstrate that:
They are able to work effectively within entrepreneurial teams, contributing constructively to the development of the project and the achievement of shared objectives
(DdD4 – Communication Skills; DdD5 – Learning Skills).
They are able to critically incorporate and leverage feedback and advice provided by practitioners and guest speakers to improve their business model and entrepreneurial proposal
(DdD3 – Making Judgements; DdD5 – Learning Skills).

clips on startup creation made available on Beep
Note: Topics:
team formation;
characteristics of entrepreneurial ecosystems;
startup funding;
business models.
Alexander Osterwalder, Yves Pigneur, Business model generation, Editore: John Wiley & Sons, Anno edizione: 2010

Scopo del corso è di offrire una lettura storica del percorso evolutivo dell'impresa europea nei suoi snodi fondamentali, con particolare riguardo all'innovazione tecnologica. In questa prospettiva sarà collocata anche la storia dell'impresa italiana riferita alle esperienze sia delle maggiori imprese private (Fiat, Olivetti, Pirelli) sia dell'impresa pubblica (Iri), soffermandosi infine sulle ragioni della caduta delle imprese di grandi dimensioni e sull'affermazioni di forme di capitalismo "intermedio". Nella sua parte finale il corso insisterà sulla continuità dei caratteri dell'impresa europea, domandandosi se, all'interno di uno scenario post- globale come l'attuale, essa abbia la possibilità di continuare il percorso lungo il proprio asse o se invece emerga l'opportunità di una contaminazione con altre esperienze modulate da differenti contesti storici.

The first part of the course presents a few of the main nanoscopic imaging techniques, focusing on the information that can be obtained (morphology, chemical contrast, short-and long-range structure, spectroscopy) and on the choice of the most effective nanoscopic techniques to tackle a technological or scientific investigation. The main topics are the theory and operation of scanning (SEM) and transmission (TEM) electron microscopy, and of scanning probe microscopy (SPM). The second part of the course presents the theory and technology of micro/nanofabrication, providing the student with the essential knowledge on the basic and advanced processing techniques for the realization of electronic, photonic and spintronic devices. The course covers the principal deposition methods (e.g. thermal evaporation, sputtering, laser ablation, chemical vapour deposition), for the growth of thin films and heterostructures, and the main micro- and nanopatterning technologies (e.g. optical and e-beam lithography, advanced lithographic techniques, and pattern transfer methods) for the definition of the devices.

This is a non-standard course, with no lectures, where physics is learned by team working.
Physics aims at the description of all phenomena through a minimum set of laws and definitions.
Experimental physics deals with the accurate and objective observation of phenomena, and their interpretation in terms of physical laws.
Engineering exploits for practical purposes the laws of physics and enables new observations.
Following this approach, students participating to competitions of experimental physics are confronted with the three steps of observation, medialization and realization.
Beyond the pure exercise of physics, being part of a team that needs to self-organize the work over several months, the course aims at confronting the students with the difficulties of overcoming practical obstacles. Moreover, the need of interacting effectively within the team and of presenting and defending their own results to other teams requires the development of good communication skills.

Being part of a team working for severa month to solve experimental physics problems the student will know how to:

• Organize the work of a sub-team of 2 to 4
• Coordinate the work of the sub-team with that of the other sub-teams
• Plan the individual activities in order to provide results to the thet of the team
• Make simple physical models, realize practically experimental prototypes and compare theory with experiments.
• Discuss the quality of the results with peers and tutors
• Present and defend results in the competition events
  Therefore, with reference to the Dublin's Descriptors, after passing the exam, the student will be able to:
  DD2 - combine concepts, methods and theories of different field of physics and engineering to solve a physics problem; write simple codes to simulate physical problems of very different kinds; design, build and use experimental apparatus for physics experiments.
  DD3 - solve complex problems beyond the strict range of competences acquired in the Study Course in Engineering Physics
  DD4 - defend and discuss their solutions within the team and externally
  DD5 - learn from scientific literature, from experience and from peers

All aspects of experimental physics, in particular optics, electromagnetism in matter, mechanical properties of materials, fluid motion.
Interdisciplinary problem solving across physics and engineering will require dealing with new subjects.
Organization of teamwork.
Public presentation of original results.
The reference is to the IPT, International Physics Tournament, a yearly competition over about 15 problems to be studied from Septemebr to April. Teams that qualify for the final tournament defy other 15 to 20 teams from all over the World in a different city every year.

Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, senza presentazione/discussione finale
Valutazione continua obbligatoria
Continuous evaluation by tutors and professor during the 6 to 8 months of the course. The activities start in September and last until April, so they span two semesters. Evaluation is usually made in June, but upon motivated request it can be anticipated to February. It is advisable to choose this course, of First semester, and not the equivalent one in second semester, becaus activities start in September. It is not possible to start in February. The other course, of second semester, is a dummy for administrative reasons.

This soft-skills course aims to introduce foreign students to the fundamentals of Italian culture within the broader context of European history and culture. The course is divided in two units.
In Unit 1 (3 credits) students will learn about the main events in the history of Europe and Italy and about their socio-cultural implications, with special attention to contemporary issues. Students will also learn about key artistic and literary movements within this broad historical context.
In Unit 2 (2 credits), the focus will be on Italian contemporary literature, focusing on the potential intersection of literary research with pressing contemporary issues, such as ecology, mental health, and human and civil rights.
The goal of the course is to provide students with a deeper understanding of the social and cultural context in which they are studying and living and in which they might work in the future. Given the variety of topics and subjects, which will span art, literature and cinema, lectures will make considerable use of multimodal resources.

At the end of the course students will have developed an understanding of Europe's and Italy's cultural past and present, and will be able to use these competences to interpret current socio-cultural issues. A special focus on Italian history of the 19th-20th centuries will enable students to understand Italy’s cultural landscape and the basic structure of Italian society. They will also be able to understand and appreciate cultural artefacts in a culturally competent manner.
In Unit 2, the course will explore Italian contemporary literature, using debates and literary objects as a way to face pressing contemporary issues, such as ecology, mental health, and human and civil rights.
During the course, team-work will be frequently used to improve students' communication skills, especially in relation to topics they are not familiar with, from both a disciplinary and cultural viewpoint. Most lectures include an analysis of a specific cultural object: students will learn to apply analytical techniques to a variety of artefacts, and will eventually be able to use them autonomously. At the end of the course, students will be able to discuss in detail a single cultural object, discussing and assessing its main historical and formal features.

Unit 1 (3 credits)
A first series of lectures will focus on European history, from Ancient Grece up to the 19 th century; by learning about the foundations of European civilisation, focusing especially on its early times, international students will gain a better understanding of the contemporary western world. This historical background is a prerequisite for approaching the study of culture: the course will deal with the fundamentals of western literature, religion, art and cultural imaginaries. Moreover, the historical part will provide an overview of contemporary Europe both as an institution and a cultural entity.
The second part of this Unit is mainly dedicated to Modern Italy. Lectures will mainly deal with the history of Italy from unification (1861) to the present day. Students will have an opportunity to discuss crucial cultural problems, spanning politics, literature, cinema and local customs. Special attention will be paid to social imaginaries and narratives, which will be used as an entry point for a critical approach to the investigation of identities and cultural trends.
Unit 2 (2 credits)
In the second part, the course will explore Italian literature—with forays into cinema and the arts—focusing particularly on the thematic aspects of the reflections of major authors: Calvino’s ecological anxieties, Primo Levi’s unique blend of science and literary inquiry, and the distinct ways Ginzburg and Morante retell History. In short, the second part of the course will provide an opportunity to delve deeper into events and themes highlighted in the first part, using different approaches and languages.

Prova scritta facoltativa (a scelta dello studente), senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
The examination for 5-credit students is composed of three parts: a group presentation, a written test about unit 1 and an interview about the contents of the whole syllabus.
3-credit students will give the group presentation and sit the written test about unit 1 or, alternatively, an oral interview about the sylllabus of the same unit.
Attending students will study the content of the lectures based on the power point presentations and other materials uploaded on Beep, as well as on the compulsory references books (see references section). In gr
Non-attending students will study the recordings of the lectures, the power point presentations, the compulsory reference books and the other materials uploaded on Beep. Additionally, they are required to read the book Modern Italy. A Very Short Introduction by Anna Cento Bull (see references section).

Ernst Hans Gombrich, A Little History of the World, Editore: Yale University Press, Anno edizione: 2008, ISBN: 978-0-300-14332-4
Christopher Duggan, A Coincise History of Italy, Editore: Cambridge University Press, Anno edizione: 2014, ISBN: 978-0-521-74743-1
Note: Second Edition
Anna Cento Bull, Modern Italy. A Very Short Introduction, Editore: Oxford University Press, Anno edizione: 2016, ISBN: 978-0-19-872651-7
Jeremy Black, A Brief History of Italy, Editore: Robinson, Anno edizione: 2018, ISBN: 978-1-47214-088-3
John Foot, Modern Italy, Editore: Palgrave Macmillan, Anno edizione: 2003, ISBN: 978-0-333-66905-1
Zygmunt G. Baranski, Rebecca J. West, The Cambridge Companion to Modern Italian Culture, Editore: Cambridge University Press, Anno edizione: 2001, ISBN: 9780521559829

The course aims to provide students with both theoretical and practical competencies in the acquisition, processing, and interpretation of biosignals collected through wearable devices. Students will learn fundamental and advanced methods for handling signals such as ECG, EEG, EMG, PPG, and EOG, addressing the main challenges of real-world data such as noise, motion artifacts, and variability. Through lectures, laboratory work, and external seminars, students will develop the ability to design and implement signal processing pipelines, critically evaluate results, and understand current trends in wearable health monitoring.

1. Knowledge and Understanding (Dublin descriptor #1)
Understand the physiological origins and main characteristics of biosignals such as ECG, EEG, EMG, PPG, and EOG.
Comprehend fundamental and advanced methods for acquisition, preprocessing, and analysis of wearable biosignals.
Recognize challenges in real-world wearable signal analysis, such as motion artifacts, noise, and sensor variability.
2. Applying Knowledge and Understanding (Dublin descriptor #2)
Apply computational methods and algorithms to analyze biosignals using Python or MATLAB.
Design and implement complete signal processing pipelines for data acquired from wearable systems.
Evaluate data quality and select suitable preprocessing and feature extraction methods based on the signal characteristics.
3. Making Judgements (Dublin descriptor #3)
Critically assess the performance and limitations of different processing approaches.
Interpret results and justify methodological choices based on evidence and data characteristics.
Reflect on ethical and privacy issues related to the use of biomedical data from wearable devices.
4. Communication Skills (Dublin descriptor #4)
Present technical results clearly and effectively through oral presentations and written reports.
Collaborate efficiently in small research teams, sharing and discussing data and methodological decisions.
5. Lifelong earning Skills (Dublin descriptor #5)
Develop autonomous learning abilities for acquiring and integrating new signal processing techniques.
Critically engage with scientific literature and apply emerging methods to wearable biosignal analysis.

Introduction to biomedical and wearable signal processing
Overview of wearable systems: sensors, acquisition hardware, and data transmission
Physiological principles and signal characteristics of ECG, EEG, EMG, PPG, and EOG
Data acquisition and preprocessing techniques
Filtering, denoising, and motion artifact removal methods
Feature extraction, time–frequency analysis, and statistical signal modeling
Machine learning and data-driven methods for biosignal classification and interpretation
Multimodal data integration and synchronization in wearable systems
Laboratory sessions with real wearable data and project development
Seminars by external experts on emerging applications and research trends

Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
Valutazione continua obbligatoria
Evaluation is based on a group project involving the analysis of real biosignals acquired from wearable devices. Each group will design a complete processing pipeline and present the results through an oral presentation and a concise technical report. The assessment will consider methodological soundness, innovation, result interpretation, and clarity of presentation. Active participation in lectures, labs, and seminars will also contribute to the final grade, which reflects technical ability, analytical reasoning, and teamwork skills.

Machine learning deals with the problem of building programs that learn from experience. While artificial intelligence focuses on the representation of knowledge and the algorithms for reasoning about such knowledge, machine learning deals with the problem of extracting knowledge from experience gathered solving problems to be used to solve unseen instances of similar problems.
This course provides the theoretical and methodological foundation of machine learning methods and algorithms that are used in several application areas, including, Data Mining, Robotics, Autonomous Agents, Bioinformatics, Recommender Systems, Social Networks, etc.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn how to:
Model supervised learning and reinforcement learning problems
Train their models
Select the model complexity
Evaluate the performance of their models
Applying knowledge and understanding
Given specific project cases, students will be able to:
Model the learning problem
Select the most appropriate learning technique
Tune the hyperparameters of the studied techniques
Assess the performance of the learned model.
Making judgements
At the end of this course, students will be able to:
Correctly assess the performance of the proposed solutions
Be aware of fairness issues involved in the training of machine learning methods
Lifelong learning skills
Students will learn the trade-off between bias and variance and hot to manage it by selecting the proper complexity of the learning model
Students will learn to develop algorithms to solve relevant learning problems.

Introduction (1 class)
Basic concepts.
Learning theory. (4 classes)
Bias/variance tradeoff. Union and Chernoff/Hoeffding bounds.
VC dimension. Worst case (online) learning.
Practical advice on how to use learning algorithms.
Supervised learning. (8 classes)
Supervised learning setup. LMS.
Logistic regression. Perceptron. Exponential family.
Kernel methods: Radial Basis Networks, Gaussian Processes, and Support Vector Machines.
Model selection and feature selection.
Ensemble methods: Bagging, boosting.
Evaluating and debugging learning algorithms.
Reinforcement learning and control. (6 classes)
MDPs. Bellman equations.
Value iteration and policy iteration.
TD, SARSA, Q-learning.
Value function approximation.
Policy search. Reinforce. POMDPs.
Multi-Armed Bandit.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The assessment will be based on a written exam at the end of the course, where both theoretical competence and modeling skills will be tested.
Type of assessment
Description
Dublin descriptor
Written test
Solution of numerical problems: exercises on supervised learning and reinforcement learning problems
Solution of modeling problems: exercises where the student needs to properly model the machine learning problem and choosing the most appropriate solution technique
Theoretical questions with open answers.
1,2
1,2,3,5
1,2,5

Christopher M. Bishop, Pattern Recognition and Machine Learning, Editore: Springer-Verlag Berlin, Heidelberg, Anno edizione: 2006, ISBN: 0387310738 https://www.microsoft.com/en-us/research/people/cmbishop/prml-book/
Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, Editore: MIT Press, Cambridge, MA, Anno edizione: 2018, ISBN: 0262039249 http://incompleteideas.net/book/the-book-2nd.html
Murphy, K. P., Probabilistic Machine Learning: An introduction, Editore: MIT Press, Anno edizione: 2022 https://probml.github.io/pml-book/book1.html
Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, Mathematics for Machine Learning, Editore: Cambridge University Press, Anno edizione: 2020 https://mml-book.com/

This course introduces the fundamental principles, methods, and tools of Project Management, providing students with the knowledge required to plan, organize, execute, monitor, and control projects across a variety of organizational contexts. It offers a comprehensive understanding of the project life cycle, covering the core knowledge areas of project management, including scope, schedule, cost, resources, risk, communication and stakeholder management.
The course also examines project governance and the managerial competencies required to successfully deliver projects in increasingly complex and uncertain environments. Students will learn how to apply structured project management processes while developing an understanding of the strategic role of projects in organizational value creation.
Alongside traditional (predictive) project management approaches, the course introduces the principles of Agile Project Management. Students will explore Agile values and principles, iterative planning, Scrum, Kanban, and hybrid project management approaches, gaining an understanding of when predictive, adaptive, or hybrid methods are most appropriate.
The course combines lectures, practical exercises, case studies, and team-based activities to develop both analytical and practical project management skills.

Upon successful completion of the course, students will be able to:
explain the fundamental concepts, terminology, and principles of Project Management;
understand project governance mechanisms and the role of the project manager within different organizational settings;
understand the project life cycle and the main project management knowledge areas;
define project objectives, scope, deliverables, and develop a Work Breakdown Structure (WBS);
plan project schedules, estimate costs, allocate resources, and develop project budgets;
identify, assess, and manage project risks using both qualitative and quantitative techniques;
identify and manage project stakeholders and communication processes throughout the project life cycle;
monitor and control project performance using appropriate project management tools and techniques (EV);
explain the principles, practices, and artifacts of Agile Project Management, including Scrum, Kanban, and hybrid approaches;
evaluate the suitability of predictive, Agile, and hybrid methodologies according to project characteristics, uncertainty, and organizational context;
apply project management techniques to practical business cases through team-based exercises.

The course provides a comprehensive introduction to the principles, methodologies, and tools of Project Management across the entire project life cycle. It begins by examining the nature of projects, the role of project management in organizations, project governance, organizational structures, and the responsibilities and competencies of the project manager. The course then addresses the core project management processes, including project initiation and planning, scope definition, the development of the Work Breakdown Structure (WBS), scheduling techniques, cost estimation and budgeting, resource planning, quality management, procurement, and stakeholder engagement. Particular emphasis is placed on project risk management, covering both qualitative and quantitative approaches to risk identification, assessment, mitigation, and contingency planning, as well as project monitoring and control through performance measurement, project reporting, change management, and Earned Value Management. The final part of the course introduces Agile Project Management, presenting the principles of the Agile Manifesto and the most widely adopted Agile frameworks, including Scrum and Kanban. Students will explore iterative planning, adaptive delivery, Agile roles, events, and artifacts, while also examining hybrid project management approaches and the criteria for selecting predictive, Agile, or hybrid methodologies according to project characteristics and organizational context. Throughout the course, theoretical concepts are reinforced through practical exercises, case studies, and team-based project work, enabling students to develop both analytical and practical project management skills.

Prova scritta obbligatoria, senza prove in itinere
Student assessment is based on a written examination designed to evaluate both theoretical understanding and the ability to apply project management concepts and techniques. The written examination consists of three components: (i) a set of multiple-choice questions assessing the understanding of the fundamental concepts and methodologies presented during the course; (ii) one quantitative problem requiring the application of project management tools and analytical techniques; and (iii) two open-ended questions aimed at evaluating the student's ability to critically discuss and integrate the topics covered during the course.

Mauro Mancini, Costanza Mariani, QUANTITATIVE TOOLS FOR A SMART PROJECT MANAGEMENT, Editore: Esculapio, Anno edizione: 2021, ISBN: 9788893852555

The intelligence of any autonomous system is strictly related to its capabilities to sense the surroundings. Sensors thus are becoming a fundamental building block in several societal fields, from personal hand-held devices (smartphone, cameras, wearables...) to autonomous mobility (car, planes, drones...), from biomedical smart tools (imaging, cardio, speakers...) to space exploration (satellites, rovers...), from Industry 4.0 (robotics, surveillance...) to health/fitness (step counter...).
The course aims thus at introducing the basic concepts of different microelectronic devices that allow acquiring information from the physical world around us (microsensors), and performing actions on it (microactuators) at the microscopic and nanoscopic level. The focus is centered on MEMS (Micro-Electro-Mechanical-Systems) transducers, on optical CMOS (complementary-metal-oxide-semiconductor) pixel image sensors, on related driving/readout electronics, and on system-level considerations (e.g. noise-consumption-area trade-off, bounded/unbounded performance optimization, definition of the specifications for the different applications...).
The course is thaught with a balanced mix of theoretical classes (with realistic numerical examples and case studies), project-oriented exercises, and software-assisted exercises. Through the teaching of these technologies, sensors, and circuits, the course provides consistent examples of electronic sensing systems applied in several fields of the modern and future society (Internet of Things, Autonomous Driving, Smart City, Industry 4.0...).

At the end of the course:

• the student will be aware of the increasing needs for sensors that the society is having in this era, their benefits and their prospected evolution;
• according to DdD1 for Master Level courses, the student will have a comprehensive knowledge, both from a top view (system-level) and in details (subsystems design), about the design strategies of sensing and transduction systems based on microelectronic sensors (MEMS and CMOS technologies, in particular) and on electronic circuits;
• according to DdD1 for Master Level courses, the student will know the theoretical basis and equations that lie behind the design of a sensing system, the trade-off between the electronic domain (power, noise) and the sensor domain (area, dynamic range, linearity, bandwidth...); he/she will know if, when and why the aid of CAD simulations is needed; and he/she will know how to use the approriate softwares to develop such simulations;
• according to DdD1 for Master Level courses, the student will also be able to discuss the physical principles at the basis of the discussed variety of sensors (in particular, MEMS and CMOS), and to identify the pros and cons of the different approaches;
• according to DdD2 for Master Level courses, the student will be able to tackle the design of a sensing system (e.g. accelerometer, clock, magnetometer, gyroscope, light sensor...) starting from the specifications given by the target application (consumer, automotive, medical, industrial...); to co-develop then the sub-specifications for the sensor part and for the electronic part; and finally to design the sensor and the electronics according to the technologies discussed in the course;
• acciording to DdD 2 for Master Level courses, the student wil be able to choose among different technologies to solve a specific problem related to a sensing system similar to those discussed in the course;
• according again to DdD2 for Master Level courses, the student will know the basics of advanced software used in the design of sensing systems (e.g. use of multiphysics finite element modeling for sensors design, use of Cadence for electronic design);
• according to DdD2 for Master Level courses, the student will know the most significant parameters that characterize the sensing system behavior (scale-factor and its drift, linearity, full-scale range, offset and its drift, noise, power consumption, bandwidth, area and - last but not least - cost). She/he will also know how to practically measure them;
• according to DdD3 for Master Level courses, the student will be able to extend the concept studied on the categories of sensors discussed in the course to other categories, not discussed in the course which share the same set of problems (e.g. to microphones, pressure sensors, ultrasonic transducers, IR sensors...). To this purpose, specific exercise will be given, where the student is request to design sensing systems not discussed in the course. To stimulate this ability, the students will be left time for autonomous solving before the exercise is solved by the lecturer.

The course discusses topics related mostly to MEMS and CMOS microsensors, and the related driving/readout electronics.

• MEMS sensors and transducers are probably the most disruptive technology of the 21st century. They are rapidly changing our everyday life and they are at the basis of the Internet-of-Things forthcoming revolution. MEMS allow acquiring - in ultra-compact dimensions and high-performance - several physical quantities like acceleration, rotational speed, magnetic field direction and pressure. These devices are nowadays widely applied in automotive, avionics, health care instrumentation and consumer applications (mobile phones, tablets, portable devices). Other kinds of MEMS allow generating and detecting ultrasound waves or producing video images by deflecting light beams.
• Optical and infrared image sensors, mostly CMOS Active Pixel Sensors, allow getting color and multispectral digital images both in still and video cameras. The applications of these devices range from consumer to scientific, surveillance, medical and technical fields. They pervade our everyday life in cameras and mobile phones, with a so far neverending performance improvement.
  The target audience of the Course is represented by students oriented towards either:
  (i) advanced design of innovative micro-sensors, or
  (ii) advanced design of their low-noise, low-power integrated electronics for sensors, or finally
  (iii) advanced design of electronic systems embedding these sensors.
  The Course is split into 3 main sections, whose main topics are summarized in the following.
• MEMS sensors (about 40% of the Course).
  Building technologies of MEMS and NEMS (Nano Electro Mechanical Systems). Sensing and actuation elements based on capacitive, piezoelectric and piezoresistive effects. Inertial sensors: accelerometers, gyroscopes. Springs configurations. MEMS resonators and resonant mass sensors. Magnetic field sensors: MEMS, Hall and AMR magnetometers. MEMS based on membranes: ultrasonic transducers (CMUT and PMUT) and microphones. Time sensors (resonators, oscillators, clocks). Optical MEMS (micromirrors, microbolometers). Aspects of MEMS relaibility (fracture, adhesion, fatigue).
• Electronics for MEMS driving and signal acquisition (about 25% of the Course).
  Low noise–low power front-end electronics for accelerometers. Oscillators and sensing electronics for gyroscopes. Amplitude-gain-control in oscillators. Resonant and off-resonance circuit operation. High-performance characterization setups (rate tables, shakers,...) and electronics for MEMS. Driving and sensing electronics for ultrasonic devices.
• Digital image sensors (about 35% of the Course).
  Basics about the interaction of light with the semiconductors. CMOS-APS pixel detectors. General architecture and sensor performances: noise, dynamic range, nonlinearities, frame rate. Main acquisition technques: 4T circuits and CDS (Correlated Double Sampling). HDR (High Dynamic Range) architecture. Backside illumination technologies.
  For each sensor topology, realistic case studies will be discussed through numerical exercises and comparisons to existing cutting-edge products. Some activities are also foreseen concerning the application of CAD software to the design of high-performance MEMS sensors. The software is licensed to PoliMi students. 2-3 seminars will be given by invited indusrial members about hot topics for the sensors community.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
Evaluation will be carried on so to understand whether the student has achieved the expected results, listed above. To this aim, the exam is formed by:
1 - a written exam, split into three parts
a) one theoretical question, whose aim is to verify the student comprehension of the physical principles behind the sensors and/or to verify her/his capability in making choices among different system topologies and/or to verify his capability in taking decisions and being convincing, and/or to verify her/his knowledge of the industrial state of the art and need (according to DdD1, DdD4 and DdD5);
b) one numerical exercises on design of sensing systems, whose aim is to verify how the student practically turns design considerations into a quantitative, optimized, system design, accounting for the trade offs and the boundary conditions (according to DdD2);
c) a second numerical exercises on design of sensing systems, whose aim is to stimulate the applications of the concept studied in the course to similar domains, e.g. other sensing systems, or systems for the same physical quantity but based on other technologies (according to DdD3);
The written exam duration will be 3 hours, and the maximum grade that can be achieved with the written exam is 30/30 (no cum laude).
2 - the discussion of the exam, and facultative oral exam
Students will be given the possibility to discuss the exam correction together with the lecturers. This will allow to understand more in depth the student awareness of the topics discussed in the written exam, and thus to deepen its evaluation. This procedure also aims at identifying possible misunderstandings, and thus may give rise to partial amendment of the grade.
There is then the possibility to take a facultative oral exam (approximate duration is 10-15 minutes) only for students with a grade equal or larger than 26. The oral exam can increase or decrease the grade, typically by 1 to 3 points. Oral questions often deal with problems that go beyond the course content, but that can be discussed by the students that have a comprehensive knoledge of the course topics.
The aim of the additional oral exam is to verify more in depth the knowledge acquired by the top-10% of the class, and gives the possibility to grant 30 cum laude for the students that really demonstrate advanced system design capabilities.
No intermediate (in itinere) exam sessions are foreseen. However, the new didactic approach stimulates a continuous self-judgment of the student preparation.

V. Kempe, Inertial MEMS: principles and practice
C. Akar, A. Shkel, MEMS vibratory gyroscopes
G. Langfelder, A. Tocchio, MEMS integrating motion and displacement sensors, Editore: Woodhead publishing
Note: Chapter 13 of the book: "Smart Sensors and Mems"

Aims and scope
The course aims to derive the macroscopic properties of gaseous systems starting from
their microscopic (molecular dynamics) analysis.
In this perspective, analytical and numerical methods for the solution of kinetic equations
are presented.

Knowledge and understanding
At the end of the course the students are expected to:
-know the basic mathematical properties of the Boltzmann and related kinetic equations
-be able to solve kinetic equations both analytically and numerically
-be able to model various problems in science

Program
1-Introduction to Statistical Mechanics

• The postulate of classical statistical mechanics.
  -Microcanonical Ensemble.
  -Canonical Ensemble.
  -Grand Canonical Ensemble.
  2-The Boltzmann equation
• Derivation (Lanford Theorem). Existence of solutions (Diperna-Lions Theorem).
  -The BBGKY Hierarchy.
  -Elementary properties of the collision operator.
  -Boundary conditions and the gas-surface interaction.
  -The H-Theorem and the problem of irreversibility.
  -Kinetic Models.
  3-Methods of solution of the Boltzmann equation
• Variational Methods.
  -Transformation into Integral Equations.
  -Numerical methods.
  4-Quantum kinetic equations
  -The Boltzmann-Nordheim equation.
  -Equilibrium states and the H-Theorem.
  -Quantum BBGKY-like Hierarchies.
  -Kinetic theory of Bose-Einstein condensation.
  5-Application of the Boltzmann equation to Microfluidics
• Analytical and numerical investigation of microchannel flows (Poiseuille, Couette and
  Thermal-Creep problems; sound waves propagation).
  -Slip boundary conditions for the Navier-Stokes equations.
  -Kinetic modeling of gas mixtures.
  6-Mathematical Models and Methods in Micro-Nano-Technologies
  -Characterization of Micro-Electro-Mechanical Systems (MEMS) devices.
  -Analysis of damping forces in low-frequency and radio-frequency MEMS devices.
  7-Kinetic equations for Biomedical applications
  -The Smoluchowski equation.
  -Two-scale homogenization.
  -Mathematical models of neurodegenerative diseases.

Prova scritta facoltativa (a scelta dello studente), senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
Examination
The final exam consists either in an oral discussion on the whole program,
or in a written project in which one of the topics, dealt with during the course,
is investigated thoroughly.

Kerson Huang, Statistical Mechanics, Editore: John Wiley & Sons, Anno edizione: 1987, ISBN: 978-0-471-81518-1
Carlo Cercignani, The Boltzmann Equation and Its Applications, Editore: Springer-Verlag, Anno edizione: 1988, ISBN: 3-540-96637-4
Carlo Cercignani, Rarefied Gas Dynamics: From Basic Concepts to Actual Calculations, Editore: Cambridge University Press, Anno edizione: 2000, ISBN: 0-521-65992-2

This course aims at providing essential knowledge on human neurosensory systems from a biomedical engineering point of view. Methods focus on physiological and anatomical studies of the mammalian nervous system as well as behavioral studies in animals and humans. The material is expected to provide a concise, solid platform of knowledge on specific sensory pathways of the human neural system, and to provide the student with the basic tools to begin the journey of discovery and learning into the field of Computational Neuroscience. The course will further provide benchmark knowledge on state-of-the art technology aimed at replacing specific sensory mechanisms and at diagnosing pathologies related to their functions.
The course is divided in two parts:
PART 1: Methods. The first lectures present the general structure and functional anatomy of the neurosensory systems, including the neuron, neural networks and brain organization. A second section highlights methodological representations of the basic physiology of the ear, as well as transduction, transmission and neural processing of the auditory stimulus. The course further presents a methodological portrayal of the basic physiology of the eye, as well as transduction, transmission and neural processing of the visual stimulus. Basic mechanisms related to visual pattern, color and depth perception, and spatial localization are also introduced. Finally, a brief overview of the somatosensory, olfactory, and gustatory systems is provided.
PART 2: Technologies. The first lectures of this part provide basic concepts on advanced signal processing techniques. Then, basic concepts in psychoacoustics, including speech fundamentals, and visual psychophysics, including color coding and color perception, are introduced. The course further highlights critical elements that have contributed to the technological knowledge towards the realization of diagnostic devices and auditory aids, including recording evoked potential techniques. Technological advances towards the realization of diagnostic devices and visual aids are also introduced.

Students are expected to gain a solid platform of knowledge on how the human neural system is organized and how it functions, on the basic mathematical principles underlying the system’s physiology, and on state-of-the art technology aimed at replacing specific sensory mechanisms and at diagnosing pathologies related to their functions.
More specifically, students will be able to understand basic principles on:
Understanding the brain: from the neuron to the central nervous system. (DD1)
Physiology of the ear and transduction, transmission and neural processing of the auditory stimulus. (DD1)
Physiology of the eye and transduction, transmission and neural processing of the visual stimulus. (DD1)
Key concepts related to visual pattern, color and depth perception, and spatial localization. (DD1)
Physiology of the somatosensory, olfactory, and gustatory systems. (DD1)
How to apply technological concepts and advanced signal processing techniques towards the realization of diagnostic devices and auditory aids. (DD2)
How to apply technological concepts and advanced signal processing techniques towards the realization of diagnostic devices and visual aids. (DD2)
How to identify specific classes of common problems in the different systems or organs, at various scales of investigation. (DD3)

PART 1
General structure and functional anatomy of neurosensory systems. The Neuron and Neural Pathways. Functional investigation methods. Introduction to modeling of neurosensory systems.
Highlights on the auditory system. Sound and speech. The external and middle ear. Cochlea, basilar membrane and ciliate cells. The spiral ganglion and the VIII nerve. Brain stem nuclei, middle geniculate, and auditory cortex.
Highlights on the visual system. Remarks on optics. Structure and function of the retina. Chiasm and lateral geniculate body. Structure and feature extraction in the primary and secondary visual cortex. Dorsal and ventral integration areas.
Brief overview of the somatosensory, olfactory, and gustatory systems.
PART 2
Basic elements of neurosensory signal processing.
Auditory psychophysics and audiology. Evoked potentials and otoacoustic emissions. External prostheses, implantable devices and cochlear implants. Speech processing: brief remarks. eHealth and Hearing: novel solutions and mobile apps. Auditory diagnostics and hearing effort assessments.
Visual psychophysics. Color perception and coding. Visual illusions. Visual evoked potentials. Ophthalmological instrumentation. Eye tracking and Pupil Dynamics Analysis. Aids for visual impairment. Echography. Basic principles of Laser surgery. Overview on retinal implants.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente) o condizionata (a scelta del docente)
Students are expected to pass two written tests, focused on both open and multiple-choice questions related to PART 1 and PART 2 respectively. At each call, the student may choose to take the exam relative to either both Parts of the Integrated Course or one of the two Parts, provided that he/she passes both Parts in one of the sessions of the academic year of attendance. The exam can be finalized only if BOTH written exams are passed, and the final grade will be the average of the two grades. A further Oral Exam is OPTIONAL, unless specifically required by the teacher. If taken, the oral will contribute to 30% of the final grade.

Riccardo Barbieri, Giuseppe Baselli, Ferdinando Grandori, Alessia Paglialonga, Bioengineering of Neurosensory Systems, Editore: Pàtron Editore, Anno edizione: 2023, ISBN: 9788855535977

Obiettivo dell’insegnamento è permettere agli studenti di acquisire competenze di progettazione di sistemi elettronici basati su microcontrollori, sia per quanto riguarda la progettazione hardware di schede che per lo sviluppo del firmware (in Assembly) e del software di più alto livello (in linguaggio C). L’insegnamento presenta le architetture interne dei microcontrollori ad 8 bit di Microchip e di STMicroelectronics.
Oltre alle lezioni teoriche su architetture, memorie e periferiche interne (12 ore in aula), l’insegnamento verterà su esercitazioni pratiche svolte in modalità laboratorio informatico (14 ore) per lo sviluppo di firmware e routine e su laboratori sperimentali pratici di tipo “hands-on” (24 ore), in cui ogni singolo studente utilizzerà una scheda di sviluppo a microcontrollore, ambiente CAD di sviluppo, e linguaggi di programmazione (assembler e C). In particolare verranno impiegate schede di sviluppo di MikroElektronika, basate su microcontrollori PIC della Microchip.
Nei laboratori pratici, gli studenti saranno divisi in sezioni di circa 30 studenti; ogni sezione frequenterà un laboratorio di 2 ore e mezza consecutive a settimana (a scelta tra i giorni previsti nel calendario), per circa 2 mesi, ovvero un totale di 10 laboratori di 2:30 ore ciascuno, per un totale di 24 ore di laboratorio sperimentale pratico, per ciascuno studente.
Dei 5 CFU dell’insegnamento, 3 CFU saranno erogati in modalità Didattica Innovativa di tipo “Blended Learning” e “Flipped Classroom”, in cui lo studente sarà coinvolto attivamente nelle esercitazioni e nelle attività pratiche di laboratorio sperimentale, mentre l’esercitatore ed i responsabili di laboratorio supervisioneranno lo studente durante lo svolgimento degli esercizi di programmazione Assembly ad esercitazione e di linguaggio C in laboratorio.

Conoscenza e Comprensione (Descrittore di Dublino #1)
Al termine del corso e dopo aver superato l’esame, lo studente:
saprà illustrare le architetture ed il funzionamento delle periferiche dei microcontrollori, comprenderà come programmarle e saprà confrontare le prestazioni tra microcontrollori diversi;
saprà scegliere gli strumenti più adeguati (sistemi di sviluppo, IDE, linguaggi di programmazione) per progettare un sistema elettronico basato su microcontrollore;
saprà selezionare il microcontrollore adatto al sistema elettronico da sviluppare, consultando il data-sheet di diverse famiglie di prodotti e case costruttrici;
saprà programmare in Assembly ed in linguaggio C i microcontrollori ad 8 bit di Microchip;
Capacità di Applicare Competenza e Comprensione (Descrittore di Dublino #2)
Al termine del corso e dopo aver superato l’esame, lo studente:
sarà capace di comprendere i data-sheet di microcontrollori commerciali e valutarne l’impiego;
comprenderà quali specifiche del microcontrollore impatteranno sulle prestazioni di sistema;
saprà progettare circuiti a microcontrollore, programmandoli e debuggandone il firmware, per individuarne problemi e risolverli;
saprà applicare soluzioni innovative all’hardware e al firmware di sistemi a microcontrollore.
Autonomia di Giudizio (Descrittore di Dublino #3)
Grazie alla modalità di Didattica Innovativa di tipo “flipped classroom”, lo studente:
acquisirà l'autonomia di giudizio nell’analizzare e nel progettare l’hardware ed il firmware di una scheda a microcontrollore, valutando pro e contro delle varie implementazioni possibili.
Abilità Comunicative (Descrittore di Dublino #4)
Grazie al lavoro di gruppo in piccoli team di 3-4 persone presente nei laboratori, lo studente
acquisirà competenze comunicative efficaci e capacità di autogestione nel lavoro di gruppo che favoriranno la condivisione di idee, il confronto costruttivo e la collaborazione nella risoluzione di problemi tecnici.
Capacità di Apprendimento (Descrittore di Dublino #5)
Grazie alla modalità di Didattica Innovativa di tipo “flipped classroom”, lo studente:
imparerà ad apprendere in autonomia i miglioramenti introdotti da microcontrollori di futura generazione e i vantaggi di nuove implementazioni firmware;
capirà come affrontare studi successivi di prodotti e soluzioni progettuali che diverranno disponibili durante la sua attività professionale.

Microcontrollori (12 ore di lezione)
Microcontrollori a 8bit: architetture, data-sheet di prodotti commerciali, famiglie e prestazioni.
Memorie dati (RAM e flash) e programmi (ROM, OTP, E2PROM, flash) e registri di configurazione.
Periferiche interne: porte di I/O, counter, timer, convertitori Analogico-Digitale (ADC) e Digitale-Analogico (DAC), modulazione impulsiva (PWM), comunicazione seriale.
Circuiti ausiliari: reset, oscillatore, interrupt, watchdog.
Firmware e software (14 ore di laboratorio informatico)
Schemi circuitali del sistema di sviluppo utilizzato a laboratorio e ambiente di simulazione.
Linguaggio Assembly: istruzioni, cicli macchina, sviluppo di codice.
Linguaggio C: esempi di codice, compilazione, portabilità.
Esempi pratici: modalità di indirizzamento; gestione banchi di memoria; gestione periferiche; routine di interrupt.
Progetti pratici “hands-on” (24 ore di laboratorio sperimentale)
Esempi pratici con scheda di sviluppo dedicata a ciascun studente e ambiente di sviluppo.
Gestione di ingressi/uscite digitali e ingressi analogici; pilotaggio di relè e LED; interfacciamento con memorie e DAC/ADC esterni; gestione degli interrupt; gestione tastiere e display:
Temporizzazione mediante timer interni; polling, loop e interrupt; modalità low-power e sleep.
Comunicazioni seriali: SPI, I2C, RS232; gestione memorie e ADC e DAC esterni e altri dispositivi.
Schermi LCD grafici: interfacciamento; temporizzazioni; data-sheet di prodotti commerciali.

Prova scritta obbligatoria, senza prove in itinere
Altro
L’esame consisterà di due parti, svolte consecutivamente durante la stessa giornata:
Parte “teorica”: durata 30 minuti, strutturata con 14 semplici domande a risposta multipla su Microsoft Forms; se si risponderà correttamente ad almeno 9 domande si verrà ammessi alla parte laboratoriale. La correzione sarà istantanea e generata automaticamente da Forms. Questa parte contribuirà a 7/30 del voto finale (ad esempio, rispondendo correttamente a 12 domande si partirà da un voto finale di 12/14·30/7 = 4 trentesimi). In questa prova lo studente verrà valutato sulle architetture di base del microcontrollore, snippet di codice, schemi circuitali utilizzanti microcontrollori e codice/codifiche utilizzate nel codice C. (DD1)
Parte “laboratoriale” (solo per coloro che avranno superato la parte “teorica”): durata 2:30 ore, concernente la progettazione di un firmware (in C). Questa parte contribuirà a 23/30 del voto finale. La prova sarà strutturata in più sotto sezioni, per ciascuna si dovrà consegnare un archivio contenente l’intera cartella del progetto MikroC. Durante l’esame verrà messa a disposizione la scheda EasyPIC a ciascun esaminando, per collaudare il firmware. In questa prova lo studente verrà valutato sulla base delle sue capacità di prendere decisioni in autonomia sul progetto proposto, scegliere gli strumenti più adatti per portare a termine il lavoro, correttezza e funzionalità del firmware sviluppato insieme alla correttezza del codice C utilizzato. (DD1, DD2, DD3) Il materiale liberamente consultabile è il seguente:
File inizializzazione LCD (lcd.c);
File simulatore proteus (EasyPICVx.rar);
HRLV – MaxSonarDatasheet;
HRLV MaxSonar – EasyPIC;
Datasheet PIC18f45K22;
Schematico EasyPIC (SchematicEasyPIC.pdf).
Le consegne avverranno telematicamente tramite Microsoft Forms. La correzione avverrà in differita ed i risultati saranno pubblicati alcuni giorni dopo la prova.
Lo studente potrà decidere di congelare il voto della parte "teorica" anche per gli appelli successivi, ma dello stesso anno accademico, per poi rifare solamente la parte "laboratoriale" allo scopo di migliorare il voto.
L'esame si intenderà superato se il voto finale sarà uguale o superiore a 18/30.

Franco ZAPPA, MICROCONTROLLERS, Editore: Esculapio, Bologna, Anno edizione: 2017, ISBN: 978-88-9385-022-3

The course illustrates the unit processes needed to realize an integrated circuit on silicon substrates. Beside acquiring familiarity with the unit processes physics and tools, the students will also learn how to arrange those basic steps into a process flow sequence. A simple CMOS circuit process flow will be analyzed.

Knowledge and undertsanding (Dublin descriptor 1):
After the course, the students will:
-Know the basic unit processes for an integrated circuit fabrication
-Understand the physics of the single process steps
-Understand the role of each process step in a CMOS and in a memory process flow
Applying knowledge and understanding (Dublin descriptor 2):
After the course, the students will:
-Be able to choose the best technique for each unit process step, based on the technology node and process flow requirements
-Understand how unit process properties must evolve as a function of technology node scaling
-Be able to predict the impact of a unit process step on the whole device process flow and on a device electrical performances

Introduction
-Semiconductor industry history
-Semiconductor physics review
Process flow (1)
-Process flow for a CMOS device
Silicon

• Crystallography review (Bravais lattice, crystalline structures)
  -Silicon lattice
• Defects and thermodynamics of point defects
  -Mono-crystalline silicon growth (CZ)
  -Silicon wafer fabrication process
  Silicon oxidation
• Silicon oxide structure
• Deal and Grove model
  -Oxidation kinetic dependence on substrate orientation and doping
• Oxidation and point defects
• Charged defects in silicon oxide and CV measurements
• Advanced silicon oxidation techniques
• High-k dielectrics
  Dopant diffusion in silicon
• Fick’s law
• Analytical solutions to second Fick’s law (Gaussian and ERFC profiles)
• Correction to Fick’s law
• Diffusion and point defects
  Ion implantation
• Ion implanter structure
• Implanted concentration profiles, channeling effects
• Nuclear and electronic ion stopping
• Crystallographic damage and TED
  Lithography
• Lithogrpahy basics concepts and main lithographic system architectures
  -Light sources
  -Reticles
  -Photo resist
  -Registration
• Projection lithography;
• Resolution and depth of focus
  -Advanced applications (immersion lithography, EUV, e-beam, self-aligned double patterning, self assembling block-co-polymers)
  · Thin films deposition: CVD and PVD techniques
  -Fluid dynamics basics
  -APCVD and LPDCVD basic models
  -Plasma-assisted CVD techniques
  -Evaporation
  -Sputtering
  Etching
  -Wet etching quick overview
  -Plasma ecthing
• Main properties
• Plasma etchers
• Etch chemistries
• Basic models
  Interconnects
• Local interconnects: silicides and salicides
• Contacts
  *Ohmic vs rectifying behavior
  *Main contact technologies
• Dielectrics: oxides and low-K materials
• Interconnects: subtractive and damascene approach
  Packaging and advanced packaging
  Text book
  D. Plummer and P. B. Griffin, Integrated Circuit Fabrication: Science and Technology. Cambridge University Press, 2023.

Prova orale obbligatoria
The final exam consists of an oral interview (NO WRITTEN TEST).
During the oral interview, every student will be asked three questions slected randomly among the course topics. The questions purpose will be to understand if the student has acquired the proper knowledge of signle process steps physics and their role in an integrated cirucit process flow (Dublin descirptor1), and to verify that the students can autonomously estimate the impact of each step on the entire process flow as welll as the challenges related to technology scaling.
A mark between 0/30 and 30/30 will be assigned for each question. If a 30/30 mark is assgned for each question and for the excercise, the final mark will be 30/30 cum laude.

Plummer, J. D.; Griffin, P. B., Integrated Circuit Fabrication: Science and Technology., Editore: Cambridge University Press., Anno edizione: 2023
James D. Plummer, Michael Deal, Peter D. Griffin, Silicon VLSI Technology: Fundamentals, Practice, and Modeling, Editore: Prentice Hall, Anno edizione: 2001

This course aims at showing some techniques, based on electromagnetic radiation, for the evaluation of different properties of the materials through a practical approach. This means that the general theory behind the techniques is discussed; then the instrumentation, the sample preparation and the practical applications of the techniques are considered. An important goal is to provide the tools for the choice of the sutiable technique (in accordance to the target property) and for the interpretation of the IR spectra of materials and molecules. The course aims also at providing the knowledge of the optical properties of materials as function of their structure with also a practical activity related to the measurement of these properties in different samples.

At the end of the course and after the examination, the student should:
Know the optical properties of materials (dispersion, scattering, absorption) and their differences according to their structure, e.g. the optical behavior of conductive and dielectric materials;
Know the basic theory of the different characterization techniques starting from the simple models used to describe the material;
Know the structure and the main elements of the instrumentation peculiar of the different characterization techniques also considering the methods and issues related to the sample preparation;
Be able of choosing the suitable characterization techniques as function of the target property to be measured and the main features of the sample and apply these knowledge starting from the thesis work;
Be able to interpret an IR spectrum in terms of main chemical groups present in the molecule/material under study;
Be able to use simple instrumentation for the measurement of optical properties of materials (refractometer, spectrometer).

The course deals with principles and applications of widespread modern methods for material characterization. For each technique considered, the basic principles, the methods of interpretation will be shown, paying also attention to the instrumentation and sample preparation. The aim of the course is to provide tools and clarify what structural information can be obtained from the different techniques; what is the range of applicability and what are the possible options for measuring specific properties.
Table of contents (from this following list, 3-4 topics will be discussed and deepened according to the class composition and the students' learning activities).
INTRODUCTION TO SPECTROSCOPIC METHODS: Electromagnetic (EM) waves and propagation in vacuum. EM weaves-matter interaction: reflection, refraction, transmission and absorption. Energy levels in materials: quantized transitions. Elastic and inelastic scattering processes. Diffraction. Basic elements and properties of a spectroscopic instrumentation; design of a spectrograph.
OPTICAL PROPERTIES: refractive index in dielectric and conductive materials, techniques for its measurement.
ELECTRONIC SPECTROSCOPY: absorption, fluorescence, reflection electronic spectroscopy of atoms and molecules.
VIBRATIONAL SPECTROSCOPY: Normal modes of vibrations in molecules and crystals. Infrared and Raman spectroscopy.
X RAY SPECTROSCOPY: Laue approach. Reciprocal lattice, intensity analysis: Structure Factors and Electron Density.
SPIN SPECTROSCOPY: nuclear magnetic resonance (NMR) spectroscopy, electronic spin resonance (ESR) spectroscopy.
MICROSCOPY: Optical microscopy, electronic microscopy, scanning probe microscopy (SPM).
THERMAL ANALYSIS OF MATERIALS: differential scanning calorimetry (DSC), thermal gravimetric analysis (TGA).
Laboratory activity:
For each technique discussed there will be a laboratory activity to practise with the instrumentation, sample preparation and data analysis.

Prova orale obbligatoria
The examination is an oral discussion covering the course topics, chosen by the examiner. Usually, there is a general question related to one key topic, then other more focused questions.
The student has to answer to the general questions contextualizing the topic, showing the key features with the help also of equations, models, schemes. Moreover, the discussion of details is welcome as well as the connections with other topics that are part of the program. The student has also to solve simple numerical exercises to support the discussion of the general topics. There is also an exercise related to the interpretation of the infrared spectra where the candidate has to assign the chemical structure to the corresponding spectrum.

The goal of the course is to enable students to extract (“learn”) from measured data useful information. The course covers advanced topics of data-driven black-box system identification and virtual sensing, with focus on dynamical systems. Examples of AI-based variable estimation are presented.

Through theoretical lectures and numerical-exercises sessions, the students are expected to:
Understand the fundamental problems that can be encountered while extracting information from a finite set of observations taken from an unknown system [Dublin Descriptor 1]
Learn the main tools of system identification and virtual sensing. [DD1, DD2]
Be able to formulate identification problems corresponding to different applications. [DD3, DD5]
Understand a range of algorithms along with their strengths and weaknesses. [DD1, DD3]
Be able to apply suitable algorithms to solve application problems. [DD2, DD3]
Be able to read current research papers and understand the issues raised by current research in the field. [DD5]

The program of the course is as follows.
Non-parametric system identification: the subspace-based state-space approach
Frequency domain parametric identification of models from data
Kalman Filter: prediction, virtual-sensing and gray-box system identification
Analysis and design of closed-loop systems using the minimum-variance approach
Non-linear system-identification and SW-sensing: parametric nonlinear fitting; N-ARMAX models; Examples of AI-based variable estimation are presented.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i condizionato (a scelta del docente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The exam will be a written test in one of the official available dates. The exam will consist of both theoretical questions and simple (i.e., no computer-aided) exercises. The final marks will be awarded based on correctness of the answers, appropriateness of technical terminology, and clarity of exposition.
The written exam is designed to assess the achievement of the following Dublin Descriptors:
DD1 – Knowledge and understanding: students will be required to demonstrate a solid understanding of the theoretical foundations of system identification and data-driven modeling, including stochastic modeling, parametric and non-parametric identification methods, and the main issues arising when extracting information from finite data sets, together with the corresponding countermeasures.
DD2 – Applying knowledge and understanding: students will be asked to apply suitable identification, estimation, and analysis methods to solve system-identification problems of moderate complexity, through analytical reasoning and simplified exercises, and to correctly interpret the obtained results.
DD3 – Making judgements: students will be evaluated on their ability to critically analyze an identification problem, to select and justify the most appropriate modeling and identification tools for a given application, and to discuss the strengths and limitations of alternative approaches.
DD5 – Lifelong learning skills: through questions requiring synthesis and critical interpretation, students will demonstrate their ability to reason on advanced methodologies and to frame specific problems within a broader system-identification context, showing the conceptual tools needed to approach new methods and current research literature.

Sergio.M. Savaresi, Lecture notes
Sergio Bittanti, Model Identification and Data Analysis, Editore: Wiley, Anno edizione: 2019
P. van Overschee, B.L. de Moor, Subspace Identification for Linear Systems: Theory, Implementation, Applications, Editore: Kluwer Academic Publishers, Anno edizione: 1996 ftp://ftp.esat.kuleuven.be/pub/SISTA/ida/reports/96-26a.pdf
Note: Freely available online.
L. Ljung, System Identification: theory for the user (II ed.), Editore: Prentice-Hall, Anno edizione: 1999
T.S. Soderstrom, P.G. Stoica, System Identification, Editore: Prentice-Hall, Anno edizione: 1989

The course develops knowledge and skills for advanced modelling, management and control of natural resources systems. The emphasis will be on the operational aspects of natural resources modeling and management, with focus on water resources systems and real world case studies. Topics include: modeling for management, uncertainty and sensitivity analysis, stochastic and robust optimal control, approximate methods, real time control, and complexity reduction methods. The course is aimed at graduate students preparing to work in environmental and water resources engineering field.
Course delivery and contents: Modelling for management, Optimal planning, Approximate Dynamic Programming and Reinforcement Learning; direct use of exogenous information; Real time control: deterministic and stochastic Model Predictive Control; deterministic simulation; MonteCarlo simulation. Dimensionality and complexity: non dynamic emulation modeling (iterative Response Surface); model reduction/dynamic emulation modeling; objective reduction; from multi to many objective; multidimensional visualization tools.

The course provides students with the physical foundations required to understand, critically assess and select nuclear techniques for advanced materials characterization.
Emphasis is placed on understanding the physical principles governing each technique, the type of information that can be extracted, the achievable sensitivity and spatial resolution, and the strengths and limitations of each method. Particular attention is devoted to the comparison of complementary techniques and, where appropriate, to their relationship with widely used non-nuclear characterization methods, in order to highlight the specific information uniquely provided by nuclear techniques.
Complementary classes of techniques based on nuclear reactions, ion beams, neutrons, hyperfine interactions and positron annihilation are presented, highlighting the information provided by each method, their capabilities and their limitations in materials characterization.
An introductory review of the nuclear physics concepts required for the subsequent topics is included, making the course accessible to students from different educational backgrounds.
The final part of the course adopts an active learning approach based on Blended Learning, in which students critically analyse recent scientific papers illustrating advanced applications of the techniques discussed during the lectures.

The student
knows the physical principles underlying the main nuclear techniques for materials characterization and the type of information provided by each technique (DdD1);
knows the capabilities, limitations and complementarity of the different techniques, including their sensitivity, spatial or depth resolution, and typical fields of application (DdD1);
is able to select and critically compare the most appropriate technique, or combination of techniques, for a given materials characterization problem (DdD2);
is able to interpret the scientific literature employing nuclear techniques for materials characterization and discuss their applications using appropriate scientific terminology (DdD2).

Module 1 – Introduction to materials characterization and nuclear techniques
Role of materials characterization techniques in materials science
Information accessible through different characterization methods (elemental composition, depth profiling, crystal structure, local atomic environment, point defects)
Overview of nuclear techniques for materials characterization and their complementarity with other characterization methods
Module 2 – Physical foundations
Review of the physical concepts underlying the techniques presented in the course:
collisions
multipole expansion of electromagnetic fields and interaction of multipoles with electromagnetic fields
magnetic dipoles in magnetic fields
X-ray emission: continuous and characteristic X-ray spectra
interaction of X-rays and gamma rays with matter: scattering and absorption
For students without previous background in nuclear physics, the introductory lectures also provide the fundamental concepts of nuclear structure, radioactivity and nuclear reactions needed for the subsequent modules.
Module 3 — Photon-based techniques
Physical principles of X-ray emission
X-ray Fluorescence (XRF)
Particle-Induced X-ray Emission (PIXE)
Comparison between XRF and PIXE
Information provided, capabilities, limitations, detection limits and typical applications
Module 4 — Ion Beam Analysis techniques
Rutherford Backscattering Spectrometry (RBS)
Elastic Recoil Detection Analysis (ERDA)
Nuclear Reaction Analysis (NRA)
Particle-Induced Gamma-ray Emission (PIGE)
Integration of complementary Ion Beam Analysis techniques
Information provided, capabilities, limitations, depth resolution and typical applications
Module 5 – Neutron-based techniques
Neutron Activation Analysis (NAA)
Neutron diffraction
Inelastic neutron scattering
Comparison with X-ray diffraction
Information provided, capabilities, limitations and typical applications in structural and dynamical characterization of materials
Module 6 – Nuclear spectroscopies and positron-based techniques
Hyperfine interactions
Mössbauer spectroscopy
Nuclear Magnetic Resonance (NMR)
Positron Annihilation Spectroscopy (PAS): positron lifetime spectroscopy; Doppler broadening spectroscopy.
Comparison of complementary techniques
Information provided, capabilities, limitations and typical applications for the characterization of local atomic environments, vacancy-type defects and free volume
Module 7 – Applications and case studies (Blended Learning and active learning)
Critical analysis and discussion of recent scientific papers
Student presentations
Selection and critical comparison of techniques for specific materials characterization problems
Integration of complementary characterization techniques

Prova orale obbligatoria
Assessment consists of an oral examination aimed at evaluating the student's understanding of the physical principles underlying the techniques presented in the course, their capabilities and limitations, and their applicability to specific materials characterization problems.
Students are expected to discuss and compare different nuclear techniques for materials characterization, justify the choice of the most appropriate experimental approach for a given investigation, and critically interpret examples drawn from the scientific literature.
Students participating in the Blended Learning activities through the presentation of a scientific paper will have this activity taken into account in the overall assessment.

H.R. Verma, Atomic and Nuclear Analytical Methods: XRF, Mössbauer, XPS, NAA and Ion-Beam Spectroscopic Techniques, Editore: Springer, Anno edizione: 2007, ISBN: 3-540-30277-8
Note: Covers several nuclear techniques. - Available as E-book at Politecnico library
editor-in-chief: John C. Lindon, Encyclopedia of Spectroscopy and Spectrometry, Editore: Elsevier, Anno edizione: 2000, ISBN: 978-0-12-226680-5
Note: Note: Available as E-book at Politecnico library

The course aims to provide a deep knowledge of modern optical and optoelectronic measurements. It starts with a review of LASER principles and measurements of main optical parameters of LASER light (spectrum, power, coherence, beam profile), to proceed with applications of optical instrumentation and measurements in scientific and technological fields. The class will study measurement methods and electro-optical systems for electronic material and device characterization (e.g., optical spectroscopy and laser interferometry), optical methods and instrumentation for optical communications, and electro-optical instrumentation for automotive and industrial applications, including optical telemeters, interferometers, laser vibrometers, and velocitmeters.

We expect that the student will be able to understand and solve problems related to optical instrumentation and measurements.
In particular, the student will develop the ability to design and use practical optical telemeters, interferometers, and velocimeters, and understand the capabilities of optical laboratory instrumentation (optical power meter, optical spectrum analyzer, wavelength meter). These abilities will be tested with written exercises and tests and eventually an oral interrogation.
At the end of the course and after successfully passing the exam, the student should reach the following learning results, with reference to the Doublin Descriptors:
Knowledge and understanding (Dublin Descriptor 1, DD1)
knows how to analyze block diagrams of optical measurement setups and to understand the choice of different laser sources on the system performance;
knows how to design coherent optical detection systems, evaluating signal and noise performance;
understands how to use an optical telemeter, interferometer, velocimeter, and optical measurement instrumentation (optical spectrum analyzer, optical power-meter, wavelength-meter).
Applying knowledge and understanding (Dublin Descriptor 2, DD2)
can apply the acquired understanding to assess the operation and performance of available optical measurement instrumentation;
can apply the acquired knowledge and skills to design an optoelectronic sensor or an optical interferometer;
can discuss pros and cons, as well as performance trade-offs, among different optical measurement instruments.
Making judgements (Dublin Descriptor 3, DD3)
given a specific problem and project cases in the field of optical measurements, should be able to analyze the problem, highlight the peculiar requirements and characteristics, and compare autonomously different choices in terms of hardware and system performance;
can communicate effectively the results of his activity regarding the study and design of optical instrumentation, and can autonomously take design decisions.
Communication (Dublin Descriptor 4, DD4)
will be able to write reports about the analysis and design of optical instrumentation;
will be able to write comparisons between different lasers and techniques for the optical measurements.
Learning skills (Dublin Descriptor 5, DD5)
will be able to study and understand autonomously, even different instrumentation
from the one studied in the course;
will be able to understand and calculate the limiting performance of different and new optical measurements and measurement systems;
will be able to deepen his knowledge in the studied topics, and to read and understand scientific and technical publications dealing with laser instrumentation and optical measurements.

Review of incoherent/coherent optical sources and lasers, photodetectors (direct and coherent detection), amplitude and frequency noise in optical oscillators.
Laser telemetry: triangulators, Time-Of-Flight (pulsed/sine-mod.), LIDAR, and DIAL.
Measurements on optical sources: emission spectrum, spatial profile and beam divergence of lasers and LEDs.
Optical power and energy measurements (optical power/energy meters). Polarization measurements. Optical Spectrum Analyzers (OSA). Monochromator and wavemeter for the measurement of optical wavelengths. Measurements for optical communications: Polarization Mode Dispersion (PMD); Optical Time Domain Reflectometry (OTDR); eye diagram and BER measurements on the optical signal.
Laser Doppler Velocimetry (LDV) and Particle Image Velocimetry (PIV), optical autovelox (pulsed tele-laser and laser-barrier).
Laser interferometry: Michelson, Mach-Zehnder, Sagnac, Twyman-Green, Double-Beam, Double-Frequency interferometers. Limits due to phase noise and quantum detection in laser interferometry. Electronic speckle‑pattern interferometry. Self-mixing interferometry. Interferometric techniques for industrial measurements: displacement, planarity, angle, and vibration measurements. Laser interferometry for Gravitational-Wave detection.
Measurements of temperature, vibrations, and intrusion, using optical fibers.
ALL THE SLIDES OF THE COURSE AND PREVIOUSLY REGISTERED LECTURES ARE AVAILABLE IN ADVANCE on the OneDrive space of the Course.
Essay optional topics
Stability and active stabilization of laser oscillators: natural and technical fluctuations of amplitude and frequency of the optical field and their suppression using optoelectronic control loops.
Ultra-high-resolution optical measurements: hi-res and Doppler-free laser spectroscopy; atom manipulation (laser cooling and trapping of atoms); optical frequency clocks.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The exam is based on a written test about the topics of the course and on an optional oral interrogation, which is mandatory when requested by the professor.
No partial examinations are given during the semester. A pre-exam in December is possible and usually made available to the students.
The written test, approximately 1.5-to-2 hours long, is typically made of 3-to-4 exercises, mostly based on theory applications and the corresponding analysis and numerical calculations. Some theoretical questions can be included in the written test. Logical order and clarity in answering the questions and solving the problems will be considered in the evaluation. The given classwork is appropriate for testing the expected learning results.
In agreement with the class, up to 2 auto-evaluative class works (about 1 hour each) will be done voluntarily.
A LARGE SELECTION OF PREVIOUS CLASS WORKS WITH SOLUTIONS IS AVAILABLE ON THE COURSE WEBSITE.
The dates of the exams will be notified well in advance by the Professor to the students and published on the web pages of the course.
The official and final exam dates are those published on the School and POLIMI websites.
In the unfortunate event of a student delivering for correction a significantly incomplete classwork, or a severely insufficient one, highlighting a deep lack of knowledge in different and important topics of the course, this may imply the need to skip the next and "near" (at less than one month distance) exam date.
In such an unfortunate case, the professor will very likely use the right to question the student the next time he or she takes and passes a written test.
Prof. Cesare Svelto phone: 02-2399.3610, mobile: 3409761929, e-mail: [email protected]
The written exam, with the assigned exercises & design problems & theoretical questions in it, will be aimed at verifying the student's capabilities also with reference to the following Doublin Descriptors. In particular, the student will/can be asked to provide:
Knowledge and understanding (Dublin Descriptor 1, DD1)
description and analysis of block diagrams of optical measurement setups will be asked;
comparison of different laser sources and their impact on the optical system performance;
analysis of coherent optical detection systems, including signal and noise figures;
explanation about the application of an optical telemeter, interferometer, velocimeter, and optical measurement instrumentation (optical spectrum analyzer, optical power-meter, wavelength-meter).
Applying knowledge and understanding (Dublin Descriptor 2, DD2)
assessment of the operation and performance of available optical measurement instrumentation;
design of an optoelectronic sensor or an optical interferometer;
discussion about pros and cons, as well as performance trade-offs, among different optical measurement instruments.
Making judgements (Dublin Descriptor 3, DD3)
complete analysis of the problem (specific problem and/or project cases in the field of optical measurements), highlighting the peculiar requirements and characteristics, and comparing autonomously different choices in terms of hardware and system performance;
effective written communication, in scientific/engineering language, and reporting individual results and proposals regarding the study and design of optical instrumentation.
Communication (Dublin Descriptor 4, DD4)
reports about the analysis and design of optical instrumentation;
analysis and comparison of different lasers and techniques for optical measurements.
Learning skills (Dublin Descriptor 5, DD5)
design and analysis of optical instrumentation to solve optical measurement tasks;
quantitative analysis of the limiting performance of different and new optical measurements and measurement systems;

Silvano Donati, Photonic Instrumentation: Sensing and Measuring with Lasers, Editore: CRC Press, Anno edizione: 2023, ISBN: 9781032489063
Note: Some 50% of the textbook can be used for a deeper insight of the topics described in the Course. The slides and didactical material, and REGISTRATIONS of lectures and exercise classes, (available on the Course OneDrive website) are more than adequate for a full preparation of the exam.

The course is designed to provide students of engineering with in-depth skills on Intellectual Property (IP) management, with a special focus on patents.
The students' learning goals are:
Understand challenges, functions, processes in a business and industrial environment and their mutual effects on business, economy, environment and society.
Identify trends, technologies, key methodologies and stakeholder needs in a specific domain.
Design solutions applying a scientific and engineering approach (Analysis, Learning, Reasoning, and Modeling capability deriving from a solid and rigorous multidisciplinary background) coupled with participatory approaches to face problems and opportunities in a business, industrial and societal environment.

Knowledge, applied knowledge and understanding (DdD1 and DdD2):

• Understand the economic and legal rationale of Intellectual Property rights.
• Understand the range of IP tools and the related procedures available to obtain the desired patent coverage based on the set objectives.
• Understand the functioning of international databases to carry out basic patent searches, for monitoring or determining the freedom of operation.
• Identify the content, duration and coverage of a patent document and the freedom to operate that it entails for future or adjacent inventions.
• Understand the sources of uncertainty regarding IPs and the limits and challenges of IP enforcement.
  Autonomy of judgment (DdD3):
• Design suitable strategies of IP coverage and management, taking into account the specific context of the company and its competitive environment.
• Design IP portfolio strategies that match the strategic objectives of the related organization.

The course includes three parts.
PART ONE focuses on the explanation of the economic and legal rationale of IP rights and describes the various protection mechanisms available at the Italian, European and extra-European level, highlighting the main international differences.
Topic 1: What are patents. The economic and the legal-procedural point-of-view.
Topic 2: The requirements of patents (novelty, inventive step, industrial applicability, sufficient description).
Topic 3: The full range of IP rights (copyrights, utility models, trademarks, design protection, and other IPs).
Topic 4: The patenting procedure (filing, examination, extension, granting, opposition, maintenance) of the UIBM, EPO, UPC, PCT and the main international differences.
Topic 5: Practitioner view (guest lecture)
Topic 6: Copyright, trademark
PART TWO focuses on the management of IPs, providing students with a current and far-reaching view on strategies for managing single IPs and portfolios of patents, discussing the strategies currently used by companies in different types of industries, including those that make an open and semi-open use of patents.
The lecutures will also highlight the global trends in infringement and counterfeiting.
Topic 7: Extracting value from IPs. Closed, semi-closed and open IP strategies and how/when to use them.
Topic 8: Patent portfolio management strategies with practical applications.
Topic 9: IP violations: counterfeiting, plagiarism, piracy and infringment. An equitable balance between IP protection and the human rights to a have affordable healthcare.
Topic 10: Methods, circumstances and reliability of IP assessments.
Topic 11: Open innovation
PART THREE focuses on training the students on the use of the databases.
This part includes a series of practical exercises, such as those to check the freedom to operate, the monitoring of competitors, and describes the main steps of prior art searches.
Topic 12: Patent Lab (part I). The patent databases. Search and read basic patent information.
Topic 13: Patent Lab (part II): Freedom to operate, monitoring, preliminary prior art search.
Topic 14: Groupwork: IP Portfolios

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, senza presentazione/discussione finale
Student assessment will be based on:
Written homework assignment (10 points)
Written in-class essay exam (20 points)

Il Corso "Personalità Teambuilding e Leadership" introduce lo studente alla conoscenza delle strutture di personalità che orientano l'immagine di Sé, l'atteggiarsi, l'agire, il pensare, l'interagire, il comunicare e il cooperare: per lo studio, il lavoro e le relazioni interpersonali e con particolare riferimento allo sviluppo di competenze per il teambuilding e la leadership. Porta lo studente: - a valorizzare le proprie risorse e migliorare il proprio rendimento nell'acquisizione di nuove abilità e competenze; - a conoscere e ad affrontare meglio situazioni difficili a livello personale o di gruppo; - a prepararsi alla cooperazione e alla leadership; - ad affrontare al meglio le prime esperienze di inserimento lavorativo. Il corso offre laboratori esperienziali per lo sviluppo di soft skill nelle aree trattate, presenta i principi e modelli (di area costruttivista e relazionale) che guidano le esperienze di laboratorio e che possono essere adottati nella futura vita professionale e personale.

The course aims at providing the students with a critical understanding of the philosophical foundations of science and technology. But what is the relation between philosophy and science? On the one hand, the discoveries by scientists are often inspired by philosophical ideas. On the other hand, scientific achievements and technological advancements pose deep conceptual questions to philosophers. The connection between these disciplines is thus so tied that it becomes difficult to even draw a border between them. This course explores such a connection through the study of important episodes in the history of science. Outstanding issues of philosophy of science and technology, such as scientific progress, the method of science and the confirmation of theories and laws, will be addressed. Moreover, a particular focus will be devoted to philosophical questions concerning the nature of space as they emerge in the foundations of science, especially in relation to the famous debate between Leibniz and Newton.

The course aims at providing the students with a critical understanding of the philosophical foundations of science and technology.
But what is the relation between philosophy and science? On the one hand, the discoveries by scientists are often inspired by philosophical ideas. On the other hand, scientific achievements and technological advancements pose deep conceptual questions to philosophers. The connection between these disciplines is thus so tied that it becomes difficult to even draw a border between them. This course explores such a connection through the study of important episodes in the history of science. Outstanding issues of philosophy of science and technology, such as scientific progress, the method of science and the confirmation of theories and laws, will be addressed. Moreover, a particular focus will be devoted to philosophical questions concerning the nature of space as they emerge in the foundations of science, especially in relation to the famous debate between Leibniz and Newton. Finally, in the context of philosophy of technology, we will explore epistemic tools that are used to draw scientifically-informed inferences, such as computer simulations and thought experiments.
Here's below a list of the main topics:

• Method of science
• Demarcation between science and pseudo-science
• Confirmation
• Scientific Progress
• Philosophy of Space: Absolutism vs Relationism
• Scientific Reasoning
• Philosophy of Technology

Prova scritta obbligatoria, senza prove in itinere
Altro
Final written exam based on general questions about the topics of the lectures: specifically, the students are assigned to write several essays, which have to be emailed directly to the instructors at the following address: [email protected]. There are also individual class presentations scheduled during the term

Alan Chalmers, What is This Thing Called Science?, Editore: Hackett Pub Co Inc
Nick Huggett, Space from Zeno to Einstein: Classic Readings With a Contemporary Commentary, Editore: Bradford Books

This course is part of the Phortify study programme - Photonics Education Network for Next-Gen Innovation and Digital Skills Excellence for Industry and Society (https://phortify.eu/).
Students enrolled on the Engineering Physics degree programme, PSPA in Photonics and Quantum Technologies, and registered for the Phortify programme (https://phortify.eu/myphortify/) may select this course to include in their study programme one of the Phortify courses offered remotely by one of the other seven universities in the consortium without the need to undertake a mobility period.

This course is part of the Phortify study programme - Photonics Education Network for Next-Gen Innovation and Digital Skills Excellence for Industry and Society (https://phortify.eu/).
Students enrolled on the Engineering Physics degree programme, PSPA in Photonics and Quantum Technologies, and registered for the Phortify programme (https://phortify.eu/myphortify/) may select this course to include in their study programme one of the Phortify courses offered remotely by one of the other seven universities in the consortium without the need to undertake a mobility period.

The course “Photonic Computing” explores the potential of photonic technologies as an alternative and complement to conventional digital electronic solutions. Students are introduced to photonic integrated circuits and free-space optical systems, progressing from fundamental building blocks to complex and programmable architectures.
The course examines a broad range of computing applications, including photonic implementations of matrix operations and linear transformations, analog photonic processors, neuromorphic photonic circuits, and architectures for artificial intelligence and machine learning. It also addresses the role of photonics in enabling high-speed, energy-efficient interconnects for distributed computing systems, with examples spanning intra- and inter-data-center communications.
A comprehensive overview of state-of-the-art technologies is provided, along with selected case studies and a forward-looking discussion on the role of photonics in the future of high-performance computing (HPC) as outlined in current technology roadmaps.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will
know the main advantages/current limitations of photonic analog computing versus digital electronic computing;
comprehend the physical mechanisms that can be used to perform mathematical operations in the optical domain;
know the state-of-the-art of photonic technologies and devices that are used in computing applications and in distributed systems;
know the basic criteria and design rules of simple photonic processors
Applying knowledge and understanding
Students will be able to
map linear algebra operations into a photonic hardware
design simple photonic circuits that perform computational operations on optical signals
design of a fiber optic link for distributed computing systems
Making judgments
Students will be able to
· compare the performance of different photonic circuits implementing specific mathematical operations
· identify the most suitable photonic architecture to solve computational problems
Lifelong learning skills
Students will be able to apply the acquired knowledge to different and more complex computational problems, and autonomously conceptualize and design new photonic computing architectures and interconnections

Motivation & perspectives. Energy, bandwidth & latency bottlenecks of current digital electronics technologies for high performances computing (energy/bit metrics, I/Os density, memory wall, beyond-Moore scalability and sustainability). Photonics in datacentes and AI infrastructures. Analog vs digital processing. Advantages of photonic technologies for high performance computing.
Fundamentals of optics: Representation of optical signals in time and frequency domain; basics on electromagnetic waves in free space and waveguiding structures; optical fibers and integrated waveguides: technology and characteristic parameters (attenuation, group delay, chromatic dispersion, polarization). Temporal coherence and orthogonality of waves; principles of optical interference.
Implementation of matrix algebra in photonics. Sum and multiplication in the optical domain. Photonic building blocks for computing (waveguides, power splitters/combiners, phase shifters). Transmission matrix representation. Mach-Zehnder interferometer (MZI) as a 2x2 coherent adder. MZI architectures for coherent vector matrix multiplication. Mapping unitary and non-unitary operators on MZI meshes. MZI architectures (diagonal, triangular, rectangular). Singular value decomposition (SVD). Fidelity. Photonic Discrete Fourier transform (DFT).Microring resonators (MRR). MRR architectures for non coherent vector matrix multiplication. Broadcast and weight. Control strategies for MRR and MZI based architectures, technologies and programmability. Free space solutions: metasurfaces, spatial light modulators (SLM) and multiplane light converters (MPLC).
Linear operators and photonic analog processors: Analog implementation of differential/integral operators. Photonic temporal differentiator and integrator: frequency and time domain response, performance and limits. Photonic solvers of first-order and higher order ordinary differential equation (ODE). Hilber transform. Recursive programmable photonic circuits. Programmable optical solvers of systems of differential/integral equaltions. Analog matrix inversion.
Optical Neural Networks (ONN): implementation of photonic neurons, architectures of optical neural networks (ONN). Integrated ONN (coherent/non coherent) and free space ONN. Nonlinear activation functions in photonics. Electronic vs photonic neuronal functions.
Photonics for distributed computing. Optical interconnections for intra- and inter-datacenter communications. Design of a fiber optic link, power budget and time rise budget. Switches in datacenters: requirements and characteristics. Optical switches: 2D and 3D topologies, techologies (MZI, MRR, MEMs), performance, port-count scalability. Optical vs electrical switches
Case studies:
Coherent Photonic Adder for Communication and Sensing
All-optical calculation of orthongonal channels of arbitrary transmission system (optical SVD)
Mathematical coprocessors (analog matrix inversion)
Photonics circuits and architectures for AI and machine learning.
Students will have the opportunity to visit to the Photonic Devices Lab (https://photonics.deib.polimi.it/) and see live experiments of photonic chips that perform computational operations in real time (2 hours)
During the course two seminars will be held (remotely) by professors from international research groups carrying out pioneering research in the field of photonic computing (2 hours).

Prova scritta obbligatoria, con prove in itinere
Prova orale facoltativa (a scelta dello studente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The exam consists of two tests structured as follows:

• written test (open book) on the topics covered in the first part of the course (Fundamentals of optics, Implementation of matrix algebra in photonics). On this part of the course there is also a mid-term written test.
• oral test on the topics covered in the second part of the course (Linear operators and photonic analog processors, Optical Neural Networks, Photonics for distributed computing ) or alternatively a thematic study (project) which can be carried out in pairs (2 students) on selected topics agreed with the teacher.
  Type of assessment
  Description
  Dublin descriptor
  Written test
  · numerical exercises focusing on photonic devices and circuits for computing
  1, 2, 3
  Oral test
  · oral exam on the second part of the course;
  · assessment of the thematic study (project) through an oral presentation
  1, 2, 3, 4, 5
  Passing the written test is not a prerequisite for the oral test. Students who have not passed the mid-term written exam can still take the oral test and recover the written test in a subsequent date of the same session.

Course slides (Ed. 2025) https://webeep.polimi.it/
Note: Available on the course's WeBeep website.
Mahdi Nikdast, Sudeep Pasricha, Gabriela Nicolescu, Ashkan Seyedi, Di Liang, Silicon Photonics for High-Performance Computing and Beyond, Editore: CRC Press, Anno edizione: 2024, ISBN: 9781032122441
José Capmany, Daniel Pérez, Programmable Integrated Photonics, Editore: Oxford University Press, Anno edizione: 2020, ISBN: 0198844409
Paul R. Prucnal, Bhavin J. Shastri, Neuromorphic Photonics, Editore: CRC Press, Anno edizione: 2017, ISBN: 9781315370590

The course Physics of Disordered Materials is a 5-CFU course. The present detailed program defines aims, educational outcomes and the syllabus of the course.
Physics of disordered materials aims at introducing the physics of structurally disordered condensed matter, with attention to irradiation effects. After introducing the concepts of order, disorder, and order parameters (long range), the basic mechanisms of structural modification upon irradiation of a crystal with energetic particles are addressed, referring to the elastic collision regime. The phenomenology of the glass transition, including the interplay between thermodynamics and kinetics is studied before offering a robust overview over the main techniques of experimental investigation of structural disorder. Attention is then focused to the structure of clusters, taken as the constitutive bricks of disordered materials, to the synthesis of nanocrystalline and nanoglassy materials, discussing a selection of their peculiar properties, to quasicrystals, studying their relation to crystals and the features of the quasicrystalline-amorphous state transition. The Anderson-Mott localization is introduced.

After attending the course and after a positive evaluation, it is expected that the students:
1. know and are able to describe the effect of energetic particle irradiation (elastic collision regime) on the disordering and the structural stability of a target (DdD1)
2. know and are able to describe the phenomenology of the glass transition for different classes of non-polymeric materials, considering the interplay between thermodynamic and kinetic aspects of the transition (DdD1)
3. know and are able to choose (in principle) among the main experimental techniques to study structural disorder, depending on the kind of information each of them provides, in pareticular concerning the investigation of short and medium range order in a structurally disordered material (DdD1)
4. know and are able to describe the basics of the physics of atomic clusters (inert gases and simple metals) and of quasicrystals in relation to their structure, as well as the essentials of transport in heavily disordered materials and the pertinent modeling (Anderson-Mott localization) (DdD1)

structural order; kinds of disorder; ordering rules; long range order parameters
phenomenology of the glass transition; thermodynamic and kinetic concepts; glass forming ability (GFA) criteria
physical mechanisms of structural modification upon energetic particle bombardment:
electrons: replacement collision sequence (RCS)
ions (elastic regime): linear versus dense collision cascade
microscopic analysis of the space-time evolution of a collision cascade
GFA in irradiated materials: interpretative and predictive criteria
experimental techniques for the analysis of structural disorder: (a), elements of scattering theory; scattering from structurally disordered materials; (b), X-ray absorption spectroscopies: EXAFS; XANES; (c), vibrational spectroscopies: IR absorption and Raman scattering
(a), short range order, chemical (CSRO), topological (TSRO); (b), medium range order (MRO); (c), atomic clusters; fullerene; fullerite; hollow diamonds
(*) nanocrystalline materials; structure and role of the interfaces; nanoglasses; synthesis of nanostructured materials via irradiation; nanostructured material response to irradiation
(*) quasicrystals: discovery, definition, aperiodic and quasi-periodic lattices; real quasicrystals; nano-quasicrystals; quasicrystal-amorphous and the reverse transition
(*) transport in heavily disordered materials: the Anderson-Mott localization; historical introduction; conducting vs localized electrons; the metal-insulator transition; localization of light: from ballistic to diffusive transport to Anderson localization; localization and enhanced transport in quasicrystals
Every year will be selected either section 6*, or section 7*, or section 8*.

Prova orale obbligatoria
Valutazione continua condizionata (a scelta del docente)
The evaluation consists in an oral examination that points at verifying the knowledge of the course topics, with particular reference to the ability in interpreting the physical bases of the different topics. The student will be able to demonstrate that he knows, has understood, and is able to describe, using the appropriate language and mathematical formalism, the course content. In particular the exam aims at verifying that the candidate:
1. knows and is able to discuss the effect of energetic particle irradiation (elastic collision regime) on the structural stability of a target, analysing the degree of structural order by using an appropriate order parameter
2. knows and is able to discuss the phenomenology of the glass transition for different classes of (non-polymeric) materials and the interplay between thermodynamic and kinetic aspects of the transition
3. knows and is able to conceptually select the most appropriate among the main experimental techniques introduced to study structural disorder. The choice will rest on the critical knowledge of the kind of information that each technique offers to analyse short and medium-range order in structurally disordered materials of different kind
4. knows and is able to discuss.
a. the basics of the physics of atomic clusters and the models for their structural evolution and stability
b. the basics of the physics of quasicystals and the models for their structural evolution and stability
c. the basics of transport in heavily disordered materials and its modeling

K.E. Sickafus, E.A. Kotomin, B.P. Uberuaga (Eds.), Radiation Effects in Solids, Editore: Springer, Dordrecht, Anno edizione: 2007, ISBN: 1-4020-5294-4
K. Binder, W. Kob, Glassy materials and disordered solids, Editore: World Scientific, Anno edizione: 2011, ISBN: 978-981-4350-17-4
P. M. Ossi, Disordered Materials - An Introduction, Editore: Springer, Anno edizione: 2024, ISBN: 978-3-662-68426-9

This course regards the main physical phenomena that occur in a photovoltaic cell. It starts with an overview of the existing technologies, first, second and third generation. It then look in more detail to the processes taking place within the active material of a photovoltaic cell and their connection to the device performance: the absorption of light, the charge generation, the charge transport and recombination. The approach is phenomenological even if it makes extensive use of the concepts and basic tools of quantum mechanics, which are briefly introduced. The goal is to provide the wealth of basic knowledge needed to design new materials for photovoltaics.
The class hosts a number of external speakers, from the academy and from the industrial world, that introduce state of art technologies.
The approach of the teaching, through front seminar and group discussion, differs from that adopted in early years of the student cv. We will not provide notions or directions, yet the student is exposed to a broad set of knowledge and experiences, suitable for mature attendants that are approaching the end of their student career.

Students:
-will learn the fundamentals regarding photovoltaic cell devices (DD1)
-will acquire knowledge on the state of art in photovoltaic conversion and on the existing technologies, both on the market and at research stage (DD2, DD3)
-will learn the fundamental concepts in photophysics for designing and characterizing new materials and devices for photovoltaic conversion (DD1, DD2)

INTRODUCTION
Solar radiation and climate change.
Ultimate theoretical limit of a cell and detailed balance.
PHOTOVOLTAIC DEVICES
General characteristics of a PV cell.
The p-n junction and silicon solar cell; Thin film solar cells; Polymer solar cells.
Dye sensitized solar cells.
Luminescent solar collectors.
Natural systems for light harvesting and conversion.
ABSORPTION
Molecular absorption. Adibatic approximation, Vibronic linewidth and Franck-Condon principle.
Einstein A and B coefficients. Strickler-Berg relation.
Kasha molecular exciton in aggregates.
Energy transfer (Foerster, Dexter, Frenkel exciton).
Absorption in a crystal (band structure, carrier relaxation,
Wannier-Mott exciton, dimensionality).
MOLECULAR DYNAMICS
Jablonski diagram, internal conversion, vibrational energy redistribution, radiative decay, inter system crossing, phosphorescence. Mono and bi molecular recombination. Vavilov-Kasha rule.
CHARGE PHOTOGENERATION
Charge transfer states.
Marcus theory.
Band to band transition, Onsager, Poole-Frenkel. Donor-Acceptor system.
TRANSPORT
Mobility
Hopping in disordered systems.
Coherent transport.
Experimental techniques to determine mobility.

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, con presentazione/discussione finale
Students will be required to draft a research-like scientific proposal (maximum 2 pages) on the topics covered in the course. Students will then discuss their project in a short presentation, followed by questions.

Guglielmo Lanzani, The Photophysics behind photovoltaic and photonics, Editore: Wiley VCH, Anno edizione: 2012, ISBN: 978-3-527-41054-5
Peter Würfel, Physics of Solar Cells: From Principles to New Concepts, Editore: Wiley, Anno edizione: 2005, ISBN: 9783527404285

The course is offered as a 10-CFU course (Plasma Physics I+II), jointly taught with two 5-CFU courses (Plasma Physics I and Plasma Physics II) and one 8-CFU course (Plasma Physics). The present detailed programme defines the goals, expected learning outcomes and topics for all jointly offered courses.
Across the different versions, the course is organised around the main theoretical frameworks of plasma physics. Electrically charged particles and electromagnetic fields are always treated as parts of a single, self-consistent physical system: electromagnetism is therefore not introduced as an external tool used only in selected topics, but as an intrinsic component of every plasma description. Starting from single-particle dynamics, the course develops kinetic, multifluid and magnetohydrodynamic descriptions, with explicit attention to the physical assumptions, characteristic scales, applicability and limitations of each model. Selected topics from contemporary plasma research are considered and exploited to illustrate how these theoretical frameworks support the interpretation of analytical models, numerical simulations and experimental observations.
PLASMA PHYSICS I (5 ECTS)
The aim of this course is to provide a fundamental understanding of the plasma state, with special emphasis on strongly ionised, high-temperature plasmas. The subject is first framed within classical electrodynamics and particularly the electrodynamics of continuous media. This is not treated as an external preliminary topic, but as the common framework required to describe the self-consistent interaction between charged particles and electromagnetic fields. After introducing the main physical properties and characteristic quantities of the plasma state, the main theoretical descriptions are derived from first principles and related to one another. They are used to investigate single-particle dynamics, collective modes and the propagation and absorption of electromagnetic radiation in plasmas. The course concludes with a brief introduction to controlled thermonuclear fusion as a first example of a complex research field in which several plasma descriptions must be adopted and combined.
PLASMA PHYSICS II (5 ECTS)
The course deepens selected aspects of the physics of matter in the plasma state and represents a logical continuation of Plasma Physics I. It extends the analysis of collective phenomena to kinetic, nonlinear and relativistic regimes and addresses the emission of electromagnetic radiation, magnetically confined plasmas, collisions, intense laser-plasma interaction and controlled thermonuclear fusion, in both magnetic and inertial confinement approaches. These topics are selected both to provide a more complete development and understanding of plasma behaviour and to show how fundamental plasma physics is used in major areas of engineering applications and current research. Attention is paid to the complementary roles of theoretical modelling, numerical simulation and experimental observation.
Also, within the POLIMI Interdisciplinary Programme in Green Technologies, the introduction to nuclear fusion is intended to adequately place its potential long-term contribution to future energy systems in the context of the present scientific and technological status of the field and of the broader sustainable energy transition. Visits to research centres (ISTP-CNR Milano, ENEA Frascati and LNF-INFN Frascati) may be planned at the end of the course, subject to organisational availability and students’ interest.

COMMON LEARNING OUTCOMES.
At the end of the relevant course, the student is expected to be able to:
recognise a plasma as a self-consistent system of charged particles and electromagnetic fields, and explain how this feature enters the different theoretical descriptions;
identify the characteristic spatial, temporal and energy scales of a plasma problem; state the assumptions underlying a model; and assess its domain of validity and limitations;
select and justify an appropriate theoretical framework for the interpretation of a plasma phenomenon, explaining which information is retained or discarded when moving among single-particle, kinetic, multifluid and magnetohydrodynamic descriptions;
interpret the physical meaning of the mathematical methods used, perform simple derivations and order-of-magnitude estimates, and relate theoretical results to numerical simulations and experimental observations.
PLASMA PHYSICS I. The student:
knows the main mathematical and physical methods required to develop theoretical plasma physics;
knows the main quantities defining the plasma state and the theoretical models suitable for its description, with particular reference to classical, quasi-ideal plasmas;
is able to describe the main properties that distinguish plasmas occurring in nature and in the laboratory, and to estimate the principal quantities characterising them;
is able to use the theoretical plasma models to describe small-amplitude electromagnetic waves and collective modes, including the physical interpretation of resonant wave-particle interaction and Landau damping.
PLASMA PHYSICS II. The student:
knows the theoretical models required to describe kinetic, nonlinear and relativistic plasma dynamics;
knows the main physical properties of magnetically confined plasmas, with particular reference to particle dynamics, equilibrium and stability in idealised and realistic geometries, and to the role of plasma edge regions;
is able to apply plasma theory to describe nonlinear waves, intense laser-plasma interaction, radiation emission;
is able to integrate the physical results developed in the course to understand the main aspects of thermonuclear fusion research, including the properties of thermonuclear plasmas, the conceptual scheme and energy balance of a fusion power plant, and the magnetic and inertial confinement approaches. The student is also able to discuss critically the potential long-term role of nuclear fusion in climate-change mitigation and the sustainable energy transition, distinguishing established physical results, current research objectives and main challenges.

The topics are organised to make explicit the progression from the electrodynamic and single-particle foundations to kinetic, multifluid and magnetohydrodynamic descriptions. They are not intended as independent chapters or as a hierarchy of increasingly accurate models: each description is discussed with reference to the information it retains, the assumptions on which it relies and its range of validity. Selected research contexts are used to show how the same fundamental tools contribute to analytical modelling, numerical simulation and experimental interpretation.
PLASMA PHYSICS I.
Mathematical and electromagnetic framework. Role of tensor fields in physics; fundamental vector and tensor operations in general coordinates. Microscopic Maxwell equations and Lorentz force. Electrodynamic potentials, gauge invariance, Lorenz and Coulomb gauges. Systems of units in electromagnetism: SI and Gaussian units. Selected mathematical and electromagnetic topics are also developed through flipped-learning activities using the EVOLI learning tool.
Electrodynamics of continuous media. Poynting's theorem and conservation of electromagnetic energy in linear dispersive media. Anti-Hermitian component of the dielectric tensor and absorption of electromagnetic energy. Conservation of energy in the presence of temporal and spatial dispersion. Propagation of electromagnetic waves in uniform dispersive media: linear theory.
Introduction to the plasma state. Fundamental plasma parameters and characteristic scales. Electric-charge shielding and the Debye length. Thermodynamic properties of a classical plasma. Plasma oscillations and plasma frequency. Electrical conductivity. Physical conditions for collective plasma behaviour.
Single-particle dynamics and guiding-centre theory. Dynamics of charged particles in constant, uniform external electric and magnetic fields. Motion in slowly varying fields and the guiding-centre approximation. Drift motions and magnetic mirror effect.
Theoretical descriptions of plasmas. Microscopic and statistical descriptions: Klimontovich equation, kinetic theory and Vlasov equation. Macroscopic descriptions: moment equations and multi-fluid models. Single-fluid approach: magnetohydrodynamics (MHD). Relations among the different descriptions, closure assumptions and limits of validity.
Collective modes and waves in a plasma I. Macroscopic approach: collective modes and waves in cold and warm plasmas. Waves in the presence of an external magnetic field. Kinetic approach: collisionless absorption of electrostatic waves and Landau damping. Physical interpretation of resonant wave-particle interaction and comparison between macroscopic and kinetic descriptions.
Introduction to controlled thermonuclear fusion. Nuclear fusion reactions and thermonuclear plasmas.
PLASMA PHYSICS II.
Waves in a plasma II. General aspects of the kinetic study of collective modes. Kinetic description of waves in the presence of an external magnetic field: cyclotron resonances, their physical interpretation and main properties.
Nonlinear and relativistic plasma dynamics. Introduction to the mathematical description of special relativity; selected topics are also developed through flipped-learning activities using the EVOLI learning tool. Relativistic plasma models and propagation of large-amplitude waves in the cold-plasma approximation.
Laser-plasma interaction. Interaction of electromagnetic waves with underdense and overdense plasmas. Ponderomotive force, excitation of plasma waves and wave breaking. Parametric instabilities. Selected applications of superintense laser-plasma interaction, used to connect fundamental models with current experimental and numerical research.
Emission of electromagnetic radiation by plasmas. Main results of the general theory of radiation emitted by moving charged particles. Cyclotron and bremsstrahlung emission in plasmas.
Physics of magnetically confined plasmas. Dynamics of charged particles in toroidal and tokamak magnetic configurations and consequences for the physical behaviour of the system. One-dimensional MHD equilibrium and stability: theta pinch, Z pinch and screw pinch. Two-dimensional MHD equilibria and stability: balance of toroidal forces, Grad-Shafranov equation, Solov'ev equilibria and stability criteria. Fundamental properties of plasma edge regions in magnetically confined systems: limiters, divertors and scrape-off layer.
*Collisions in plasmas. General properties of the collisional term in kinetic descriptions. Coulomb collisions and characteristic collision times. Collisional energy exchange between electrons and ions. Alternative descriptions of the collision integral: Balescu-Lenard, Landau and Fokker-Planck equations.
Controlled thermonuclear fusion. Lawson criteria and ignition conditions. Magnetic (MCF) and inertial (ICF) confinement approaches. General scheme and energy balance of a fusion power plant. Fundamental physical properties of magnetically and inertially confined thermonuclear plasmas. Main challenges of fusion systems. Environmental aspects and potential long-term contribution to the sustainable energy transition. Current state of research in MCF and ICF.
For the 8-CFU course Plasma Physics, the selection of topics will be agreed with the enrolled students at the beginning of the course, taking their academic background and interests into account while preserving the overall conceptual coherence of the programme.

• Topic covered depending on available time and the interests and academic background of the enrolled students.

Prova orale obbligatoria
Assessment consists of an oral examination, normally lasting 20-30 minutes. For the 8-CFU course, the examination covers the selection of topics agreed within the end of the course.
The examination verifies knowledge of the course topics and, in particular, the ability to interpret the physical meaning of the mathematical methods used; identify the characteristic scales and assumptions underlying a model; assess its range of validity; connect single-particle, kinetic, multifluid and MHD descriptions; and select and justify an appropriate theoretical framework for a given plasma problem. For Plasma Physics I, the main focus is on plasma parameters and characteristic scales, theoretical plasma descriptions, single-particle dynamics, small-amplitude collective modes and electromagnetic-wave propagation. For Plasma Physics II, the main focus is on kinetic, nonlinear and relativistic collective phenomena, intense laser-plasma interaction, radiation emission, magnetically confined plasmas, collisional processes when included in the programme, and controlled thermonuclear fusion.
Knowledge of electromagnetic systems of units and representative values of fundamental plasma quantities, together with the ability to develop simple derivations, limiting-case arguments and order-of-magnitude estimates, is also assessed.
After the oral examination, students are required to undertake a self-assessment exercise. Its purpose is to provide feedback on their ability to identify the objective elements that emerged during the examination and to relate them to the corresponding quantitative grade. This phase has no effect on the official assessment; it is intended as an opportunity to test the level of awareness achieved and to use the feedback from the examination more effectively.

G. Pucella, S.E. Segre, Fisica dei plasmi, Editore: Zanichelli, Anno edizione: 2010
R. Pozzoli, Fisica del plasma termonucleare e astrofisico, Editore: CLUED, Anno edizione: 1984
T.J.M. Boyd, J.J. Sanderson, The Physics of Plasmas, Editore: Cambridge University Press, Anno edizione: 2003
A. I. Akhiezer et al., Plasma Electrodynamics Vol 1: linear theory, Editore: Pergamon Press, Anno edizione: 1975
A. I. Akhiezer et al., Plasma Electrodynamics Vol 2: Non-linear theory, Editore: Pergamon Press, Anno edizione: 1975
L. D. Landau, E.M. Lifshitz, Physical kinetics, Editore: Elsevier, Anno edizione: 1981
N.G. Van Kampen, B.U. Felderhof, Theoretical methods in plasma physics, Editore: North Holland, Anno edizione: 1967
L. D. Landau, E.M. Lifshitz, Electrodynamics of continuous media, Editore: Elsevier, Anno edizione: 1984
W.K. Kruer, The Physics of Laser Plasma Interactions, Editore: Westview Press, Anno edizione: 2003
J. Freidberg, Plasma physics and fusion energy, Editore: Cambridge University Press, Anno edizione: 2007

Attività progettuale di tipo intensivo che assolve a parte dei crediti di tirocinio.
Lo studente è chiamato a partecipare ad attività di progetto in gruppo e/o individuale su tematiche individuate dal Corso di Studi in collaborazione con il mondo del lavoro.

The course gives the opportunity to enter the world of radiation detection systems, a key topic in scientific research as well as in industrial applications with an increasing demand of experts in the field. The course covers the physical principles of semiconductor detectors aiming at the measurement of diverse properties (e.g. energy, time, position, imaging) of the incident radiation (X and gamma rays, visible photons, charged particles, etc.), the main architectures of the readout electronics and the basic tools for their design and optimization. The program reviews basic and advanced detection systems currently in use in a variety of scientific and technological fields (e.g. space, experiments at synchrotrons/particle accelerators, non-destructive analysis, forensics) and the more recent challenges and developments.
The course includes in-depth analysis of selected case studies and several laboratory sessions with real detection systems.

• knowledge of the main topics in the field of radiation detection systems. E.g. i) main types of radiation interaction with matter, ii) principle of operation of different semiconductor detector topologies for imaging and spectroscopy, iii) mechanisms of signal formation, iv) low noise readout architectures and v) experimental setups of practical detection systems (DD1).
• analysis of the main parameters governing the interaction of X-ray photons with a sample or with a detector, use of public cross-section databases, computation of detected signal shapes in specific detector topologies, analysis of the noise performance of a radiation detection system, optimization of the frontend electronics for specific cases (DD2).
• ability to communicate the knowledge acquired in specific activities (e.g. laboratory sessions, topical seminars or scientific papers) on cases of real detection systems using the appropriate scientific terminology and the descriptors of the main concepts in the field (DD1 and DD4).

Interaction of radiation with matter: photons and charged particles, mean creation energy, statistical fluctuations.
Semiconductors as radiation detectors: pn junction, charge transport, generation-recombination, charge multiplication mechanisms, trapping, entrance window.
Semiconductor detectors for energy and position measurement of X-rays, gamma-rays, charged particles: microstrip detectors, pixel detectors, semiconductor drift detectors for spectroscopy and position-sensing, non-silicon detectors.
Readout electronics: low-noise architectures and processing techniques.
Signal formation in radiation detectors: Shockley-Ramo's theorem, computation of induced current shapes in relevant case studies.
Imaging pixel sensors: Charge Coupled Devices (CCD), CMOS monolithic active pixel sensors.
Advanced detector-amplification structures.
Examples of detection systems for frontier applications: synchrotron light experiments, astrophysics, diagnostic imaging, high energy physics experiments, elemental analysis, etc.
Laboratory activities
Laboratory activities aim at making students more familiar with the main instrumental facilities of a detector laboratory and with the operation of a detection system from an experimental standpoint. Students will work in groups with assigned tasks, e.g. analyze realistic detection systems, conduct specific experiments with X-rays and perform data analysis, evaluate main performance figures of a low-noise detector-electronic system. At the end, the groups will report their results to the class.
Other activities
Topical seminars from experts in the field and/or assignment of topical papers and/or a visit to an international scientific center will be possibly organized during the semester on a year by year basis in order to introduce cases of real detection systems and/or advanced topics.

Prova orale obbligatoria
The evaluation is in the form of an oral interview. The interview is intended to evaluate and assess:

• the level of understanding of the main topics in the field of semiconductor radiation detectors, e.g. the main types of radiation interaction with matter, the fundamental detector topologies for imaging and spectroscopy, the formation of the electrical signals in detectors, the low noise architectures mainly used for the readout (Dublin Descriptor 1).
• the level of understanding of the principle operation of the different detector topologies and of the techniques to analyse the shape and properties of detected signals.(Dublin Descriptor 1 and 2).
• the analysis of the noise performance of a radiation detection system coupled with its frontend electronics.(Dublin Descriptor 1 and 2).
• the application of the knowledge in the field to choose the most suited topology of detector for a given application and the ability to make numerical estimates and derivations.(Dublin Descriptor 2).
• ability to communicate the knowledge of real cases of detection systems acquired in specific course activities with a critical approach in the discussion. (Dublin Descriptor 1 and 4).

Gerhardt Lutz, Semiconductor Radiation Detectors, Editore: Springer, Anno edizione: 2007, ISBN: 9783540716792
Glenn F. Knoll, Radiation Detection and Measurement, Editore: Wiley, Anno edizione: 2010
Additional teaching material https://webeep.polimi.it/
Note: Suggested readings will be made available during the course
J. Ohta, Smart CMOS image sensors and applications, Editore: CRC Press
Note: E-book available at www.biblio.polimi.it

1) Equazioni di reazione-diffusione: un comune strumento modellistico in fisica, biologia ed economia. Esempi e applicazioni.
2) Problemi ai valori iniziali: buona posizione sia nei domini limitati che illimitati; principi di massimo e confronto.
3) Soluzioni stazionarie: esistenza, comportamento qualitativo; autovalori principali; questioni di stabilità: sopravvivenza, estinzione.
4) Comportamento per tempi lunghi: convergenza agli equilibri, soluzioni eterne, onde viaggianti, velocità di propagazione.
5) Sistemi di equazioni di reazione-diffusione: formazione di pattern.
6) Diffusione anomala e laplaciano frazionario

This course aims to provide students with the knowledge of some basic topics of Mathematical Analysis. Such topics play an essential role, for instance, in Probability and Statistics (measure theory and integration) as well as in the modern approach to partial differential equations (Banach and Hilbert spaces, linear operators). The goal is to get students familiar not only with the results and their applications, but also with the technicalities of the proofs and the rigorous check of the hypotheses through examples and counterexamples.

Lectures and exercise sessions will allow students to acquire the following abilities:
Knowledge and understanding
know some basic concepts of mathematical analysis which are helpful in several applications
know the proofs of some fundamental theorems in measure and integration theory and in linear functional analysis
understand the range of applicability of various results through counterexamples
Ability in applying knowledge and understanding
state rigorous definitions of the presented notions
state and prove the most relevant theorems
solve theoretical problems related to the presented theory
Making judgements
find simple counterexamples
Communication skills
write and speak mathematical concepts in a clear and rigorous way

1. Real Analysis. Measurable space, measure, outer measure. Generation of an outer measure. Carathéodory's condition, measure induced by an outer measure. Lebesgue's measure on R ^n. Measurable functions. The Lebesgue integral. Abstract integration. Monotone convergence theorem, Fatou's Lemma, Lebesgue's dominated convergence theorem. Comparison between the Lebesgue and Riemann integrals. Different types of convergence. Derivative of a measure and the Radon-Nikodym theorem. Product measures and the Fubini-Tonelli theorem. Absolutely continuous functions and weak derivatives.
2. Functional Analysis. Metric spaces, completeness, separability, compactness. Normed spaces and Banach spaces. Spaces of integrable functions. Linear operators. Uniform boundedness theorem, open mapping theorem, closed graph theorem. Dual spaces and the Hahn-Banach theorem. Reflexivity. Weak and weak* convergences. Banach-Alaoglu theorem. Compact operators. Hilbert spaces. Projection theorem, Riesz representation theorem. Orthonormal basis, abstract Fourier series. Spectral theorem for compact symmetric operators. Fredholm alternative.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The exam consists of a written test, divided into two parts. The first one contains theoretical questions on topics covered in the course, concerning definitions, theorems, proofs, examples and counterxamples. The second part contains exercises. To pass the exam, it is required a positive assessment both in the first part and in the second one. In some cases, if it is requested by the professor, the written examination could be followed by an oral one. No mid-term test is planned.
The exam has the goal of checking whether the student has acquired the following skills:
knowledge of some basic concepts of mathematical analysis which are helpful in several applications
knowledge of the proofs of some fundamental theorems in measure and integration theory and in linear functional analysis
understanding of the range of applicability of various results through counterexamples
ability to state rigorous definitions of the presented notions
ability to prove some of the presented theorems
ability to apply the presented theory to solve given problems
finding simple counterexamples
communicating mathematical concepts in a clear and rigorous way
Referring to the Dublin Descriptors, the exam is aimed at evaluating acquired skills, understanding of mathematical tools and the ability to apply knowledge (DD1, DD2, DD3, DD4).

H. Brézis, Functional Analysis. Sobolev Spaces and Partial Differential Equations, Editore: Springer, Anno edizione: 2010
G. B. Folland, Real Analysis, Editore: Wiley and Sons, Anno edizione: 1999
Note: (second edition)
M. Muratori, F. Punzo, N. Soave, Esercizi Svolti di Analisi Reale e Funzionale, Editore: Esculapio, Anno edizione: 2021
Note: Eserciziario
H.L. Royden, P. Fitzpatrick, Real Analysis, Editore: Pearson, Anno edizione: 2010
W. Rudin, Real and Complex Analysis, Editore: McGraw-Hill, Anno edizione: 2007
Note: (third edition)
W. Rudin, Functional Analysis, Editore: McGraw-Hill, Anno edizione: 1991
Note: (second edition)

The aim of the course is to teach the fundamental facts that lead to a radical rethinking of the common concepts of space and time and to the formulation of the theory of relativity. The approach adopted is Lagrangian and a particular emphasis is placed on the role of symmetry. The implications of the theory of relativity on mechanics, electromagnetism and quantum mechanics will be discussed. Finally, we will talk about the equivalence principle and the fundamental aspects of the general theory of relativity.

• Knowledge and understanding (DD1) The students will learn the fundamental properties of the underlying scenario on which physical phenomena take place: the spacetime. Due to the universal character of these laws the students will be also exposed to their usage also in different fields of physics such as mechanism, electromagnetism, quantum mechanics.
• Apply knowledge and understanding (DD2) The students will acquire the correct perspective concerning the experimental techniques and the theoretical methods nowaday employed in the field of relativity. The students will also be made aware of the possible applications of the argumests of the course and their consequences on the human life.
• Making Judgements (DD3) The students will develop a correct perception of the orders of magnituds of the physical quantities involved in the processes taking place in relativistic phenomena. The students will be stimulated to learn how to implement a true research project starting from the basic physical ideas and by making the proper strategic choices, with a particular emphasiys on aspects such as the feasibility of the project as far as the human and economical resources available are concerned.
• Lifelong learning skills (DD5) Students will acquire the capability to focus on the relevant aspects of a particular phenomenon and forecast the steps necessary, both on the experimental and on the theoretical ground, for understanding and exploiting it. All this in the perspective of a team work where eve though the activity is splitted between the team components, each of them will maintain an integrated vision of its specific duty.

Galilean relativity
The constancy of the speed of light and the einsteinian principle of relativity
The crisis of the concepts of space and time: time dilation and lenght contraction
Events, lines of universe, intervals and Minkowski diagrams
Light cone, poper time
Lorentz transformations
Velocity transformations
Four-vectors, four-tensors, tensor analysis, metric tensor
Four-velocity, four-acceleration, four-impulse
Mechanics: lagrangian approach and role of simmetry
Relativistic lagrangian
Energy, momentum and equivalence of mass and energy
Relativistic scattering
Maxwell equations: deduction from special relativity
Lagrangian of the electromagnetic field
Covariant form of the Maxwell equations
Brief introduction to quantum relativity: the Klein - Gordon and Dirac equations
General relativity

Prova scritta obbligatoria, senza prove in itinere
The final exam will be in written form and will consist in a series of questions on a series of specific topics covered during the course. Each question will not simply involve the exposition of a specific topic but will be conceived in order to be multidisciplinary and aimed to verify, aside from the knowledge acquired by the student on individual arguments, also its comprehension of the discipline as a whole and its capacity to cross correlated the various arguments covered by the course.

E. Puppin, Relativity and its implications, Anno edizione: 2023
Note: Text available in italian, english and chinese.
V. Barone, Relatività, Editore: Boringhieri, Anno edizione: 2011, ISBN: 978-88-339-5757-9
Lev Landau - Eugeni Lifshitz, Course on Theoretical Physics - Vol II: The Classical Theory of Fields, Anno edizione: 1980, ISBN: 0750627689

The course introduces students to the application of materials science and related technologies for the study, monitoring, and conservation of Cultural Heritage. It provides a multidisciplinary framework for understanding the value of Cultural Heritage and the fundamental principles of its preservation, with particular emphasis on the relationship between material properties, manufacturing techniques, deterioration processes, and conservation strategies.
Students will become familiar with the main categories of tangible cultural heritage, including architectural heritage and sculpture, mural and easel paintings, polychrome artworks, metal artifacts, paper-based objects such as drawings and manuscripts, and modern and contemporary artworks made from polymeric materials and design objects. The specific case studies examined each year may vary according to opportunities for site visits, museum activities, and direct observation of artworks.
The course presents the materials, technologies, and execution techniques that characterize different classes of artifacts, highlighting how their knowledge is essential for conservation and monitoring activities. Particular attention is devoted to the mechanisms of deterioration resulting from environmental exposure and atmospheric pollution, as well as to the principles of preventive conservation and the materials and methods used in conservation interventions. Examples and applications will focus especially on the conservation of architectural surfaces and other outdoor cultural heritage assets.

1 Knowledge and understanding (DdD1)
After attending the course and after the final examination, the student:
knows the main concepts of Cultural Heritage, deterioration, safeguard and conservation, monitoring.
knows the main characteristics of different Cultural Heritage items such as historic buildings and sculptures, paintings and mural paintings, modern and contemporary artworks, bronzes; is able to read and classify artifacts according to material science profile;
is able to draft the main durability and conservation issues of the different Cultural Heritage items;
2 Applying knowledge and understanding (DdD2)
After attending the course and after the final examination, the student:
is able to understand and draft a diagnostic and conservation work;
is able to summarize the results of a bibliographic research in the field of Heritage Science
3. Communication skills (DdD4)
The attending student will be able to present and communicate a selected and complex case study in Heritage Science.
These outcomes are expected to provide the student basic knowledge tools necessary for their involvement in the field of research in Heritage science, but at the same time it offers the opportunity to deepen the knowledge of some investigation techniques, in lab and onsite, and materials technologies.

Main topics:
What is Cultural Heritage: concept and legislation, What are the main international Entities for the preservation of Cultural Heritage
What are preservation and conservation, What is Heritage Science
Built Heritage: deterioration and conservation processes of stone materials and ceramics
Innovative materials and nanomaterials for the conservation
Mural Paintings: an introduction to material characteristics from pre-historic rock art to XXth century murals. Main deterioration and conservation problems. Street Art.
Painting on panel and canvas: painting materials and techniques over the ages. Pigments and binders and their deterioration.
Additional topics according to the case studies and visits
Metallic artifacts, bronzes: properties and deterioration problems, corrosion and conservation.
Street Art and contemporary muralism: properties and major deterioration problems.
Early polymeric materials used in objects and works of art. The challenges of conserving plastics in museum environmental conditions.
Paper, books and manuscripts: properties and deterioration phenomena.

Prova scritta facoltativa (a scelta dello studente), senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
For attending Students. The exam consists of a 20 min. oral presentation about a topic selected and agreed with the Teacher and based on a bibliographic research done by the Student in the current scientific literature available at https://www.biblio.polimi.it/; the colloquium will be completed by some questions about the general background on the same topic. The exam will be sufficient after a presentation characterized by sufficient clarity, synthesis and respect of assigned time. The Student should also demonstrate to be able to discuss similar arguments connected to those of the presentation and possess a general overview of the argument.
For not-attending Students. The exam consists of a written multiple choice test about the whole Program. The program is available on WeBeep, together with slides and other texts and documents, at the end of the semester. The test is composed by a number of multiple choice questions and 2 open questions. The Student should get a sufficient evaluation or higher (>18/30).

M. Matteini, R. Mazzeo, A. Moles, Chemistry for Restoration, Editore: Nardini Editore, Anno edizione: 2016, ISBN: 978-88-404-4450-5
Bastidas, D. M., & Cano, E. (Eds.), Advanced Characterization Techniques, Diagnostic Tools and Evaluation Methods in Heritage Science, Editore: Berlin: Springer., Anno edizione: 2018
Siegfried Siegesmund and Rolf Snethlage, Stone in Architecture: Properties, Durability, Editore: Springer, Anno edizione: 2014
David A. Scott, Gerhard Eggert, Iron and Steel: Corrosion, Colorants, Conservation, Editore: Archetype Books, Anno edizione: 2009
David A. Scott, Copper and Bronze in Art: Corrosion, Colorants, Conservation, Editore: Getty Trust Publications: Getty Conservation Institute, Anno edizione: 2002
Yvonne Shashoua, Conservation of Plastics: Materials science, degradation and preservation, Editore: Butterworth-Heinemann, Anno edizione: 2008
Baglioni, Piero, Chelazzi, David, Giorgi, Rodorico, Nanotechnologies in the Conservation of Cultural Heritage A compendium of materials and techniques, Editore: Springer Netherlands, Anno edizione: 2015, ISBN: 978-94-017-9302-5

The course SENSOR SYSTEMS will provide the basis for understanding how sensors work (light and image sensors, temperature sensors, magnetic field sensors, strain and force sensors, displacement, distance and proximity sensors, microphones) and for designing analog and digital front-end circuits. Through the course Students will become familiar with sensors and electronic components data-sheets and also with specific tools to program and debug ARM microcontrollers. During the hands-on labs, each student will practice with a microcontroller, using the main peripherals (GPIO, SPI, I2C, USART, PWM, ADC) and a sensor expansion board, for developing both the firmware and the proper hardware configuration, to test the conceived embedded system. The goal of the course is to enable master degree Students to select the best sensor for a specific application, develop an optimized front-end circuit and acquire the output signal with a microcontroller.
The course will include 24 hours of theoretical classes about sensors and front-end circuits and 26 hours of hands-on design laboratories. In particular, the NUCLEO board by STMicroelctronics, including a 32-bit STM32 microcontroller, will be used during the design laboratories.
3 CFUs will be delivered with Innovative Teaching modality as “Flipped Classroom”, in fact the Students will be asked to develop the projects before classes, in order to actively participate during the hands-on classes. Homework will be given through the course and their discussion will be objective of the exam. For the hands-on design laboratories the Students will organize in teams of 5 people that will collaborate to complete the assigned projects.

Knowledge and understanding (Dublin Descriptor #1)
Students will learn:
the working principle of the most used sensors
the parameters of practical interest for many sensors
the architecture and programming of an ARM microcontroller
Applying knowledge and understanding (Dublin Descriptor #2)
Students will apply their knowledge in practical case studies:
design the front-end circuit for practical case studies
acquire sensors output and display results with an STM32 microcontroller
Making judgements (Dublin Descriptor #3)
Given a practical case study and thanks to the Innovative Teaching, Students will:
analyze the critical issues
select the best sensor to be used
compare different solutions for the sensor front-end
think the best way to validate the system
Communication (Dublin Descriptor #4)
Students will learn to:
explain complex theoretical concept regarding the sensors working principle
express and justify their project choices
present the outcomes of their work in an effective way
Lifelong learning skills (Dublin Descriptor #5)
Students will be enabled to
read and understand sensors and electronic components data-sheets
develop a realistic project using the existing sensors
analyze new problems and find the best sensor and circuital solution to solve them

Sensors and front-end circuits (24 hours theoretical classes)
Basics on sensors: definitions, classifications and characteristics.
Working principle and front-end circuits of the most employed sensors:
light and imaging (photodiode, CCD and CMOS sensor);
temperature (RTD, thermistors, thermocouple, thermal diode, IR thermometer);
magnetic field (Hall sensors, magneto-resistances);
strain and forces (strain gauges, piezoelectric sensor);
displacement, distance and proximity (capacitive, inductive, acoustic, optical, magnetic, touch sensors);
audio (dynamic and condenser microphones).
Microcontrollers for embedded sensors systems (4 hours theoretical classes 26 hours design laboratories)
Introduction on ARM-Cortex M cores, STM32F4 microcontroller by STMicroelectronics peripherals and Nucleo development board.
Basics on the main microcontrollers peripherals (GPIO, PWM, ADC, SPI, I2C, USART, etc.) and simple projects (pushbutton, blinking LED, sending strings to remote terminal, using communication interfaces, etc.).
Sensors expansion board: hardware description and details of the sensors included in the board.
Hands on activities to interface sensors (temperature, light, sound, rotation, accelerations) with the microcontroller using its peripherals and providing an output (sending data to pc or displaying on an LCD or generating a sound through a speaker). Low power mode will be introduced for power saving smart sensors.
The main developed projects will include:
readout of a keybord;
communicate with a PC;
readout of a temperature smart sensor with I2C interface;
measure light intensity through a Light Dependent Resistor;
detect sounds with a microphone;
readout of a rotary encoder;
acquisition of the x, y, z accelerations with a tri-axial accelerometer;
communicate between boards with optical communication.

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
Valutazione continua facoltativa (a scelta dello studente)
Assessment will be based on:
10 weekly reports on the projects developed in teams (the reports will be graded on a sampling basis; at least two reports from each team will be assessed).
A written exam covering the entire course content (theory of sensors, microcontroller architecture, and programming for sensor readout). The written exam will include both multiple-choice and open-ended questions.
A short oral examination based on the 10 projects.
The reports and the oral examination together will account for 25% of the final grade, while the written exam will account for the remaining 75%.

Franco Zappa, Microcontrollers, Editore: Società Editrice Esculapio, Bologna, Anno edizione: 2017, ISBN: 9788893850223
Franco Zappa, Electronic Systems, Editore: Società Editrice Esculapio, Bologna, Anno edizione: 2012, ISBN: 9788893850858 www.editrice-esculapio.com/zappa-electronic-systems/

The course aims to provide students with a comprehensive understanding of the design, development, and application of smart materials for wearable devices in biomedical engineering. It will focus on the integration of advanced functional materials into wearable platforms for healthcare monitoring, rehabilitation, and human–machine interaction. Students will acquire the knowledge necessary to evaluate material properties, fabrication techniques, and integration strategies to meet the functional, ergonomic, and safety requirements of wearable biomedical devices.
Il corso mira a fornire agli studenti una comprensione approfondita della selezione, progettazione, sviluppo e applicazione di materiali intelligenti per dispositivi indossabili in ingegneria biomedica. Si concentrerà sull'integrazione di materiali funzionali avanzati in piattaforme indossabili per il monitoraggio sanitario, la riabilitazione e l'interazione uomo-macchina. Gli studenti acquisiranno le conoscenze necessarie per valutare le proprietà dei materiali, le tecniche di fabbricazione e le strategie di integrazione, al fine di soddisfare i requisiti funzionali, ergonomici e di sicurezza dei dispositivi biomedici indossabili.

At the end of the course, the student:
Knows and understands the fundamental principles of smart materials for biomedical applications, their classification, functional mechanisms, and the criteria governing their selection, with particular reference to wearable medical devices, including aspects related to biocompatibility, mechanical performance, durability, and sustainability. (DdD 1 - Knowledge and understanding)
Is able to select, integrate, and evaluate smart materials for wearable biomedical systems, applying engineering design methodologies to develop innovative solutions that satisfy specific functional requirements while considering manufacturability, mechanical behavior, long-term performance, and environmental impact. (DdD 2 - Applying knowledge and understanding)
Is able to critically analyze the scientific literature and emerging technologies in the field of smart wearables, justify design choices through evidence-based reasoning, and effectively communicate technical solutions and project outcomes using appropriate scientific and engineering terminology. (DdD 3, 4 and 5 - Making judgements, Communication skills, Learning skills)
Al termine del corso, lo studente:
Conosce e comprende i principi fondamentali dei materiali intelligenti per applicazioni biomedicali, la loro classificazione, i meccanismi funzionali e i criteri che ne guidano la selezione, con particolare riferimento ai dispositivi medici indossabili, considerando aspetti quali biocompatibilità, proprietà meccaniche, durabilità e sostenibilità. (DdD 1 - Conoscenza e comprensione)
È in grado di selezionare, integrare e valutare materiali intelligenti per sistemi biomedicali indossabili, applicando metodologie di progettazione ingegneristica per sviluppare soluzioni innovative che soddisfino specifici requisiti funzionali, tenendo conto della producibilità, del comportamento meccanico, delle prestazioni nel lungo periodo e dell'impatto ambientale. (DdD 2 - Capacità di applicare conoscenza e comprensione)
È in grado di analizzare criticamente la letteratura scientifica e le tecnologie emergenti nel campo dei dispositivi intelligenti indossabili, motivare le proprie scelte progettuali sulla base di evidenze scientifiche e comunicare in modo efficace soluzioni tecniche e risultati progettuali utilizzando un linguaggio scientifico e ingegneristico appropriato. (DdD 3, 4 e 5 - Autonomia di giudizio, Abilità comunicative e Capacità di apprendimento)

Introduction to smart wearable technologies in biomedical engineering
Classification and properties of smart materials, including shape-memory alloys and polymers, piezoelectric materials, electroactive polymers, conductive textiles, hydrogels, and bioresorbable materials
Fabrication techniques such as 3D printing, microfabrication, fiber spinning, and coating processes; biocompatibility and safety standards for wearable biomedical devices
Criteria for materials selection
Case studies on wearables for physiological monitoring, rehabilitation, sports science, and assistive technologies;
Challenges and trends like user comfort and regulatory frameworks
Ethics in wearable technology.
Introduzione alle tecnologie indossabili intelligenti in ingegneria biomedica
Classificazione e proprietà dei materiali intelligenti, inclusi leghe e polimeri a memoria di forma, materiali piezoelettrici, polimeri elettroattivi, tessuti conduttivi, idrogel e materiali bioassorbibili
Tecniche di fabbricazione come stampa 3D, microfabbricazione, filatura di fibre e processi di rivestimento; biocompatibilità e standard di sicurezza per dispositivi biomedici indossabili
Criteri per la selezione dei materiali
Casi di studio su dispositivi indossabili per il monitoraggio fisiologico, la riabilitazione, le scienze sportive e le tecnologie assistive
Sfide e tendenze come comfort per l’utente e quadri normativi
Etica nelle tecnologie indossabili

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
Assessment will be based on a combination of:
written exam (40%): evaluation of theoretical understanding of smart materials, integration strategies, and biomedical applications. The exam consists of multiple-choice and open questions. Time of the exam: 60 minutes
project work (40%): design and presentation of a wearable device concept integrating smart materials;
presentation and discussion of the project work (20%): active engagement in discussions, critical analysis of project work.
Project work will be carried out in small groups.
The total number of participants in the class will determine the number of members per group.
The assessment is designed to verify the achievement of the intended learning outcomes according to the Dublin Descriptors. Specifically:
the written examination assesses the acquisition of knowledge and understanding of smart materials, wearable systems, and biomedical applications (DdD 1);
the project report evaluates the ability to apply knowledge and understanding to the design, material selection, integration, and evaluation of smart wearable devices (DdD 2);
the project presentation and discussion assess the students' ability to critically justify design choices, discuss scientific and technological aspects, and effectively communicate engineering solutions using appropriate technical terminology (DdD 3, DdD 4, and DdD 5).
La valutazione si baserà su una combinazione di:
Esame scritto (40%): valutazione della comprensione teorica dei materiali intelligenti, delle strategie di integrazione e delle applicazioni biomediche. L’esame comprende domande a scelta multipla e aperte. Durata dell’esame: 60 minuti;
Lavoro di progetto (40%): progettazione e presentazione di un concetto di dispositivo indossabile che integri materiali intelligenti;
presentazione e discussione del lavoro di progetto (20%): partecipazione alla discussione, analisi critica del progetto svolto.
Il lavoro di progetto sarà svolto in piccoli gruppi.
Il numero totale dei partecipanti alla classe determinerà il numero di membri per gruppo.
La valutazione è finalizzata a verificare il raggiungimento dei risultati di apprendimento attesi, in coerenza con i Descrittori di Dublino. In particolare:
la prova scritta verifica l'acquisizione delle conoscenze e della comprensione dei materiali intelligenti, dei sistemi indossabili e delle loro applicazioni biomedicali (DdD 1);
la relazione di progetto valuta la capacità di applicare conoscenze e competenze alla progettazione, alla selezione dei materiali, all'integrazione e alla valutazione di dispositivi biomedicali indossabili basati su materiali intelligenti (DdD 2);
la presentazione e la discussione del progetto verificano la capacità dello studente di motivare criticamente le scelte progettuali, discutere gli aspetti scientifici e tecnologici e comunicare efficacemente le soluzioni ingegneristiche adottate utilizzando un linguaggio tecnico-scientifico appropriato (DdD 3, DdD 4 e DdD 5).

The course “Smart Wearables Design and Prototyping” provides advanced electronics design techniques for prototyping a smart wearable system by means of hands-on learning. Throughout the course students will become familiar with devices, processors, sensors and communication protocols used in low-power and small size wearables. The course addresses the fundamental concepts and tools for developing sensing electronics and low-power embedded processing, within the new cross-disciplinary Major on “Smart Wearable Technologies”. Case studies of industrial interest will be discussed.
The course includes 12 hours of theoretical basics for the design of smart wearables, including key design guidelines and architecture as well as examples and use cases, and 38 hours of design and hands-on laboratories, during which teams of 5 students will prototype their own smart eyewear, learning the whole process flow, from the PCB design, to the microcontroller firmware and smartphone app development.
Starting from templates of main-board (with STM32U5 microcontroller, BLE, flash, IMU, USB-C), firmware, glasses frame, smartphone app, each team will prototype a smart-eyewear, by designing sensors-probe (e.g. PPG, IMU, barometer, photodetectors, microphone), firmware, and smartphone app to display acquired data.

Knowledge and understanding (Dublin Descriptor #1)
Students will learn:
the architecture of a portable low-power electronic system
the working principle of key solid-state sensors and their interfaces
to design and properly size a sensor acquisition chain
the architecture and programming of an ARM microcontroller
the potential and constraints of embedded machine learning
to integrate the electronics with mechanics and software
Applying knowledge and understanding (Dublin Descriptor #2)
Students will apply their knowledge in practical case studies:
design of a PCB with sensors to be integrated in the frame
develop the firmware and software needed to acquire and process the sensors data
perform measurements and analyze the data
Making judgements (Dublin Descriptor #3)
Given a practical case study and thanks to hands-on experience, students will:
analyze the critical issues
select the best configurations and components to be used
select the best processing solution
think the best way to validate the system
Communication (Dublin Descriptor #4)
Students will learn to:
work in team
express and justify their project choices
Lifelong learning skills (Dublin Descriptor #5)
Students will be enabled to
read and understand sensors and electronic components data-sheets
develop an electronic project, starting from PCB design to realization and characterization

Smart Wearables Architectures
Introduction: course organization, design and hands-on lab activities team work
Smart wearable architecture, design, challenges (size, power consumption, embedded machine learning) and case-studies (eye tracking, biosensing, context awareness...)
Electronics development
Guidelines for PCB design (materials, power and signal routing, decoupling, high frequency path and termination)
Altium Designer layout CAD (schematic and layout, PCB stack, libraries, rigid/flex, gerber output standards)
Wearable Sensors (image sensors, MEMS…)
Batteries and power management fundamentals
Detailed schematics and layouts of main-board and sensing-probes to be used in the Labs
Design of the sensing probes (board layout, revision, generation of files for manufacturing)
Firmware development
Wearable and Low-Power Computing (MCU, embedded processing)
Communications (wired and wireless protocols)
Cube environment for programming STM32U5 (DMA, interrupts, HAL_functions, peripherals) with ultra low-power modalities
Detailed code of main-board and firmware modules (memory, USB and BLE communication, I2C communication with IMU, hands-on trials)
Design of the firmware for the sensors-probe (interfacing, data analysis, tests)
App development and project validation
Flutter tools for developing Apps (from a provided template)
Design of custom smartphone App (final tests)
Customization of the App to display data from the sensing-probe

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
Assessment will be based on:
A report on the project developed in teams (individual contributions will be taken into account).
A written exam covering the entire course content (both the theoretical topics and the developed project). The written exam will include both multiple-choice and open-ended questions.
A short oral examination based on the written exam and the project report.
Technical knowledge, critical analysis of the design choices, and communication skills will all be evaluated.
The project report and the written exam will each account for 50% of the final grade. The oral examination will be used to verify each student's individual contribution to the project and to clarify any ambiguous or uncertain aspects of the written exam.

Franco Zappa, Microcontrollers, Editore: Società Editrice Esculapio, ISBN: 9788893850223

Il corso affronta con approccio critico e analitico la storia delle tecnologie e delle infrastrutture digitali di comunicazione e sarà strutturato in due parti. Nella prima parte verranno affrontati argomenti teorici generali sulla storia e la diffusione di tecnologie chiave come le reti e le infrastrutture di comunicazione, il computer, il supercomputer e i dispositivi mobili di comunicazione, analizzando casi di successo e di fallimento. Nella seconda parte, in collaborazione col Museo Nazionale della Scienza e della Tecnologia Leonardo da Vinci, gli studenti dovranno attivarsi, in maniera guidata, per ricostruire la storia di alcune tecnologie a partire da modelli/documenti/esemplari presenti nelle collezioni del museo.

The primary objective of the course is for students to learn the fundamental abstractions and features of software architectures and platforms for wearable and pervasive computing, while gaining concrete practical experience on their use in different application scenarios. The course targets concrete embedded system implementations of the Internet of Things and mobile wearable platforms. It promotes a practical approach, as the explanation of system abstractions and features is tightly linked to their practical application. The lecturers adopt a novel teaching methodology that enables students to invest most of their time in in-class lab sessions and hands-on work, with support from the lecturers.
For each software platform covered in the course, the learning activities use a combination of online videos and in-class lab sessions. The online videos introduce abstractions and fundamental features of a given concept, system, or platform. The lab sessions allow students to clarify doubts with the lecturers and experiment with the technology at hand. In specific cases, dedicated hardware for experimentation will be provided by the lecturer, including, for instance, development kits mimicking resource-rich wearable computing devices or resource-constrained embedded platforms.

By the end of the course, students are expected to be familiar with the fundamental concepts and abstractions of embedded software for wearable and pervasive computing, including Internet of Things and mobile wearable systems, as well as with platforms to build concrete prototypes. Students will be acquainted with the practical issues arising when deploying and running these systems in realistic settings. The Dublin Descriptors applicable to the course are:
Dublin Descriptor 1 (knowledge and understanding): the lab sessions of the course provide the opportunity to test the students’ understanding while facing the practical issues in deploying and running wearable computing systems.
Dublin Descriptor 2 (applying knowledge and understanding): the students apply the learned abstractions and features of each software platform in concrete application cases, drawing connections between theoretical concepts and experimentation.
Dublin Descriptor 5 (lifelong learning skills): the students are expected to acquire the ability to solve medium-complexity experimental problems on their own, using either the software platforms covered in the course or others that are based on similar abstractions or offering similar features.

For each of the software abstractions and platforms the course covers, we provide first an introduction to the specific domain, followed by a description of key abstractions and features, intertwined with concrete examples. The course includes resource-rich mobile platforms programmable with high-level languages, down to the development of embedded firmware for a resource-constrained pervasive devices, typically programmed using a dialect of the C language. To ease the learning experience, we use prototyping devices representative of the different classes of computing devices, from RaspberryPI single-board computers to STMicroelectronics embedded development kits. Sensor peripherals are covered both when physically integrated with the computing device and when wirelessly connected, for example, through Bluetooth LE.

Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, con revisioni periodiche, con presentazione/discussione finale
Valutazione continua facoltativa (a scelta dello studente)
Altro
The coursework is to be accomplished in student groups. The group evaluation may adopt two different modes: either in continuous evaluation or based on research projects.
The goal of the continuous evaluation mode (DD2, DD5) is to combine the students’ learning with in-class experimental activities that allow the teachers to assess their preparation as learning proceeds throughout the semester. In addition to online videos, in-class Q&A sessions, and lab sessions, dedicated evaluation labs are scheduled throughout the semester. The evaluation labs build on what the students learned and experimented with online videos and regular lab sessions, and are instrumental in checking their understanding of a given platform. During an evaluation lab, each student is given an online questionnaire to be completed individually first; next, the students complete a group implementation exercise. The work accomplished at the end of an evaluation lab is graded by the lecturers and contributes to determining the final grade. This model allows students to complete the coursework by the end of the lecture period.
The research project model (DD2, DD5), differently, seeks to test the students’ preparation by requiring the design and implementation of one or multiple medium-complexity software artifacts, based on current research activities at NESLab (www.neslab.it). The quality of the implementation, the correct use of the system's features, and a formal presentation in front of the teacher(s) concur to determine the course assessment. The work may be carried out during the semester; group presentations are normally scheduled during the exam period after the end of the lecture period
Groups may start with the continuous evaluation mode and possibly switch, at any time during the semester, to the research project mode.

Teaching material provided by the lecturer

L a raccolta di dati sperimentali provenienti da prove e misure su materiali ha un ruolo significativo nell’Ingegneria dei Materiali, così come la capacità di utilizzare strumenti informatici adatti a ll a rappresentazione e all ’analisi di tali dati. Il corso si propone quindi di introdurre gli allievi all’applicazione delle nozioni fondamentali della statistica e del calcolo numerico per analizzare criticamente una selezione di dati fisico-chimici che verranno acquisiti in sede di esercitazione (es. misure meccaniche, spettroscopiche, termiche). Il corso si propone di rendere autonomi gli allievi nell’uso personale d e i programmi Excel e Matlab per la rappresentazione e l’analisi critica di dati sperimentali.

Il corso si propone di introdurre gli allievi alla sperimentazione industriale attraverso l’analisi e la rappresentazione dei dati. Verranno richiamate ed affrontate in forma operativa e applicata le basi statistico-matematiche su cui poggiano tali strumenti. Gli allievi saranno guidati all’uso delle tecniche proposte nel corso tramite un approccio in prima persona ( learning by doing ) agevolato da i pacchetti Excel e Matlab, che offrono ampie possibilità di analisi e rappresentazione di dati, e validi strumenti di programmazione. A seguito del superamento dell'esame, gli allievi saranno in grado di:

• apprezzare gli aspetti statistici insiti nel processo sperimentale di una misura (DdD1)
• utilizzare l'ambiente Excel per le operazioni fondamentali di analisi e rappresentazione grafica di dati sperimentali (DdD2)
• utilizzare l'ambiente Matlab per realizzare programmi volti all’analisi e alla rappresentazione di dati sperimentali (DdD2).

Teoria dell’errore. La misura di una proprietà; riproducibilità delle misure; accuratezza e precisione. Esempi di strumentazione di interesse per l’ingegneria dei materiali; esame di dati reali.
Utilizzo di Excel. Importazione di dati; disegno di tabelle; utilizzo di funzioni predefinite; disegno di grafici. Calcolo di somme; approssimazione di serie; approssimazione di integrali definiti.
Programmazione in Matlab. Definizione di variabili; utilizzo di funzioni predefinite; condizione if; cicli for e while; costrutto switch. Disegno di grafici.
Regressione lineare, polinomiale e non lineare secondo il metodo dei minimi quadrati. Risoluzione numerica del problema dei minimi quadrati.
Analisi di dati multivariati. Introduzione alla Principal Component Analysis e alla Cluster Analysis

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
L’esame prevede due fasi distinte, valutate separatamente.
1. Test in aula sui contenuti del corso.
2. Realizzazione di un progetto di analisi dati. L'esecuzione del progetto richiede l'assemblaggio e l'utilizzo critico di un codice Matlab a partire da elementi tratti dal materiale didattico fornito durante il corso. A conclusione del progetto, lo studente redige e consegna un elaborato sintetico che documenta quanto realizzato. Il progetto viene realizzato dallo studente in modalità open book, utilizzando materiale didattico fornito durante il corso e risorse presenti in rete (manuale Matlab).
Il voto finale è dato dalla media pesata delle valutazioni 1(peso 2/3) e 2 (peso 1/3).
I principali descrittori di Dublino verificati nell'esame sono:
(per la fase 1) Conoscenza e comprensione, Capacità di apprendimento;
(per la fase 2) Capacità di applicare conoscenza e comprensione, Autonomia di giudizio.

Dispense
Note: Note: Sono distribuite dispense, note di lezione e tutte le slide presentate durante il corso. Sono indicati riferimenti bibliografici puntuali su argomenti specifici. Sono distribuiti tutti gli script Matlab discussi, e i dati sperimentali oggetto delle tecniche di analisi.

Main aim of this course is providing, by means of statistical and probabilistic methods, a rationale for the thermodynamic description and a bottom-up description of the structural and dynamic properties of materials. These concepts are then applied to the description of systems of specific interest in engineering applications, such as liquids, plasmas, disordered solids (glasses and gels) and magnetic materials, and to investigate physical problems of technological relevance such as phase transitions, superfluidity and superconductivity.

At the end of the course, students should
Know and master the fundamental ideas of statistical physics and the basic tools, such as statistical ensembles and partition functions, which allow to relate the microscopic world to the macroscopic realm of thermodynamics;
Manage to apply the former general concepts to topics in condensed matter and material science of interest for their specific course of studies.
Be capable of conveying the knowledge they have acquired with plainness but also with accuracy and the ability to synthetize the argument (even for an audience that may not be as learned in the field as their teacher).

1. Review of probability and statistics. Probability distributions of interest in physics. Statistical entropy. Brownian motion as a paradigm of stochastic processes in physics.
2. From the macroworld to the microworld. Review of thermodynamics. Physical description of classical and quantum many-particle systems. Phase space and Liouville theorem. Density of quantum microstates and its dependence on total energy.
3. Foundations of statistical mechanics. Boltzmann fundamental postulate. Microcanonical distribution and thermodynamics. Gibbs paradox and Maxwell-Boltzmann (MB) approximation.
4. Closed systems and canonical distribution. Partition function and free Energy. Average value and fluctuations of internal variables. Canonical distribution for independent particles and system of indistinguishable particles. Classical monatomic gas. Polyatomic gases. Einstein and Debye models for the specific heat of solids. Phonons in solids. Paramagnetism and diamagnetism. Bohr-van Leewen theorem.
5. Mean field theories. Van der Waals model of the liquid-gas transition. Ferromagnetism and Curie-Weiss theory. Plasmas in the Debye-Hückel approximation.
6. Open systems and gran-canonical distribution. Chemical potential. Langmuir adsorption isotherm. Inhomogeneous systems and local chemical potential. Colloidal dispersions. Quantum statistics. Weakly-degenerate gases. Fermi gas and Sommerfeld theory for electrons in metals. Degenerate Bose gas and Bose-Einstein (BE) condensation. Laser cooling and BE condensation in dilute gases confined by magneto-optical traps.
7. Highly correlated systems. Liquid structure. Density correlations and structure factor. Superfluidity in 4 He and Landau-Tisza phenomenological model. Superconductivity (hints).

Prova orale obbligatoria
The evaluation consists in an oral examination, structured so to ascertain whether and to what extent the goals of the course have been reached. Specifically, the student is first asked to give a concise introduction to one of the general subjects discussed in the course, so to check her/his skills in summarizing and framing an argument. Practical mastering of the acquired knowledge is then verified by asking the student to explicitly derive some of the main results related to that topic (namely, the student should be able to combine synthetic with analytic skills), focusing on a specific application of the discussed subject to the main subject of his/her studies. For the overall evaluation, a yardstick of the student’s performance, besides the clarity and effectiveness of the exposition, will also be the skill in using an appropriate scientific language and the readiness in finding connections between different topics. The final assessment will also take into account the degree of incremental knowledge acquired by student with respect to her/his starting level.

Roberto Piazza, Statistical Physics: A prelude and fugue for engineers, Editore: Springer, Anno edizione: 2017, ISBN: 978-3-319-44536-6
Note: This is the official textbook of the course, which can be FREELY DOWNLOADED by all the students from
https://link.springer.com/book/10.1007/978-3-319-44537-3
using their PoliMi credentials

1. Introducing Stochastic Calculus and its rules.
2. Introducing Stochastic Differential Equations.
3. Showing their utility in modelling evolutions with noise in different contexts such as Finance, Engineering, Chemistry, Physics.

Knowledge and understanding
Lectures and exercise sessions will allow students to

• know the fundamental elements of stochastic processes (Brownian motion, martingales and Markov processes), stochastic calculus and stochastic differential equations;
• understand the applications: there are very many situations (in finance, telecommunications, control,...) where stochastic processes, and in particular diffusions, are a natural model;
• understand the connections with other branches of pure mathematics, in particular with partial differential equations.
  Ability in applying knowledge and understanding
  Lectures and exercise sessions will allow students to
• manipulate the basic notions of stochastic calculus acquiring elements of the theory and ability to work with them;
• have an adequate knowledge of the techniques to formalize and to model evolutions with noise in different contexts of science and technology;
• learn the constructive mathematical proofs of some key result of the theory;
• acquire the ability to present in a clear and precise way the models and the results of the theory.

1. Elements of Probability. Probability spaces, random variables. Variance, covariance, probability distribution, density. Independence. Random vectors. Convergence of random variables. Characteristic functions. Gaussian laws. Measurability theorems.
2. Stochastic processes. Filtrations. Trajectories. Equivalent processes, modifications, indistinguishable processes. Finite-dimensional laws and Existence Kolmogorov’s theorem. Kolmogorov’s continuity theorem. Stopping times.
3. Brownian motion. Definition and basic properties. Finite-dimensional distributions. The White Noise.
4. Conditional probability. Conditional expectations. The augmented Brownian filtration.
5. Martingales. Definitions and basic properties of continuous time Martingales.
6. The stochastic integral. Elementary processes. The stochastic integral. Ito Isometry. The stochastic integral as a process. Stopping theorems. Local martingales.
7. Stochastic calculus. Stochastic differential of an Ito process. Ito's Lemma. Girsanov’s Theorem. The martingales of the Brownian filtration.
8. Stochastic Differential Equations. A class of SDE. Definition of solutions. Existence and Uniqueness theorems for the solution. SDE and Markov processes. Connections between SDE and PDE. Feynman-Kac formula.

Prova scritta obbligatoria, senza prove in itinere
The exam consists of a written test, divided into two parts denoted, respectively, A and B. Part A contains theoretical questions in order to ascertain the understanding of the basic notions (definitions, theorems, proofs, counterexamples) of stochastic processes, stochastic calculus and stochastic differential equations.
Part B contains 2 exercises, each one containing 7 – 10 questions, on

• stochastic processes, and in particular Brownian Motion, and their properties;
• conditioning, martingales and their applications in the investigation of stochastic processes;
• stochastic calculus: stochastic integral and Ito’s formula;
• stochastic differential equations: existence and uniqueness of the solution and its properties.
  During the written test it is not allowed neither to consult documents or archives nor to communicate with other people. In particular it is not allowed to keep on the mobile phone or some other device allowing to communicate, or to use internet, to consult documents.
  The candidate will be asked to solve problems with computations and to justify rigorously all passages. He/she will be required to possess the elements of the theory and to be able to manipulate the theoretical notions.
  The evaluation of the written test will take into account the clarity in the exposition of the used procedures, the skill to motivate them, the accuracy in the computations, the familiarity with mathematical language.
  To pass the exam, it is required a positive assessment both in the part A and in the part B. The maximum grade of A+B is 30/30. In order to pass the exam, the student must get at least 18/30. In some cases, if it is requested by the professor, the written examination could be followed by an oral one. To get the laude the oral exam is mandatory.
  No mid-term test is planned.
  THE EXAM STRUCTURE COULD BE MODIFIED IN CASE OF EMERGENCY.
  The exam has the goal of checking whether the student has acquired the following skills:
  knowledge of some basic concepts of stochastic analysis which are helpful in several applications
  knowledge of the proofs of some fundamental theorems in stochastic integration theory, stochastic calsulus and stochastic differential equations
  understanding of the range of applicability of various results through counterexamples
  ability to state rigorous definitions of the presented notions
  ability to prove some of the presented theorems
  ability to apply the presented theory to solve given problems
  finding simple counterexamples
  communicating mathematical concepts in a clear and rigorous way

P. Baldi, Stochastic Calculus, Editore: Springer, Anno edizione: 2017, ISBN: 978-3-319-62225-5
P. Baldi, Equazioni differenziali stocastiche e applicazioni, Editore: Pitagora Editrice, Bologna, Anno edizione: 2000, ISBN: 88-371-1211-4
F. Caravenna, Moto Browniano e Analisi Stocastica, Anno edizione: 2011 http://www.matapp.unimib.it/~fcaraven/download/other/dispense-3.3.pdf
B. Oksendal, Stochastic Differential Equations, Editore: Springer, Anno edizione: 2000, ISBN: 3540637206
S.E. Shreve, Stochastic calculus for finance - Continuous-time models (Vol 2), Editore: Springer, Collana Springer finance, Anno edizione: 2004, ISBN: 0387401016

The aim of this course is to provide a simple and accessible introduction to discrete and time-continuous Markov chains, the simplest mathematical model for random phenomena evolving in time, with applications to queueing models, biological models, Markov chain Monte Carlo, reliability. Moreover, a wide variety of models will be illustrated to provide the student with useful tools to apply the methods in new situations. A brief introduction to martingale theory and methods with applications to Markov chains will also be given.
Lectures and exercise sessions will allow students to:

• Analyze and understand the goals, assumptions and requirements associated with Markovian models in discrete and continuous time
• Identify situations in which the Markov property may hold or not
• Learn the most relevant models (random walks, birth and death processes, random queues)
• Learn methods for computing the relevant probabilities and mean times
• Study the behaviour of the random evolution

Learn the fundamental mathematical models with discrete and continuous time Markov chains and martingale methods. Learn the constructive mathematical proofs of some key result of the theory. Understand the key structures underlying Markov chain models.
Analyze random phenomena discriminating those that can be described by Markov chains and design the corresponding models with a correct scientific approach. Develop new ideas and solutions to find quantitative rigorous solutions in new situations creating new models.
Acquire the ability to present in a clear and precise way the models and results of the theory.

1. DISCRETE TIME MARKOV CHAINS. Transition kernels. Markov chains, classes of states and their structure, irreducibility, periodicity, transience and recurrence. Random walks, recurrence and transience of random walks, binary communication channels. Invariant distributions (discrete and continuous state space). Recurrence and transience. Harris recurrence. Stopping times and strong Markov property. Hitting times and absorption probabilities. Mean absorption times. Application to ruin problems (ruin probability and mean ruin time). Empirical means and ergodic theorem. Reversibility. Applications to queueing models and population models. Lyapunov functions and Foster criteria. Exponential convergence to invariant distributions and Doeblin condition. Monte Carlo methods, Metropolis algorithm, binomial model.
2. CONTINUOUS TIME MARKOV CHAINS. Consistent families of probability distributions, Kolmogorov’s theorem, canonical processes. Trajectories and modifications. Transition rates, Chapman-Kolmogorov equation and transition semigroup, forward and backward Kolmogorov equations. Transition rate matrices and their exponentials. Markov property and exponential sojourn times, jump chain and holding times of a continuous time Markov chain. Invariant distributions, ergodic theorem and convergence to invariant distributions. Poisson process, independence of increments. Birth and death processes. Non-minimal chains and explosion in finite time. M/M/1 and M/M/k queues and performance indices. Epidemic models.
Renewal processes: law of large numbers and central limit theorem. Hidden Markov Models.
3. MARTINGALES. Martingales, supermartingales and submartingales. Modelling a player’s fortune. Filtrations and information. Stopping theorem. Maximal inequality and Doob inequality. Martingales of a Markov chain, Lyapunov functions and submartingales.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The exam consists of a written test (compulsory) and an oral examination. The written test consists of two parts denoted, respectively, A and B. Part A contains theoretical 2-3 questions in order to ascertain the understanding of the basic notions (definitions, theorems, proofs, counterexamples) of stochastic processes, Markov chains, applications and models. Part B contains 2 exercises with 5-6 questions (sometimes three shorter exercises) whose solutions require the theoretical, Markov chain (both discrete and continuous time), martingale and modelling tools developed through the course.
During the written test it is not allowed to keep the mobile phone or some other device allowing to communicate, or to use internet, to consult documents, except the course lecture notes (teacher’s notes or your own personal notes).
The candidate will be usually asked to construct the mathematical model that he will analyze to obtain quantitative information (probabilities, mean times …), to solve problems with computations and to rigorously justify all steps. He/she will be required to possess the elements of the theory and to be able to manipulate the theoretical notions and apply them rigorously and correctly in the construction and quantitative analysis of models.
The evaluation of the written test will take into account the clarity in the exposition of the used procedures, the skill to motivate them, the accuracy in the computations, the familiarity with mathematical language.
The maximum grade of A+B is 30/30. In order to pass the exam, the student must get at least 18/30. The teacher may require an oral exam in addition to the written one. As a general rule, this will apply to students who achieved a score of 26 or higher on the written test. Any oral examination will serve to confirm the assessment of the level of learning resulting from the written exam. (Additional note: it is not to be considered an additional test to "improve the result," as no such provision exists).To get the laude the oral exam is mandatory. No mid-term test is planned.
The exam has the goal of checking whether the student has acquired the following skills:
Knowledge of some basic concepts of stochastic processes, especially Markov processes and martingales, which are helpful in several applications
Knowledge of the proofs of some fundamental theorems in the theory of discrete and continuous Markov chains and martingales,
Ability to construct Markov models in concrete applications,
Understanding of the range of applicability of various results through counterexamples
Ability to state rigorous definitions of the presented notions
Ability to prove some of the presented theorems
Ability to apply the presented theory to solve given problems
Finding simple counterexamples
Communicating mathematical concepts in a clear and rigorous way

J.R. Norris, Markov Chains, Editore: Cambridge University Press, Anno edizione: 2012, ISBN: 9780511810633
Lecture Notes
Note: available on WeBeep

Il corso intende introdurre lo studente al tema dello sviluppo sostenibile e al ruolo della tecnologia nella complessità delle sfide globali. Lo sviluppo sostenibile mira a identificare modelli innovativi per la Prosperità economica compatibili con il rispetto del Pianeta e l'inclusione sociale di tutte le Persone. In questa prospettiva il razionale e le logiche che sottendono a tale paradigma dello deve essere analizzate e compreso dalle generazioni di ingegneri che lavoreranno in contesti sempre più globali e inserito come elemento caratterizzante per una professione che inevitabilmente è in continua evoluzione. L'interesse per questi temi è evidente e ben esplicitato nell'Agenda 2030 delle Nazioni Unite e nei 17 obiettivi di Sviluppo Sostenibile, nella visione di una Europa della conoscenza e nell'interdipendenza globale marcata dagli effetti diretti e indiretti delle crisi economiche e sociali mondiali sui sistemi tecnologici e economici

Le aziende che operano in settori ad alta tecnologia devono continuamente adeguare le proprie capacità al ritmo dell'innovazione e alle sfide del cambiamento tecnologico, come la crescente complessità dei moderni sistemi cyber-fisici. In questo contesto, il Technology Risk Governance (TRG) comprende l'insieme di strategie, metodi e modelli organizzativi che le organizzazioni mettono in atto per comprendere, valutare e gestire le incertezze e i rischi legati alla tecnologia.
Il corso mira a trasferire agli studenti conoscenze teoriche e pratiche sulla TRG, anche attraverso la discussione in classe di casi reali e testimonianze di aziende, che copriranno un ampio spettro di sistemi industriali e di servizi (ad esempio, produzione, energia, trasporti). Il corso affronta lo stato dell'arte degli approcci, dei metodi e dei modelli per supportare decisioni informate sul rischio nella gestione di sistemi socio-tecnici complessi, dalla selezione della tecnologia alla progettazione del sistema, dai fattori di rischio organizzativi alla regolamentazione e alla governance.

Obiettivi:
L’insegnamento si propone di fornire allo studente le conoscenze base sulle tecnologie di fabbricazione di nanostrutture e sugli interessi applicativi dei nanomateriali. Nonostante siano privilegiati gli approcci bottom-up, sono trattate anche alcune tecnologie top-down di interesse industriale. Nel caso di nanostrutture zero-dimensionali e mono-dimensionali le conoscenze teoriche sono accompagnate da attività sperimentali di laboratorio, in particolare, sono preparate nanoparticelle metalliche e nanofibre polimeriche. Infine, vengono forniti gli strumenti per lo studio della morfologia mediante analisi delle immagini.

A seguito del superamento dell’esame lo studente:
Per tutte le classi di strutture considerate, conoscerà i principi fondamentali della crescita mediante tecniche bottom up (DdD1); sarà in grado di valutare gli effetti dei parametri sperimentali che governano la crescita della nanostruttura (DdD2);
conoscerà le tecniche top–down di preparazione di nanoparticelle e saprà valutare i vantaggi e gli svantaggi in funzione delle possibili applicazioni (DdD1);
conoscerà le principali tecnoclogi di produzione di nanofili/nanobacchette, sia di tipo inorganico che di tipo polimerico (DdD1);
conoscerà le principali tecniche di deposizione in vuoto e le principali reazioni in fase vapore per la formazione di film sottili e film molecolari (DdD1);
sarà in grado di preparare nanoparticelle metalliche, applicando le nozioni acquisite con le lezioni frontali, e di valutarne le proprietà di assorbimento nel visibile in funzione delle dimensioni (DdD2);
sarà in grado di preparare membrane di nanofibre polimeriche, applicando le nozioni acquisite con le lezioni frontali, e variarne la morfologia in funzione delle variabili di processo (DdD2);
sarà capace di applicare gli strumenti di analisi delle immagini (Fiji/image J) e ricavare dimensioni medie e distribuzione delle dimensioni (DdD2);
sarà in grado di comprendere la letteratura del settore in modo da affrontare successivi approfondimenti (DdD2).

Strutture “zero-dimensionali”: nanoparticelle
Preparazione di nanoparticelle mediante tecniche top-down
Sintesi per nucleazione omogenea: nanoparticelle metalliche, nanoparticelle di semiconduttori non ossidi, nanoparticelle di ossidi (sol-gel); nanoparticelle in matrice solida
Sintesi per nucleazione eterogenea
Sintesi cineticamente confinata: aerosol, pirolisi spray, sintesi in micelle.
Cenni di plasmonica e di spettroscopia di assorbimento UV-vis
Cenni di applicazioni di nanoparticelle
Laboratorio sperimentale: preparazione e caratterizzazione di nanoparticelle d'argento
Laboratorio informatico: analisi di immagini e calcolo delle dimensioni di nanoparticelle
Strutture mono-dimensionali: nanofili e nanobacchette (nanowire e nanorod)
Crescita spontanea per evaporazione-condensazione
Crescita spontanea vapore-liquido-solido (VLS); crescita spontanea soluzione-liquido-solido
Sintesi basata su template: elettrodeposizione, riempimento
Electrospinning
Cenni di applicazioni di nanofibre
Laboratorio sperimentale: preparazione e caratterizzazione di nanofibre polimeriche mediante electrospinning
Laboratorio informatico: analisi di immagini e calcolo delle dimensioni di nanofibre
Strutture bidimensionali
Tecniche di deposizione di film sottili: dip coating, casting, tecnica Doctor Blade, wire bar coating, roll-to-roll, spin coating, flexografia, serigrafia, stampa a getto di inchiostro.
Deposizioni fisiche in fase vapore: evaporazione, MBE, sputtering.
Deposizioni chimiche in fase vapore; Atomic Layer Deposition
Self-assembly
Langmuir-Blodgett
Film sol-gel
Oltre ai testi consigliati verrà reso disponibile materiale didattico a cura del docente.

Prova scritta obbligatoria, con prove in itinere
L’esame consiste in una prova scritta così organizzata:
una domanda aperta su uno dei principali argomenti trattati
Ha lo scopo di verificare le conoscenze di un argomento nel suo complesso. Della risposta alla domanda aperta sono valutati non solo la completezza e grado di approfondimento della discussione, ma anche la proprietà di linguaggio e la chiarezza nella descrizione.
un esercizio numerico
Della stessa tipolgia di quelli affrontati durante il corso, si tratta nella maggior parte dei casi di determinare l'area superficiale in funzione delle caratteristiche geometriche e il suo ruolo nella determinazione di alcune proprietà funzionali. Nell'esercizio potrebbe talvolta essere richiesto di applicare alcune nozioni di chimica base e stechiometria, come trattato durante il corso. L'esercizio può richiedere di applicare le conoscenze e gli strumenti acquisiti per operare scelte, oppure di analizzare e giustificare dati sperimentali mediante un ragionamento.
un esercizio di analisi di immagini
ha lo scopo di verificare la capacità di applicare a casi concreti il software di immagini Fiji (image J)
una domanda puntuale
Mira a verificare la conoscenza nel dettaglio degli argomenti trattati
una serie di domande a risposta multipla
Ha lo scopo di valutare la conoscenza di tutti gli argomenti dell’insegnamento.

Peter Atkins, Julio de Paula, Physical Chemistry, Editore: Oxford University Press, Anno edizione: 2006, ISBN: 9780198700722
Lecture notes and slides
Note: Lecture notes and slides will be distributed during the course.

Technology is one of the most pervasive features of modern society and the impact of technology on society has been a topic of longstanding concern. This course cultivates a critical approach to technology that highlights how technological objects, both old and new, are shaped by the social and cultural contexts in which they are designed, manufactured, and used.
Students are introduced to a corpus of studies that looks at how social relations and society shape technology and get ‘inside technology’, which highlights how the very design of technology (including the artifacts themselves) embodies important social, cultural, ethical, and political assumptions that inform how we use, value, or reject technology. Readings will examine some of the recent work by sociologists, anthropologists, historians, philosophers as well as feminist and postcolonial scholars who have provided new insights into our understanding of the development and dissemination of technology. Throughout we will look at studies of various technologies, such as bridges, the bicycle, the microwave, birth control, financial markets, and the internet.
Students will apply the conceptual and methodological tools from the discussed literature in a case study of a contemporary or emerging technology. This will allow for a more in-depth exploration of the conceptual frameworks provided during this course through the examination of the particular issues and concerns related to a specific technology. Students will critically examine how their selected technology shapes and is shaped by the social context in which it is produced and consumed, and what the implications are of these processes. Throughout the course, students are expected to play an interactive, collaborative, and participatory role by engaging in class discussions, writing exercises, presentations, and peer review.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will:
Acquire a broad critical perspective on the ethical and social impacts and implications of technology;
Learn how to recognize and analyze ethical and social aspects and issues inherent to old and emerging technologies;
Be able to understand how technical problems are informed by the socio-cultural contexts in which they occur.
Applying knowledge and understanding
Students will:
Be able to use critical skills in identifying and analyzing technological case studies;
Be able to apply sociological theories to problems created, aggravated or transformed by technologies;
Be able to explore and assess possibilities for addressing social problems generated by technologies.
Making judgements
Students will be able:
To independently analyze the social impact of a technology and the social processes that inform technological development and use;
To evaluate and select the appropriate critical approaches to understanding and addressing social problems generated by technology.
Communication
Students will learn to:
Exercise and improve their scholarly writing skills;
Present the results of their independent research in an effective manner;
Justify their arguments clearly and coherently.
Lifelong learning skills
Students will:
Be better prepared for their future professional life as a critical thinker and communicator;
Be better able to engage critically with issues that affect the society they live in;
Be better able to analyze problems through a theoretical lens.

The course consists of a series of interactive lectures dealing with major topics in social studies of technology. Students are required to come to class prepared and expected to actively participate.
Lectures will discuss a number of approaches central to a critical examination of technology, such as the Social Construction of Technology (SCOT), Feminist and Postcolonial studies of Technology, and Disability Studies. Students will also be introduced to several key conceptual and methodological approaches to technology studies, such as the role of technoscientific controversies, accidents and disasters; studies of maintenance and repair; technological determinism; social constructivism; the agency of artifacts; scripts and affordances; sociomateriality, and cyborg embodiment. During the lectures, students will also be introduced to case studies of contemporary and emerging technologies.
For the final essay, students are expected to focus on the social, ethical, and political dimensions of a topic or case of their choosing, rather the technical aspects of a certain technology. Students are also expected to give a final (group) presentation on a topic of their collective choosing. This topic can be, but need not necessarily be, related to the invidual papers of each student in a group. If students cannot present, they need to submit an additional (shorter) essay.
Students who take the course for 3 CFU instead of 5 CFU can submit a shorter essay.

Altro
Type of assessment
Description
Dublin descriptor
Collective presentations
Develop an argument in which the presented claims are justified by use of relevant literature and sociological argumentation and evidence;
To present this argument in an oral presentation in class in front of the instructor and their fellow students.
1, 3
4, 5
Individual written essay
The individual final essay demonstrates that a student has the capacity to read and engage with the relevant literature, and the ability to apply relevant concept and methods to a case study;
The individual final essay demonstrates an ability to conduct a thorough sociological analysis of a technology, and to communicate the results of this analysis in a clear and comprehensible manner;
The individual final essay discusses the social dynamics that are associated with their chosen case study of a contemporary or emerging technology, and critically reflect on this technology’s social impact and implications.
1, 2
3, 4, 5

H.M. Collins & T. Pinch, The Golem at Large: What you should know about technology
Ron Kline, Technological Determinism
Note: in International Encyclopedia of the Social & Behavioral Sciences, 2001 (pp. 15495-15498)
Leo Marx, Does Improved Technology Mean Progress?
Note: (1987) Technology Review, 71, 33-41.
Kline, R. & Pinch, T (1996)., Users as Agents of Technological Change: The Social Construction of the Automobile in the Rural United States.
Note: Technology and Culture, 37(4), 763-795.
David, P.A., Clio and the Economics of QWERTY
Note: (1985). The American Economic Review, 75(2), 332-337.
Judy Wajcman, Feminist Theories of Technology
Note: (2010). Cambridge Journal of Economics, 34, 143-152.
Nakamura, L., Indigenous Circuits: Navajo Women and the Racialization of Early Electronic Manufacture
Note: (2014). American Quarterly 66(4), 919-941
Audre Lorde, The Master's Tools Will Never Dismantle the Master's House.
Note: 1983
David Edgerton, The Shock of the Old: Technology and Global History Since 1900
Note: 2007
Couldry, N., Mejias, The Costs of Connection: How Data is Colonizing Human Life and Appropriating It for Capitalism
Note: (2019). Stanford University Press
Noortje Marres, The Costs of Public Involvement: Everyday devices of carbon accounting and the materialization of participation.
Note: (2011). Economy and Society, 40(4), 510-533.
Langdon Winner, Do Artifacts Have Politics?
Note: 1986
David F Noble, Social Choice in Machine Design: The Case of Automatically Controlled Machine Tools
Judy Wajcman, Technofeminism
Note: 2004

Elementi di algebra e topologia: relazioni di equivalenza e insiemi quoziente; campi finiti; spazi topologici e funzioni continue. Omologia simpliciale: simplessi e complessi simpliciali; catene e operatori bordo; gruppi di omologia. Suddivisioni baricentriche e approssimazione simpliciale. Omologia persistente e diagrammi di persistenza. Topological data analysis.

The course aims to provide students with the skills necessary to contextualise, define, gather requirements for, and implement smart systems, with a particular emphasis on wearable devices. These systems, designed to interact seamlessly with their environment, integrate computational capabilities, communication technologies, and control mechanisms. Wearable systems—ranging from smartwatches to health monitors and augmented reality headsets—represent a subset of cyber-physical systems that are increasingly embedded into everyday life.
Wearables typically rely on low-power microcontrollers, specialised sensors and actuators, and various communication interfaces (Bluetooth, Wi-Fi, LTE, etc.). These systems collect and process contextual data in real-time to adapt to the needs and behaviour of the user, often operating as part of larger interconnected ecosystems.
The framework for this learning is grounded in the paradigm of Ambient Intelligence (AmI), where the physical and digital worlds converge to create intelligent environments that are sensitive, adaptive, and responsive to human presence. In such ecosystems, processing power is distributed across numerous embedded devices, enabling what is often referred to pervasive computing.
Wearable devices play a central role in this evolution, with applications expanding across numerous domains: personal well-being and health monitoring, mobility and smart transportation, workplace productivity, sports and fitness, fashion technology, interactive entertainment, tourism, and even agriculture and environmental sustainability. These devices not only enhance individual user experiences but also contribute to broader socio-technical systems through real-time data analytics and decision support.

Dublin Descriptors
Intended Learning Outcomes (ILOs)
1 Knowledge and understanding
Students will learn to address the design process of smart systems, with a particular focus on wearable devices. This includes the collection, definition, and refinement of requirements; analysis of existing solutions; system specification; and the identification of suitable sensors, actuators, and connectivity technologies based on project goals. Students will acquire knowledge of how to design and program embedded systems that interact with the physical environment, focusing on the constraints and opportunities specific to wearable technologies (e.g., power efficiency, form factor, real-time sensing).
2 Applying knowledge and understanding
Students will be able to independently study, design and prototype a wearable or embedded digital system. They will be able to critically evaluate and select hardware components (e.g., microcontrollers, wearable sensors) and software tools appropriate to the context. They will be able to write and test embedded programs capable of acquiring, processing, and reacting to data from the surrounding environment, possibly in real time.
3 Making judgments
Given a real-world problem and design alternatives, students will develop the ability to analyse both functional and non-functional requirements (e.g., latency, battery life, usability) and to compare hardware/software trade-offs, especially in the context of wearable and mobile applications. Students will be prepared to keep up with evolving trends in microcontroller architectures, low-power design techniques, and human-centred computing.
4 Communication skills
Students will be able to clearly communicate the conceptual model, design decisions, and implementation strategies of their wearable system to both technical and non-technical audiences, using visual aids, prototypes, and technical documentation.
5 Lifelong learning skills
Students will develop the ability to plan and manage the end-to-end lifecycle of an embedded or wearable device project, from idea to functional prototype. Students will cultivate the capacity to autonomously learn how to use emerging platforms, SDKs, and development tools used in wearable and ubiquitous computing. Students will be prepared to keep up with evolving trends in microcontroller architectures, low-power design techniques, and human-centred computing.

Introduction
The course explores the principles of Ambient Intelligence (AmI), focusing on system classification, design lifecycle, and context-aware technologies. Through real-world examples—particularly involving wearable devices—students will investigate how digital systems can sense, interpret, and respond to human presence and environmental changes.
Smart System Design
Students will engage in the end-to-end development of an embedded or wearable system, from concept to functional prototype. Emphasis is placed on:
Critical analysis of existing solutions;
Identification of user needs and technical requirements;
System architecture definition and component selection;
Integration, testing, and evaluation of smart systems in real-world scenarios.
Rather than focusing on programming basics, the course assumes a solid technical foundation and challenges students to apply their knowledge autonomously in the context of open-ended design problems.
Project-Centered Learning
The course is structured around a project-based methodology. Each student team will define and develop an original idea, building a Minimum Viable Prototype (MVP) that demonstrates the core functionality of the proposed system. The goal is to quickly validate the concept, collect feedback, and iterate.
Projects are expected to reflect innovation, practical relevance, and user-centred design, especially in domains such as wearable computing, health and well-being, personal mobility, sustainability, and smart interaction.
Learning Experience
Activities are carried out in person and combine:
Thematic lectures on AmI and smart system design;
Individual and group design sessions with continuous feedback;
Peer critique and review of intermediate stages;
Final demo of the working prototype and presentation of design decisions.
Students are encouraged to take full responsibility for their project—from ideation to prototype demonstration—developing both technical autonomy and design maturity.
Note: Projects are carried out in teams of two. Groups of three or more are strongly discouraged.

Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
Students are required to deliver the following outputs. Note: The percentages shown are indicative.
Final Grade (Maximum: 33 points)
Final Paper: 40%
Pitch Presentation: 10%
Prototype and experimental results: 50%
Assessment Component
Weight (%)
Description
Dublin Descriptors Assessed
Final Paper
40%
Technical report describing the design process, rationale, and evaluation of the proposed smart or wearable system. It assesses students’ ability to understand, analyse, and document design choices, demonstrating knowledge of relevant methodologies and technologies.
1, 2, 3
Pitch Presentation
10%
Oral presentation of the project’s concept, design, and results to both technical and non-technical audiences. Evaluates clarity, synthesis, and effectiveness of communication.
4
Prototype and Experimental Results
50%
Practical realization of a working prototype and its empirical evaluation. Demonstrates the ability to apply theoretical knowledge to real-world contexts, analyse performance, and justify design decisions.
2, 3, 5
Final Paper
Format: LaTeX using the IEEE Template
Length: Minimum 8 pages
Submission:.pdf
Weight: 40% of the final grade
Structure (All sections are mandatory):
Title (~100 characters, including spaces) & Authors
Abstract (~1000 characters, including spaces)
Introduction (~4500 characters, including spaces)
Related Works (~6000 equivalent characters, including text, tables, and figures)
Methodology (~7000 equivalent characters, including text, tables, and figures)
Experimental Results (~7500 equivalent characters, including text, tables, and figures)
Conclusions (~2000 characters, including spaces)
References (~15:20 references)
Important: All sections listed above are mandatory. Missing any section will make the paper invalid.
Important: The writing process begins after defining the main content areas, with each section remaining open to revision based on the evolving outcomes of the research.
Pitch Presentation
Format: PowerPoint slides
Length: ~12 slides;
Submission:.ppt
Weight: 10% of the final grade
Duration: 10 minutes
Evaluation Criteria:
Slide quality: 70%
Presentation delivery: 30%
Prototype
Format: A working physical or digital prototype demonstrating the core functionality of the proposed system (MVP – minimal viable prototype)
Experimental Results: experimental results, obtained through the prototype (hw-sw) are essential to validate both the idea and the prototype.
Weight: 50% of the final grade
NOTE: All sections in the document are “mandatory”— any missing section will invalidate the entire document.
NOTE: 30cum Laude will be assigned when the total score is higher or equal to 31.

Gaetano D. Gargiulo, Ganesh R. Naik, Wearable/Personal Monitoring Devices Present to Future, Editore: Springer Singapore, Anno edizione: 2021, ISBN: 978-981-16-5323-0
Note: This book discusses recent advances in wearable technologies and personal monitoring devices, covering topics such as skin contact-based wearables (electrodes), non-contact wearables, the Internet of things (IoT), and signal processing for wearable devices.
Hemachandran K., Manjeet Rege, Zita Zoltay Paprika, K. V. Rajesh Kumar, Shahid Mohammad Ganie, Handbook of Artificial Intelligence and Wearables, Editore: CRC Press, Anno edizione: 2024, ISBN: 1032684933
Note: Handbook of ArThe book explores AI's role in wearable tech, addressing challenges, solutions, and recent advances. It covers design and development of devices like fitness trackers, smart glasses, sports wearables, and diagnostic tools, with real-world case studies.
Cameron, N., Electronics Projects With the ESP8266 and ESP32: Building Web Pages, Applications, and Wifi Enabled Devices
Gulliksson, H., Pervasive Design, fourth edition (English Edition)
Osterwalder A., Value Proposition Design: How to Create Products and Services Customers Want

The course provides a technical overview of Wireless Body Area Networks (WBANs), which consist of wearable devices used to collect, process, and transmit multiple types of data from a living being. It begins by highlighting the most relevant application scenarios for WBANs and the key technical components required for their implementation, further discussing their specific characteristics with respect to conventional Internet-of-Things (IoT) systems. Students learn how to design the acquisition layer of WBANs to transform analog signals from the monitored subject into digital data. The course then moves on to the study of the so-called edge layer, comprising the wireless communication technologies used to exchange data among wearable devices. After a brief refresher on Bluetooth Low Energy (BLE) and Wi-Fi, the focus shifts to WBAN-specific wireless technologies, including IEEE 802.15.6 and ETSI SmartBAN. The application layer is then introduced through the study of protocols for representing and exposing the collected data to end users. WBAN-specific messaging protocols are discussed and compared with legacy protocols (e.g., HTTP, MQTT, and CoAP). The last part of the course is dedicated to intra-body communications and the related emerging research topics, including molecular communications and the Internet of Bio-Nano-Things (IoBNT). Security and privacy issues in WBANs are also discussed.

1 - Knowledge and understanding
At the end of the course, students should be able to:

• understand the architecture and components of WBANs, including acquisition, edge, and application layers
• know the main wearable sensors and devices used to monitor physiological parameters, their characteristics, and limitations
• understand WBAN-specific wireless communication standards (IEEE 802.15.6, ETSI SmartBAN) and their differences with classical IoT protocols (WiFi, BLE, HTTP/MQTT/COAP)
• understand intra-body communication principles and the IoBNT
• recognize the key security and privacy challenges in WBANs and the techniques to address them
  2 - Applying knowledge and understanding
  At the end of the course, students should be able to:
• design the acquisition layer of a WBAN, selecting appropriate sensors and processing strategies to convert physiological signals into digital data
• implement and evaluate communication strategies at the edge layer, including the use of WBAN-specific wireless standards
• select and apply suitable application layer protocols for healthcare data collection and sharing
• analyze and propose solutions for intra-body communication scenarios, considering emerging research topics
• apply security and privacy measures in the design of WBAN systems
  3 - Making judgements
  At the end of the course, students should be able to:
• critically evaluate different WBAN architectures and communication protocols in terms of energy efficiency, reliability, and suitability for specific healthcare applications
• assess the trade-offs between performance, cost, and privacy in the design of WBANs
• propose informed solutions for novel WBAN and IoBNT applications by integrating interdisciplinary knowledge from electronics, communication, and biomedical domains

1. Introduction to Wireless Body Area Networks (WBANs)
a. Motivation and overview
b. Application scenarios
2. Architectures and Components of WBANs
a. Acquisition, edge, and application layers
b. Network topologies
c. Design challenges and trade-offs
3. Acquisition Layer
a. Sensor architectures and types
b. Data acquisition and processing
c. Energy management in WBANs
4. Edge Layer
a. Wireless communication standards for WBANs
i. Refresher on Wi-Fi for WBANs
ii. Refresher on Bluetooth Low Energy (BLE) for WBANs
iii. IEEE 802.15.6
iv. ETSI SmartBAN
b. Power efficiency and security at the edge
5. Application Layer
a. Legacy messaging protocols (HTTP, MQTT, CoAP)
b. Emerging protocols for Healthcare IoT
6. Intra-body Communications (IBC)
a. Molecular communications
b. Internet of Bio-Nano-Things (IoBNT)
c. Emerging research trends
7. Security and Privacy in WBANs
a. Threat landscape
b. Techniques for ensuring data privacy and integrity

Prova scritta facoltativa (a scelta dello studente), senza prove in itinere
Prova orale condizionata (a scelta del docente)
Valutazione continua facoltativa (a scelta dello studente)
The students will be evaluated through two mutually exclusive options:
(i) In-course challenges
They consist of one written homework with quantitative and qualitative exercises, similar to those discussed during the exercise sessions, and two practical mini-projects on the design and implementation of WBAN solutions. The mini-projects include the design, simulation, and performance evaluation of WBAN systems using the concepts introduced during the lectures and laboratory sessions, as well as specialized software tools such as COMSOL Multiphysics®, Python, and MATLAB. Mini-projects may be developed in groups of up to two students, while the written homework must be completed individually. Each of the three in-course challenges is graded on a scale of up to 11 points.
or
(ii) Final written examination
It consists of two exercises and two questions covering the course topics. It will be aligned with the examples and exercises presented during the lectures, as well as with the written examinations from previous academic years. The maximum achievable score in the final written examination is 33 points.
Students may choose their preferred option. The final grade is:
the score obtained in the final written examination, for students choosing the examination option;
the sum of the points obtained in the three in-course challenges, for students choosing the challenge option.
For both evaluation options, the maximum achievable score is 33 points. A final score of 33/33 is awarded the grade of 30 cum laude, while scores from 0 to 32 correspond directly to the final grade on a 30-point scale (e.g., 30/33 corresponds to a final grade of 30/30).
At the discretion of the instructor, an oral examination may be required to verify the student's individual preparation and the achievement of the intended learning outcomes, regardless of the selected evaluation option.
The assessment evaluates the achievement of the intended learning outcomes according to the Dublin Descriptors. Knowledge and understanding are assessed through the written examination or the written homework by evaluating the student's understanding of WBAN, sensor architectures, wireless communication standards, application-layer protocols, intra-body communications, and emerging research trends. Applying knowledge and understanding are assessed through the quantitative exercises and the practical mini-projects, which require the analysis, design, simulation, implementation, and performance evaluation of WBAN-based systems using appropriate software tools. Making judgements is evaluated through the student's ability to analyze design trade-offs, assess energy efficiency, security, privacy, and networking solutions, interpret simulation and experimental results, and justify the adopted methodological choices. Communication skills are assessed through the clarity, completeness, and technical quality of the written homework, the project documentation, and, when required, the oral examination. Learning skills are evaluated through the student's ability to autonomously integrate the concepts introduced during the course and apply them to the design and evaluation of innovative Body Area Network solutions

Course slides and handouts https://webeep.polimi.it