electronics engineering - ingegneria elettronica

Aim of the course is to make students mastering the analysis and the design of amplifiers (OP-AMPs and OTAs) covering all the steps from topology choice, device sizing, compensation techniques, optimization of noise, power dissipation, bandwidth, large signal performance.
Lectures are intended to deliver core concepts and methods, supported by notes and references to on-line material. Blended class sessions will present sample problems, pushing students to develop problem-solving strategies. Exercise classes will show students how to solve numerical problems thus strengthening their comprehension of the subjects.

Expected learning outcomes
Dublin descriptor
On completion of this course, students will have a sound knowledge and understanding of design appraoches to OTAs and OP-AMPs both CMOS and bipolar/BiCMOS.
1) knowledge and understanding
Specific examples and case studies will be provided to make students able to:
· analyse the requirements of amplifying stages
· define the circuit architectures suited to match the design targets
· size the components, accounting for noise and device variability
· implement frequency compensation techniques.
2) applying knowledge and understanding

Background on transistors’ operation: Review of basic MOSFET operation: the charge sheet model, ohmic and saturation regime. Channel modulation and finite output resistance. Modulation voltage and dependence on channel length. Resistive coupling between source and drain terminals. Subthreshold operation. Diffusion regime. Limit to the maximum voltage gain. Moderate inversion. Inversion coefficient. EKV model. Figures of merit: maximum voltage gain and cut-off frequency. A review of BJT operation: The planar npn structure. Current gain and transconductance. Early effect, maximum current gain. Gummel plot and beta dependence on current. High injection-low injection effects. Optimal bias. Options for pnp devices: lateral and substrate devices. BJT equivalent circuit. Resistance values at the BJT terminals accounting for the resistive coupling between emitter and collector. Cut-off frequency in BJTs: diffusion capacitance and dependence on current density.
Complements on network theory and feedback: Network theory: independent/interacting capacitors and poles. Zeros of the transfer function. The time constant method and its extensions to derive the circuit transfer function. A review of feedback theory: positive vs. negative. Closed-loop systems, open loop gain, loop gain, ideal and real gain. Signal sampling: voltage or current. Feedback topology: series or parallel. Effects of negative feedback: gain stability, bandwidth extension, improved input and output impedances, improved linearity. Bode plots. Bode stability criterion. Loop gain and phase margin. Root locus.
Complements on noise: Quantitative description of noise: noise variance and noise power spectral density. Noise transfer in circuits. Input referred noise sources of a two-port network. Definitions and derivation. Noise models: Thermal noise of resistors. The Nyquist argument for the thermal noise power spectral density. Shot noise model. Application to p-n junctions, BJTs and MOSFETs in weak inversion. Trapping noise: trapping noise in a resistor. McWorther’s model of the 1/f noise in MOSFETs. Tvidis’s formula.
OTA's and OP-AMP's: The prototypical differential stage: from resistive to active loads. Common mode feedback and single ended option. Single ended differential stage with mirror: Bias, input and output voltage swings, differential gain, Common mode gain. Input referred noise sources of a differential stage with MOSFETs and BJTs. Power-noise trade-off.
Two-stage CMOS OTA: topology, frequency response using the time constant method, Miller compensation. Pole splitting vs. compensation capacitance value. The RHP zero and the high frequency pole. OTA compensation and FoM. Two-stage CMOS OTA: frequency compensation with the nulling resistor. Implementing the nulling resistor. Frequency compensation with ideal voltage and current buffers. Impact of the buffer finite resistance. Two stage bipolar amplifier: input resistance, input referred voltage and current noise, sizing example and compensation. uA741 - first stage, bias and common mode feedback, output resistance, differential gain. Mirror with emitter follower and bleeding resistor. Wilson's mirror. uA741 second stage. Setting the bias: trade-off between gain and input impedance. Frequency response.
Single-stage CMOS OTAs: telescopic cascode topology, differential gain, input and output voltage swing, power dissipation, frequency response. Folded cascode topology, enhanced mirrors, voltage dynamics, power dissipation. Folded cascode with bipolar transistors. Feed-forward compensation. Three-stage CMOS OTA: Nested Miller Compensation
OTA Linear response. In-band zero-pole doublets and features of the settling response. The slew rate limit. Impact on settling time. CMOS-OTA: Internal and external slew rate limits. Improving SR with class AB output stages. Output stages: Emitter follower as output stage. Emitter follower efficiency. Push-pull. Efficiency. Cross-over distortion. Class A-B stage. Total harmonic distortion. Distortion reduction by feedback. Output stages in bipolar technology (uA741). Short-circuit protections.
Variability and matching: Relative matching of threshold voltage values. Relative matching of resistors. Common centroid techniques. Pelgrom’s formula. Offset: deterministic and statistical contributions to input referred offset. Input referred offset in bipolar differential stages. Temperature effects. OTA: Common-mode rejection ratio. Deterministic and statistical limits to CMRR.

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
Assessment is based on a final exam consisting of a written test followed by an oral. The written test is a 3-hours closed-book test devoted to the quantitative solution of two problems. Students who pass the written test go through the oral exam consisting of a discussion of topics randomly picked from a list made available on WeBeeP. At the oral, the student is asked to discuss the selected topics, clearly stating the framework, deriving the quantitative results and, then, summarizing their main implications. The oral exam usually results in an adjustment (-2/30 to +2/30) of the written test result, thus leading to the final grade.
Type of assessment
Description
Dublin descriptor
Written exam
· Solution of quantitative problems (e.g. analysis of OTA/OP-AMP scheme, deriving input referred-offset, noise, frequency response, large signal parameters etc.).
· Exercises focusing on design aspects (e.g. selection/design of OTA/OP-AMPs to match specs such as input/output voltage range, input-referred offset, noise, GBWP, slew rate, etc..).
1,2
1, 2
Oral exam
· Theoretical questions on topics covered by lectures.
· Discussion of design examples to verify the understanding.
1, 2

A. L. Lacaita, Lecture Notes made available on BeeP
F. Maloberti, Analog Design for CMOS VLSI Systems, Editore: Springer Science and Business Media, Anno edizione: 2001, ISBN: 978-0-7923-7550-0

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

The primary objective of the course is to prepare students to address the challenges associated with the development of state-of-the-art digital electronic systems.
To this end, the course provides not only theoretical and methodological knowledge, but also advanced practical skills in the implementation of digital processing architectures, with particular emphasis on programmable hardware devices such as Field-Programmable Gate Arrays (FPGAs).
Building upon the knowledge of digital electronics acquired in introductory courses, students will develop advanced competencies and acquire the methodologies and techniques required for the design of high-speed, high-performance digital systems, as well as architectures dedicated to digital signal processing.

Dublin Descriptor 1: Knowledge and understanding
Expected learning outcomes:
· Understand the principles of configurable computing.
· Understand the architecture and implementation issues of FPGAs.
· Explain the complete implementation flow of a digital design on an FPGA.
· Demonstrate knowledge of the fundamental Digital Signal Processing (DSP) algorithms suitable for FPGA implementation.
· Understand the principles of pipelined and parallel processing.
· Explain pipelined and parallel implementation techniques for fundamental DSP structures (e.g., FIR and IIR filters).
· Understand timing analysis and timing constraints in FPGA-based designs.
Dublin Descriptor 2: Applying knowledge and understanding
Expected learning outcomes:
· Analyze system requirements and design a corresponding digital architecture.
· Evaluate alternative architectural solutions according to design specifications.
· Apply digital design methodologies to assess the feasibility of a processing architecture.
· Design, implement, and validate digital architectures on FPGA devices that satisfy a given set of specifications.
Dublin Descriptor 3: Making judgements
Expected learning outcomes:
· Select the processing techniques and architectural solutions best suited to specific application requirements.
· Evaluate the correctness of a digital design by defining appropriate verification and validation strategies.
· Compare alternative implementations in terms of performance, resource utilization, and implementation cost, identifying potential risks and appropriate mitigation strategies.
Dublin Descriptor 4: Communication
Expected learning outcomes:
· Present and effectively communicate the design process, implementation results, and technical outcomes.
· Justify and discuss design choices, architectural decisions, and implementation trade-offs with peers and instructors using appropriate technical terminology.
Dublin Descriptor 5: Lifelong learning skills
Expected learning outcomes:
· Develop and manage a realistic FPGA-based design project.
· Independently acquire and apply new digital signal processing techniques and FPGA design methodologies.
· Transfer the acquired knowledge and skills to the solution of real-world digital signal processing problems.

The course is based on the use of a personal laptop computer and of an Evaluation Board ("Basys 3 Artix-7 FPGA Trainer Board") whose supplying by the student is mandatory. Students enrolled in the course will be given precise indications for supplying the Evaluation Board.
Contents
Review of Digital Electronics Fundamentals
· Logic levels and noise margins.
· Contamination and propagation delays.
· Power dissipation.
· Area utilization.
Programmable Logic Devices
· Principles of operation.
· Spatial vs. Temporal Computing.
· Overview of major programmable architectures: CPLDs, FPGAs, SoCs, and ACAPs.
FPGA Architectures
· Logic blocks.
· I/O blocks.
· Clock distribution networks.
· Power distribution and management.
· FPGA configuration technologies.
Pipeline and Parallel Processing
· Principles of pipelined architectures.
· Parallel processing techniques.
· Design trade-offs for high-performance digital systems.
· Optimization for area, timing, and power.
Timing Issues in Digital Design
· Metastability and timing hazards.
· Setup and hold requirements.
· Static timing analysis for single-clock designs.
· Clock Domain Crossing (CDC).
FPGA Development Using the Xilinx Vivado Design Suite
· Hardware description using VHDL.
· FPGA design flow: simulation, synthesis, implementation, and debugging.
· Design constraints and timing closure.
· Memory resources (RAMs and FIFOs).
· Communication protocols (AXI4-Stream).
Design and Implementation Activities
· Laboratory exercises, in-class design assignments, and individual homework assignments focused on the design, implementation, and optimization of FPGA-based digital systems, including DSP applications and hardware accelerators.

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 is graded on a 31-point scale and consists of a written examination divided into two parts: a theoretical part (maximum 16 points) and a practical part (maximum 15 points).
Both parts of the examination must be passed in order to obtain the final grade. The final mark is calculated as the sum of the scores achieved in the theoretical and practical parts. Honors ( lode ) are awarded to students achieving a final score of at least 30.5/31.
· The theoretical part consists of a traditional written examination completed on paper (or equivalent methods). It includes exercises, theoretical questions, waveform analysis, and VHDL code analysis. A minimum score of 9 out of 16 is required to pass this part.
· The practical part consists of the design and implementation of a VHDL project and is intended to assess the practical and design skills acquired during the course. A minimum score of 9 out of 15 is required to pass this part.
The practical assessment may be completed in one of two ways: i) during the laboratory activities scheduled within the course or, ii) during one of the official examination sessions held after the end of the lectures.
i) Laboratory option: during the semester, laboratory sessions are organized in which VHDL projects are assigned for development and implementation on the evaluation board. Students may participate by forming their own working groups. Each group is assigned a project to be completed by a specified deadline and assessed before the first official examination session. All members of the same group receive the same score. Students are required to use their own laptop computer and evaluation board. Any additional hardware required (e.g., joysticks or audio modules) will be provided during the laboratory sessions.
ii) Examination-session option: during the official examination sessions, the practical assessment consists of developing and implementing a VHDL project on the evaluation board. Students must use only their own laptop computer, with no Internet access, and their own evaluation board. If required, additional hardware (e.g., joysticks or audio modules) will be provided during the examination. No groups are allowed. Each student works independently and receives an individual evaluation.
Only the practical assessment score may be retained ( "frozen" ), subject to the following conditions:
· Students who obtain a laboratory score (laboratory option) of at least 9/15 and subsequently choose to retake the practical assessment during an official examination session permanently forfeit their laboratory score.
· The practical assessment score, whether obtained through the laboratory activities or during an official examination session, remains valid untill the fifth exam section.
· If the student retakes the practical assessment, the previously retained score is automatically forfeited.
· Neither the theoretical assessment score nor the overall final examination grade may be retained.
The instructor reserves the right to require an additional oral examination in the following cases:
· students who completed the practical assessment as laboratory option, when clarification is needed following the evaluation of the laboratory assignment
· students whose performance shows a significant discrepancy between the theoretical and practical parts of the examination.

Volnei A. Pedroni, Circuit Design and Simulation With VHDL, Editore: The MIT Press, Anno edizione: 2010, ISBN: 0262014335
Note: Second Edition
Peter J. Ashenden, The Designer's Guide to VHDL, Editore: Morgan Kaufmann, Anno edizione: 2006, ISBN: 0120887851
Note: Third Edition
David Harris, Sarah Harris, Digital Design and Computer Architecture, Editore: Morgan Kaufmann, Anno edizione: 2012, ISBN: 9789382291527
Note: Second Edition

This course focuses on the design methodolgies and architectures underlying modern digital integrated circuits. The objective is to understand how to use individual devices to build combinational logic gates, sequential logic circuits, and complex architectures under constraints of reliability, dynamic performance, area, and power consumption and which implementation of a digital circuit is the most suited for a given constraint, i.e., area, power consumption, speed, and reliability.

Knowledge and understanding (Dublin Descriptor 1)
At the end of the course and after succesfully passing the exam, the sudent:
knows how to analyze combinational and sequential digital circuits implemented in different logic families.
knows how to analyse the effect of interconnect parasitics on the performances of a digital circuit.
understand which implementation of a digital circuit is the most suited for a given constraint, i.e., area, power consumption, speed, and reliability.
knows how to analyze simple digital modules, such as finite state-machines, counters (asynchronous and synchronous), memories (ROM, SRAM, DRAM) and arithmetic circuits (adders, multipliers, digital comparators, equality circuits).
Applying knowledge and understanding (Dublin Descriptor 2)
At the end of the course and after successfully passing the exam, the student:
is able to apply the acquired knowledge to assess the operation and performance of a digital integrated circuit.
is able to apply the acquired knowledge to design a combinational gate or a sequential circuit with given specs in terms of area, power consumption, reliablilty and dynamic performance.
is able to discuss pros and cons and performance trade-offs between different implementations of the same circuit.

Introductory concepts and background
A historical perspective: from vacuum tube to transistor and IC era
Issues in digital integrated circuit design
Implementation strategies for digital ICs: Custom design and standard-cell based design
Quality metrics of a digital circuit (cost of an integrated circuit, functionality and reliability, dynamic performance, power and energy consumption).
The devices:
MOSFET transistor as switch
Static and dynamic behavior
Velocity saturation and unified model
Mismatch and process variations
Technology scaling
The wire:
Interconnect parameters (capacitance, resistance and inductance)
Electrical wire model (ideal wire, lumped model, lumped RC model, distributed rc line, P and T model)
Elmore theorem
Effects of scaling of wires and impact on the overall performances
The CMOS inverter:
The static CMOS inverter (switching threshold, noise margins, robustness revisited);
Dynamic behavior (computing the capacitances, propagation delay)
Power consumption (dynamic, cross-conduction, leakage)
Power- and energy-delay product
Inverter chain and optimum number of inverters in a chain with a capacitive load
Technology scaling and its impact on the digital circuit metrics
Combinational logic gates:
Fully-complementary CMOS logic
Sutherland theory on the dimension of FC-CMOS gate chain
Ratioed logic, i.e., pseudo-NMOS logic and differential cascode voltage switch logic (DCVSL)
Pass-transistor logics, i.e., LEAP (LEAn Integration with pass-transistors), Swing Restored Pass Transistor Logic (SRL), Complementary Pass-transistor Logic (CPL)
Transmission gate logic
Sequential logic circuits:
Latches and flip-flops as basic sequential element
Timing metrics for sequential circuits
Static latches and flip-flops (the bistability principle, multiplexer-based latches, master-slave edge-triggered flip-flops, Low-Voltage Static Latch, Static SR flip-flop with pure force writing)
Dynamic latches and flip-flops, i.e., basic topology, C 2 MOS latch, True Single-Phase Clocked (TCSP) flip-flops
Pulsed flip-flops
Pipelining as approach to optimizing sequential circuits
Finite state-machines (FSMs), counters asynchronous and synchronous
Arithmetic circuits:
Datapath in digital signal processor
The adder (half-adder and full-adder)
Circuit implementation of single-bit adder (static implementation, mirror adder, CPL,
Multi-bit adder (ripple carry, carry-skip, linear carry-select, root-square carry-select)
Multipliers (array and carry-save multiplier)
2’complement form of signed numbers
Digital comparators, subtractors, equality circuits.
Semiconductor memories:
Memory classification
Memory architectures and building blocks
Read-only memories (ROM), NOR and OR ROM architectures
Nonvolatile read-write memories (EPROM, EEPROM, flash)
Static RAM with resistive load, 6T static RAM
Dynamic RAM (1T, 3T), open and folded bit line architecture
Memory peripheral circuitry (address decoders, sense amplifiers)

Prova scritta obbligatoria, senza prove in itinere
The final examination consists of a written test made of a numerical exercise and two open questions concerning all the topics faced during the course.

Jan M. Rabaey, Anantha Chandrakasan, and Borivoje Nikolic, Digital Integrated. Circuits: A Design Perspective (2nd Edition), Editore: Prentice-Hall, Anno edizione: 2002, ISBN: 978-0130909961
Neil Weste, David Harris, CMOS VLSI Design: A Circuits and Systems Perspective, Editore: Pearson, Anno edizione: 2011, ISBN: 9780136076919

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.

Main goal of the course is to enable students to analyse and to design electronic circuits and systems.
The course applies the concepts of frequency-domain and time-domain analysis, noise investigation, and performance assessment to electronic components and circuits, for designing and developing electronic boards and products. The course presents and exploits both analog (transistors and operational amplifiers) and mixed-signal (samplers and ADC/DAC converters) electronic components, in either general-purpose configurations (e.g., voltage-mode operational amplifiers and basic ADCs) or advanced implementations (e.g. current-feedback amplifiers, multi-step ADCs, sigma-delta modulators, etc.).
The course provides the theoretical methodology and the practical skills to understand how a circuit works (moving from the electronic schematics to hand computations of frequency-domain and time-domain performance) and vice versa from high-levels specifications and requirements, to component selection, schematics design, and component sizing. Each step is explained both theoretically (“lesson” classes) and practically (“exercise” classes), through the study of real circuits and case-studies.

Knowledge and understanding (Dublin Descriptor 1)
At the end of the course and after successfully passing the exam, the student:
knows how to analyse analog and mixed-circuit electronic circuits, and to understand the impact of component sizing on performance;
knows how to design feedback circuits and to compute frequency response, impedances, stability, and noise performance;
understands how to sample, hold, and convert analog signals to digital samples, for proper static and dynamic performance, and how to select the suitable ADC and DAC components.
Applying knowledge and understanding (Dublin Descriptor 2)
At the end of the course and after successfully passing the exam, the student:
is able to apply the acquired understanding to assess operation and performance of given electronic circuit schematics;
is able to apply the acquired knowledge and skills to design electronic circuits from specifications and block diagrams;
is able to discuss pros and cons and performance trade-offs among different circuital implementations.

Noise
Basics of noise: distribution, spectrum, power, and effective value.
Electronic noise: shot, thermal, flicker, and burst noise.
Noise Equivalent Generators, equivalent Bandwidth, Noise Figure and Temperature.
Case-studies of noise analysis in electronic circuits.
Amplification
Operational Amplifiers: parameters, feedback and virtual ground, main configurations.
Frequency-domain response and stability of circuits: analysis, diagrams, compensation methods.
Advanced components: instrumentation (INA), isolation (ISO), current-feedback (CFA), current-mode (CMA, Norton), transconductance (OTA), and Fully-Differential (FDA) amplifiers.
Component data-sheets and case-studies of circuits employing amplifiers.
Sampling
Basics of sampling: time-domain and frequency-domain analysis, aliasing, Shannon theorem.
Sample-and-Hold circuits: static and dynamic errors, compensation techniques, performance.
Advanced components: role of feedback, stability and precision issues, speed-accuracy trade-offs.
Analog multiplexers and digital potentiometers: parameters, timing, acquisition techniques.
Conversion
Digital-to-Analog converters (DAC) and Analog-to-Digital converters (ADC): basic architectures, conversion time, speed/accuracy trade-off, timings.
Dynamic performance: FFT spectra, SNR, SiNAD, ENOB, THD and IMD distortions.
Advanced ADCs: interpolation, folding, half-flash, multistep, time-interleaving architectures.
Over-sampling Sigma-Delta modulators: noise-shaping, SNR and bit improvements, components.
Components data-sheets and case-studies of circuits employing S&Hs, muxes, ADCs, and DACs.

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
Students are required to pass a WRITTEN test, followed by a final VIVA (oral) test. The written test aims at assessing student’s understanding of circuit analysis methodologies and design criteria provided through the course (Dublin Descriptors 1 and 2). The viva test aims at evaluating the student’s knowledge and understanding about the overall course topics (Dublin Descriptors 1 and 2). The date of the written test will be published on-line on the POLIMI webpage. The viva test will be held usually within 1-2 weeks after the written test, once the teacher will have evaluated the written tests and informed the students.
The WRITTEN test lasts 3 hours, during which students work autonomously (no chat, no smart-phones, no text books, no lesson slides) on 5 different circuits, to be either studied, sized, or designed; each circuit has two questions to answers with computations, graphs and text. Soon after the written test, the teacher will send the detailed solutions to students by email, for self-assessment. Those students wishing to withdraw their test may send an email to the teacher, asking not to evaluate it (e.g., if she/he realises that the test does not reach her/his target quality). Then, the teacher evaluates all written tests, fills in an excel file with a grade (from 0.0 to 1.0) per each question, and the final mark (from 0 to 30 out of 30) for the written test, and emails it to all students. Only students with a ranking higher than 17/30 can attend the viva test. The email also provides dates and locations among which the student can choose from to give the final viva test.
At the VIVA test, each student will be asked with questions covering all course topics, mainly focused on assessing the theoretical know-how acquired (Dublin Descriptor 1) and how to apply it to actual electronic circuits (Dublin Descriptor 2) for both analysis and design. The viva usually lasts 20-30 minutes per student. The viva ends with a final mark (from 18 to 30 cum laude out of 30) if passed, or a fail (the student has to redo the complete exam). Usually, the viva test results in an increment or decrement of the written test’s mark up to +/- 4 (out of 30). Therefore, students may consider to withdraw after the evaluation of the written test or during the viva test, if the grade is far away from their expectations (e.g., with a written test grade of 22/30, when the desired mark is higher than 28/30).

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

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"

This course provides the fundamentals of radio-frequency (RF) and millimeter-wave (mmW) front-ends integrated in silicon processes (CMOS/BiCMOS) for wireless communication and sensing applications. Topics covered include: architectures of RF/mmW receivers/transmitters for single-chip integration, design of building blocks such as low-noise amplifiers, mixers, oscillators, frequency synthesisers, power amplifiers. The primary goal of the course is to provide students with the basic skills and knowledge required to analyse and design practical RF circuits in silicon technologies. Laboratory experiences will be carried out to show practical design flows at system level.

Dublin Descriptor 1 (DD1): Knowledge and understanding
On completion of this course, students will be able to:

• comprehensively understand the principles and operation of wireless communication systems and the main sources of performance degradation;
• understand the impact of non-linearity on RF receiver performance and have a knowledge of the methods to measure non-linearity;
• have a sound knowledge of techniques for frequency synthesis, and understanding of the operation of phase-locked loops;
• have a knowledge of impedance transformation networks and of practical realization of varactors, planar inductors and transformers;
• have a basic knowledge and understanding of the circuit techniques to design low-noise amplifiers, oscillators, mixer circuits and power amplfiers;
• have a knowledge and understanding of the operation of basic RF receiver/transmitter architectures and their impact on building block design at circuit level.
  Dublin Descriptor 2 (DD2): Applying knowledge and understanding
  Specific examples and case studies will be provided to make students able to:
• evaluate the impact of circuit impairments (e.g. noise, mismatch, nonlinearity) on the performance of receiver/transmitter for a specific wireless application;
• model and simulate an RF transmitter at behavioral level, and evaluate the performance;
• handle jitter/phase noise calculations and their impact on SNR performance;
• design a frequency synthesizer based on a phase-locked loop for a given wireless application;
• model and simulate a phase-locked loop at behavioral level;
• design a passive network for impedance transformation;
• design an RF oscillator circuit for a specific application;
• apply appropriate circuit techniques in the design of different types of low-noise amplifiers;
• choose among different types of mixer circuits and design;
• choose a specific class of RF power amplifier for a specific application.

Lecture topics (60 hours)
Basics of Communications Systems (11 hours): multiple access to channel and heterodyne receiver. Analog modulations. Random-walk phase noise of a carrier (SSCR and jitter). Digital communications (Nyquist signalling, constellation plane, modulator/demodulator). EVM and ACPR. LO reciprocal mixing.
Phase-Locked Loop (11 hours): nonlinear differential equation, perturbation analysis, lock range. PLL linear model. First-/second-order PLL. Static phase error. Phase noise. Integer-N PLLs (reference spur). Charge-pump PLLs (type-II, tri-state phase detector, reference spur).
Passive Networks (6 hours): Resonant circuits, voltage/current amplification, impedance transformation. L-match, Pi-match, Colpitts networks. Coupled inductors. Reactive components in silicon: spiral inductors, metal-metal capacitors, MOS and pn varactors.
Oscillator Circuits (6 hours): feedback-system model, negative resistance model. Startup and a amplitude stabilisation. Double-transconductor CMOS oscillator and Colpitts bipolar oscillator. Phase noise and noise-vs-power compromise.
Receiver Issues and Amplifier Circuits (9 hours): third-order, second-order intercept points, two-tone test, cascaded stages. Maximum power transfer and power gains. Scattering parameters. Noise figure, cascaded stages. Receiver sensitivity and dynamic range. Low-noise amplifier (LNA) circuits: noise, linearity, matching, stability, gain, reverse isolation. Inductive degeneration and noise cancelling.
Transmitter Issues and Power Amplifier (PA) Circuits (6 hours): impact of nonlinearity in transmitters, saturated power, efficiency and PAE, linearity. Linear classes of operation. Impedance transformation network and its impact on power efficiency.
Mixer Circuits (5 hours): conversion gain, port-to-port feedthroughs, noise, linearity. Single-/double-balanced mixers. Passive/active mixers.
RF Front-End Architectures (6 hours): heterodyne receivers (image problem, half-IF problem, dual-IF receiver), direct-conversion receivers (LO leakage, I/Q mismatch, DC offsets, flicker noise). Transmitter architectures.
Tutorial topics (34 hours)
Phase-Locked Loop (13 hours)
Passive Networks (4 hours)
Oscillator Circuits (6 hours)
LNA Circuits (4 hours)
PA Circuits (3 hours)
Mixer Circuits (4 hours)
Laboratory topics (6 hours)
QPSK Transmitter (3 hours): modelling in Matlab of a raised-cosine QPSK TX, demodulation/signal analysis in Keysight VSA, study of impact of LO phase noise and I/Q mismatches on EVM.
Phase-Locked Loop (3 hours): modelling and simulation in Matlab Simulink of type I, type II, charge-pump PLLs. Study of locking behavior.

Prova scritta obbligatoria, con prove in itinere
Prova orale facoltativa (a scelta dello studente)
Student Evaluation and Final Grades
The final grade will be determined by an examination, which can be taken on one of five available dates throughout the academic year. The final examination consists of a 150-minute closed-book written test and an oral examination. The written examination comprises two or three numerical problems with a maximum score of 28+/30, covering theoretical topics and tutorials. Alternatively, the written test can be split into two parts: a 150-minute mid-term written test (covering theoretical topics 1–3 and related tutorials and labs, consisting of two or three numerical problems) and a 150-minute final written test at the end of the term (covering theoretical topics 4–8 and related tutorials and labs, also consisting of two or three numerical problems).
Students who pass the written test may take an oral examination (max. score 2/30), which consists of a discussion on one topic selected at random from a provided list of 40 questions. The final grade is the sum of the written and oral exam scores. Cum laude (honors) is awarded when a student achieves a mark of 28+ on the written test and a score of 2 in the oral examination.
Assessment and Dublin Descriptor
Written examination:

• Solution of numerical problems (e.g. analysis of phase-locked loop systems, RF receivers, LNA, mixer, oscillator circuits, power amplifiers, etc.): DD1, DD2
• Exercises focusing on design aspects (e.g. design of circuit/system for a specific wireless application or to meet given specifactions): DD1, DD2
  Oral examination:
• Theoretical/practical questions drawn from any topic of the course: DD1, DD2

Behzad Razavi, RF Microelectronics - Second Edition, Editore: Pearson, Anno edizione: 2011, ISBN: 0132839415
Andrea Leonardo Lacaita, Salvatore Levantino, Carlo Samori, Integrated Frequency Synthesizers for Wireless Systems, Editore: Cambridge University Press, Anno edizione: 2007, ISBN: 0521863155

The course is intended not just to lead students to know and properly describe the electronic techniques and instrumentation developed for recovering sensor signals from noise, but rather to gain a good insight in the problems faced and the approaches developed to overcome them. This implies to critically evaluate the solutions, avoiding the attitude where sensors and electronics are designed and employed just according to the established rules and standards. It is instead necessary to clarify the reasons of the choices and decisions in the light of the physics of phenomena involved, of the basic principles of signal and noise processing and of the actual performance of the available devices. It is necessary to clearly distinguish the intrinsic limitations set by physical laws from the current limitations set by the state of the art, which can be overcome by the technological progress. In essence, to gain insight means to progress at the pace of the technology evolution and be able to contribute to it.

Knowledge and understanding (Dublin Descriptor 1)
At the end of the course and after successfully passing the exam, the student will learn how to:
Identify the theoretical best filters
Understand the working principle of the most common filters
Understand and manage the issues related to the 1/f noise
Have a basic knowledge of the physics of photodiodes, phototube, strain gauges and temperature sensors
Applying knowledge and understanding (Dublin Descriptor 2)
At the end of the course and after successfully passing the exam, the student will be able to:
Apply appropriate techniques in the design of different types of filter to meet given specification
Select sensors and front-end electronics suitable for the specific application
Perform the calculation of the obtainable signal to noise ratio
Analyze and comment on specific architectural choices
Apply the theory to assess the availability of a best filter

Signals and noise. Introduction to measurements, errors and statistical distributions. Mathematical treatment of signals and noise in the time and in the frequency domain. Signal-to-Noise ratio (S/N). Autocorrelation functions, energy and power spectra. Noise sources in electronic circuits and sensors. Main types of noise spectra.
Extracting signals from noise. Linear filters with constant parameters and with time-variant parameters, action on signals and noise and resultant S/N. Pulse-signals and constant-parameter low-pass filters; Gated Integrator (GI); Boxcar Integrator (BI); Sample-and-Hold (S&H) and fast samplers; discrete filtering by sampling and weighted average of samples. Optimum filtering for pulse-amplitude measurements, significance and practical usefulness. Noise with 1/f spectrum: characteristic features and ensuing problems, filtering approach. Constant-parameter high-pass filters; correlated double sampling (CDS) and further developments; Baseline Restorer (BLR). Periodic signals and constant-parameter resonant filters; modulation of signals and noise; Lock-in Amplifier (LIA), analog and digital implementations of LIAs.
Sensors are treated by discussing how the physical principles of their operation, the characteristic features and electrical parameters impact on the signal to noise ratio. Photodetectors: vacuum tube and semiconductor photodiodes; photoconductors; Photomultiplier tubes (PMT), avalanche photodiodes (APD) and single-photon avalanche diodes (SPAD); analog and digital detection, single-photon counting (SPC) Temperature Sensors: thermo resistances. Strain and Force Sensors: strain gauges and piezoelectric sensors.

Prova scritta obbligatoria, con prove in itinere
Prova orale condizionata (a scelta del docente)
Assessment will be based on a final exam consisting of a 2 hours and 45mins closed-book written exam, typically including two problems related to practical applications and theoretical questions.
Students who pass the written exam with a score above 26/30 may take an optional oral exam to adjust the final grade. The oral exam can raise the grade up to 30/30 "cum laude", but it could also decrease it, in case of glaring errors of understanding.
After the exam, the problem solution will be published and the student will have tipically one day to decide whether to request (by email) that his or her task is corrected.
Written exam (Dublin Descriptor 1-2) will be based on:
Solution of numerical problems
Computation of signal to noise ratio in the particular application
Minimum detectable signal estimation
Sensor performancescalculation
Exercises focusing on design aspects
Selection and design of filters for the specific applications
Definition of minimum required performance of specific kind of sensor
Open-answer theoretical/practical questions on any topic of the course.
Oral exam (Dublin Descriptor 1-2) will be based on:
Theoretical/practical questions drawn from any topic of the course.

Texts, figures and documentation that cover all the course program http://home.deib.polimi.it/rech/Index.html
Note: Complete set of slides employed in the lectures; Text and explanation of problems given in the written tests carried out in previous years; Papers, presentations, technical documentation, suggested references and websites dealing with signal recovery, sensors and measurement instrumentation
Ivan Rech, Giulia Acconcia, Signal Recovery http://home.deib.polimi.it/rech/Download/SignalRecoveryBook.pdf
Note: The text covers all the course topics and can be downloaded fo free.
Sergio Cova, Notes and Bibliography for the course "Signal recovery", Editore: Libreria Cortina, Anno edizione: 2014
T.H. Wilmshurst, Signal recovery from noise in electronic instrumentation, 2nd edition, Editore: A. Hilger - IOP Publishing Ltd, Anno edizione: 1990, ISBN: 0-7503-0058-2
Silvano Donati, Photodetectors: Devices, Circuits and Applications, Editore: Prentice Hall, Anno edizione: 2000, ISBN: 0130203378

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

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 covers circuit and system level design issues of both analog and mixed-signal integrated circuits (IC).
Focusing mainly on CMOS and Bipolar technologies, the key building blocks, such as differential amplifier, voltage and current references, comparators, and the most important issues for the design analog and mixed-signal ICs, such as noise, power and offset, will be discussed in depth. Some topologies of DAC and ADC that are representative of an entire family of converters will be then discussed: current steering DACs, are studied an example of Nyquist rate converters, pipeline ADC, are an example of low-rate high-accuracy converters, delta-sigma DAC, are an example of oversampled systems.
The main goal, more important than the mere knowledge of a number of circuit topologies, is to encourage the typical intuitive attitude required in the IC design (and in many areas of engineering) i.e. to focus the main design issues and constrains, to individuate the trade-offs and, finally, to provide the optimum solution. This process must always precede the mathematical modeling of the circuit.
Each part of the program is discussed in “lesson” classes, more focused on the theoretical aspects, while “exercise” classes will provide some examples, often taken from the past written test.
This course presumes a solid understanding of the basics of analog circuits.

Being the classes intended as the final course on analog and mixed-signal IC design, the most important learning outcome upon passing the exam is not the knowledge of a list of circuits topologies but:
Knowledge and Understanding (Dublin descriptor 1)
To understand what are the trade-offs in the analog and mixed signal design
To critically discuss what are the possible solutions for the specific problem discussed.
The student should be able to understand that it does not exist an optimum solution for all the problems but the best circuit to adopt depends on the specifications (for instance gain, or linearity, or noise) and constrains (voltage supply, power, silicon area).
Applying knowledge and understanding (Dublin descriptor 2)
Select from the body of knowledge the useful skills to solve a given design problem.
Analysis both at system level and at transistor level of mixed signal circuits
Capability to design all the critical building blocks of a mixed signal circuit, given the specifications and the design constrains.
Critically analyze other proposed solutions.
Making judgements (Dublin descriptor 3)
Be able to judge the industrial and scientific impact of a new design, plus the related social issue in particular power dissipation and material waste in pervasive applications.
Learning skills (Dublin descriptor 5)
Develop the learning skill to further study in autonomy
To be able to critically read the technical literature (journal paper and, of course, books), which is clearly a necessary skill for a designer given the huge amount of technical literature today available. To this purposes an example of design will be discussed with the help of a literature paper.

Mixed Signals Silicon IC design
Review of the main issues.
CMOS vs. BJT.
Components matching and offset
Deviation from the nominal value, and relative matching of integrated components.
Main sources of mismatch in CMOS and Bipolar technologies.
Matching in a differential stage.
Statistical offset.
Systematic offset.
Impact on speed and power dissipation.
Impact on DAC INL and DNL, example of design of a current-steering DAC.
Noise
Noise sources in CMOS and Bipolar.
Equivalent input noise generators.
Input noise for basic stages, and opamp.
Noise-power trade off.
Impact on design of sampled system and ADC.
CMOS and BiCMOS stages
A short review of the basics: Input stage. Level shift. Slew rate. Frequency compensation. CMRR and its link with offset. PSRR.
Non-standard architectures.
Fully differential stages. Common mode feedback network.
Output power stages, distortion.
Feedback and distortion.
Bipolar output stages. CMOS output stages.
Voltage and current references.
Voltage and current references derived by Vbe and Vt.
Bandgap reference: theory and circuits.
Linear regulators.
Comparators.
CMOS and BiCMOS comparators. Basic structure.
Offset sampling.
Data converters.
Nyquist rate current steering DAC: INL and DNL. Thermometric vs. binary architecture.
High resolution ADC: Comparison between pipeline and SAR.
Advanced techniques and figure-of-merit.

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
The course ends with a 2-hours written exam consisting in the analysis of a single circuit, in term of its main performance, followed by a mandatory oral (viva) test. The circuits in the written test are always the building blocks discussed in the first part of the course, typically amplifying stages. Examples of past written test will be discussed and solved during the exercise classes.
The written test will be followed, for students that passed the written test, by a mandatory oral (viva) test, consisting in a discussion about all the topics discussed in the classes. There is no pre-definite limit for the mark both in the written and in the oral test.
The description of each type of assessment is as follows:
Written test: Two hours. Evaluation of the main performance of a given circuits, usually an amplifying stag. Typical questions deal with: gain, noise, offset, slew rate and bandwidth). ( Dublin descriptor 1 and 2 ). A final question is added to stimulate the open discussion about different circuits' solutions ( Dublin descriptor 1,2, 3 ) during the following oral test.
Oral (viva) test: Usually 20-30 minutes. Theoretical and practical questions, focused on design choices, picked from all the topics in the program discussed during classes ( Dublin descriptor 1,2 3 and 5 ).
The final grade is given to the student after the oral (viva) test.
The date of the written test is published on-line on the POLIMI webpage. The oral (viva) test will be held usually within 1-2 days after the written test (this time can slightly change, depending on the number of people attending the written test), once the teacher will have evaluated the written tests and informed the students.
Any student can withdraw at any time during both the written and oral test and may also decide to repeat the entire exam if she/he wants to improve its mark. It is not allowed to repeat only one part of the exam (that is either only the written test or only the oral test).

P. Gray, P. Hurst, S. Lewis, R. Meyer, Analysis and design of analog integrated circuits, Editore: Wiley an& sons, Anno edizione: 2009
Note: The book contains much more material that the one covered by the course, however it is the classical book and the reference for analog designer, and it will be useful for everyone the will work in this field.
Franco Maloberti, Data converter, Editore: Springer
M. Pelgrom, Analog-to-Digital Conversion 4th Edition, Editore: Springer, ISBN: 978-3-030-90807-2
Note: Despite the title this book is not only limited to ADC, including a lot of review material

This course provides the fundamentals of power electronics. Topics covered include: power semiconductor switches, drive and protection circuits; switching power converters; thermal design. The primary goal of the course is to provide students with the basic skills and knowledge required to analyze and design practical power electronic systems in the low-power range.

Knowledge and understanding (Dublin Descriptor 1)
On completion of this course, students will be able to:
comprehensively understand the principles and operation of modern power semiconductor devices, their limitations, and their driving and protection techniques;
have a sound knowledge and understanding of the operation of switch-mode DC-DC converters used in low power applications;
understand the dynamic performance of dc-dc converters and develop small-signal converter models;
understand and manage the thermal stress of active components in power electronic systems.
Applying knowledge and understanding (Dublin Descriptor 2)
Specific examples and case studies will be provided to make students able to:
select the active/passive components of a power electronic circuit on the basis of specifications;
apply appropriate techniques in the design of low-side and high-side MOSFET drivers;
analyze and design basic protection circuits for power semiconductor devices;
apply appropriate techniques in the analysis of different types of DC-DC converters;
develop and exploit the small-signal model of a DC-DC converter;
design different types of power converters to meet given specifications;
design simple closed loop regulators for DC-DC converters to meet functional objectives;
tackle with steady-state and dynamic thermal performance of power semiconductor devices and ICs.

Power semiconductor switches, drive and protection circuits
Introduction to power electronics. Energy efficiency: the role of power electronics. Basic switching circuits. Overview of power semiconductor switches. Conduction and switching losses.
Schottky and p-i-n power diodes. Reverse blocking. Forward conduction characteristics. Switching characteristics.
Power MOSFETs. Lateral and vertical DMOS structures. ON resistance. Absolute maximum ratings. Dynamic performance. Parasitic capacitances. Gate-charge curve. Switching waveforms. Switching time estimation.
Gate drive circuits for low-side and high-side switches. Bootstrap techniques. Isolated gate drivers. Overvoltage, overcurrent and overtemperature protection circuits.
Switch-mode DC-DC converters
Overview of Distributed Power Architectures (DPA).
DC-DC converters: basic non-isolated topologies (Buck, Boost, Buck-Boost). Pulse Width Modulation (PWM). Steady state converter analysis. Continuous and discontinuous conduction modes (CCM and DCM).
Converter dynamics and control. Circuit averaging and averaged switch modeling. Small-signal modeling of switched-mode power converters. Voltage-mode control of switching power converters: compensator design. Introduction to current-mode control.
Step-down switching regulator design example.
Isolated DC-DC converters. Flyback converter: CCM and DCM operation. Steady state analysis.
Flyback regulator design example.
Thermal management
Thermal considerations for power semiconductors. Heat transfer by conduction, convection and radiation. Packaging of power devices. Heat sinks. Steady state thermal calculations. Transient operation: thermal impedance. Transient thermal calculations.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
Assessment is based on a final exam consisting of a 2-hour, closed-book written test including:

• two numerical problems involving analysis and design (e.g. analysis of switch-mode power converters, power device drive&protection circuits, etc. Selection of active and passive devices for specific power applications) – DD 1,2;
• an open-answer theoretical/practical question on topics taught in the course – DD 1,2.
  The grade of the written exam (highest mark: 30/30) is based on the percentage weightings listed below:
• 45% for each of the two numerical problems;
• 10% for the theoretical question.
  Students who pass the written exam (pass mark: 18/30) may take an optional oral exam, consisting of a discussion on a topic picked at random from this list. The oral exam results in the adjustment (-2/30 to +2/30) of the written exam score, yielding the final grade. “Cum laude” is awarded if the final grade is strictly higher than 31/30.

R.W. Erickson, D. Maksimovic, Fundamentals of Power Electronics, 3rd Edition, Editore: Springer International Publishing, Anno edizione: 2020, ISBN: 978-3-030-43879-1
WeBeep https://webeep.polimi.it/
Note: lecture slides, suggested readings, supplementary course materials and more info available on WeBeep.

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

Goal of this course
The Advanced Circuit Theory course is designed to complement the basic students' knowledge on electrical circuits obtained in the course of the Junior Electrical Engineering degree, with more "in depth" or complementary topics that are typical of a rigourous and scientifical approach to the study of circuits. In particular, the analysis of linear and non - linear circuits, purely resistive or dynamic is formalised using general methods such as modified nodal analysis and state equations. The basic framework of circuit simulators is presented, along with explanations related to possible simulation failures. Finally, network functions of linear dynamic circuits are introduced in order to present some simple examples of one-port synthesis and filter design. In all the topics covered, specific attention is given to the application of general methods.

Knowledge and understanding (Dublin Descriptor 1)
At the end of the course and after successfully passing the exam, the student:

• knows the fundamental principles of graph theory applied to electrical circuits
• has knowledge of the state space representation of nonlinear dynamic circuits, of the linearisation methods and of energy based stability properties of circuits
• knows how to apply the standard modified nodal analysis method for the solution of general circuits (nonlinear, dynamic etc.) including integration methods and nonlinear solvers.
• Understands the basic properties of linear passive one-ports and knows the standard methods for their realization
• Moreover, as a more general and methodological goal, a rigorous, math based approach to circuits.
  Applying knowledge and understanding (Dublin Descriptor 2)
  At the end of the course and after successfully passing the exam, the student:
• is able to write solving equations for general circuits and analyzing their solutions both using numerical methods and using closed form solutions
• is able to write, using standard programming languages, the basic structure of a circuit simulator
• knows how to synthesize a passive linear one port using Cauer or Foster methods

1 Fundamental laws of electrical networks
Topological properties. I mplicit and explicit Kirchhoff's laws. Graph matrices and their relations. Power and energy. I mplicit and explicit Constitutive formulations of one-port and two-port components. Unidirectional ity, ' reciprocity', symmetry and other properties of linear two-ports. Special two-ports: transformer, gyrator and operational amplifiers. Connection of two-ports.
2 Analysis of linear and nonlinear resistive networks
General methods of analysis. Nodal analysis (NA ) and Modified Nodal Analysis (MNA ). Numerical methods for solving linear algebraic systems. Sparsity. Characterization of nonlinear elements and their linearization. Examples: diode, BJT and MOS transistors, OP-AMP. Small-signal analysis. Graphical and numerical methods for solving systems of nonlinear algebraic circuit and their interpretation. Properties of circuits composed of strictly passive non-linear and strictly monotonous non-linear bipoles.
3 D ynamic circuit a nalysis
Formulation of state equations in the time domain. Solution of the state equation of linear dynamic circuits in the time domain: free and forced components of the solution. Natural frequencies and stability. Second-order linear circuits. Qualitative study of simple dynamic nonlinear circuits: oscillators and bistable. MNA formulation for general circuits. Simple numerical methods for the solution of the MNA problem.
4 Network Functions
Analysis of linear dynamic circuit s in sinusoidal steady state. Network functions in the phasor domain. Frequency response. Analysis of linear dynamic circuits using the Laplace transform. State equations and their solution in the Laplace domain. Immittances and general network functions H(s), poles and zeros.
5 Synthesis of one- ports and passive filter design
Properties of immittance functions Z(s ) and Y( s) of RLC passive one-ports. Properties and synthesis of one-ports with only two elements (LC, RC, RL ) according to the canonical forms of Foster and Cauer. Design of a low pass filter. Sensitivity analysis with respect to a parameter of the circuit and method for that network.

Prova scritta condizionata (a scelta del docente), con prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), individuale, senza revisioni periodiche, senza presentazione/discussione finale
Valutazione continua facoltativa (a scelta dello studente)
The exam is composed of two parts, a first "practical" written test, followed by a second written test related to the theory. The goal of the practical part is assessing the student's capability of application of the main topics of the course with a direct approach to simple problems (Dublin descriptors 1 and 2), the theory part to test the knowledge and understanding of the main methods used in the course aiming at the self-sufficiency in the solution of problems not considered in this course (Dublin descriptor 1).
The date of the practical part is published online on the POLIMI webpage, students can access the theory part either immediately after the practical part (and will be corrected only if the practical part is sufficient), or (in this case only if the practical part is sufficient) in any other following exam session (in this case the student will be temporarily marked as non-passed and will have to enroll to the chosen session).
The practical part lasts 2h15' and consists in the solution of three problems covering the main areas of the course, the students must work autonomously but can have access to a single self produced A4 sheet containing formulas or anything else the student believes can be useful to pass this part of the exam. Clarity in the description of the methods used and in the results obtained and terseness of language will be evaluated positively. The practical part is marked from 0/30 to 30/30; access to the theory part is reserved to students with a mark greater or equal to 16/30. The theory part lasts 30' and consists in two questions that must be answered in written form each question taking no more than one A4 page and where terseness of language and a quantitative approach where possible are encouraged. The theory part, if not sufficient, invalidates the whole exam, if sufficient it can increment or decrement the practical part mark usually in the range -4/+3.
The practical part can be substituted by a project whose contents are discussed and agreed by both parts and must be submitted at most 7 days before the date for the theory part.
When possible a non mandatory midterm, possibly online, will be issued and will give the students a chance to increment the final mark by at most 2 points.
Readability and compliance to the instructions, including requests to fill online forms, is part of the evaluation of all parts.
Communication of results will be via mail using the official POLIMI student mail address.

Leon O. Chua, Charles A. Desoer, Ernest S. Kuh, Linear and Nonlinear Circuits - International Edition, Editore: McGraw (but now in non defined international reprint), Anno edizione: 1987, ISBN: 9780070108981
Advanced Circuit Theory - Lecture Notes https://webeep.polimi.it
Note: Lecture Notes available on the POLIMI webeep platform.

Main goal of the course is that the student understands all the major concepts used in modern microprocessors by the end of the semester.
The course will cover the different forms of parallelism found in applications (instruction-level, data-level, thread-level, gate-level) and how these can be exploited with various architectural features. It will cover pipelining, superscalar, speculative and out-of-order execution, vector machines, VLIW machines, multithreading, graphics processing units, multicore and parallel homogeneous and heterogeneous architectures. Final goal is to show how the software interacts with the hardware to provide performance and how trends in technology, application and economics have driven and drive continuing changes in the field.

Expected learning outcomes
DdD 1 (Knowledge and understanding): Students will learn how to:
Understand and analyze the most advanced microprocessors and computer architectures
Evaluate and compare the performance of computer architectures
Understand the most advanced techniques to optimize the memory hierarchy
Manage the parallelism at instruction-level, thread-level, multi-processor level
Understand the mechanisms of streaming and vector processing widely used in modern GP-GPUs.
DdD 2 (Applying knowledge and understanding): Given specific problems and project cases, students will be capable to:
Analyze and compare specific architectural choices of a computer system
Evaluate the performance of computer architectures by applying appropriate metrics
Exploit the parallelism at several levels from instructions to threads to cores to vectors
DdD 3 (Making judgments): Given specific problems and project cases, students will be capable to analyze the performance goals and compare autonomously different computer architecture choices in terms of performance metrics
DdD4 (Communication skills): Students will learn how to present in a technical text and how to discuss in front of their colleagues the benefits and drawbacks of advanced computer architecture techniques.
DdD 5 (Learning skills): Students will be capable to learn autonomously the most advanced research techniques related to state-of-the-art microprocessor and parallel computer architectures

Main lectures topics:
Review of basic computer architecture: the RISC approach and pipelining, the memory hierarchy
Basic performance evaluation metrics of computer architectures
Techniques for performance optimization: processor and memory
Instruction level parallelism: static and dynamic scheduling; superscalar architectures: principles and problems; VLIW (Very Long Instruction Word) architectures, examples of architecture families
Thread-level parallelism
Multiprocessors and multicore systems: taxonomy, topologies, communication management, memory management, cache coherency protocols, example of architectures
Stream processors and vector processors; Graphic Processors, GP-GPUs, heterogeneous architectures

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, con presentazione/discussione finale
NOTE: The course IS in ENGLISH, which means that the exam HAS TO BE PASSED in English, e.g. you HAVE to WRITE IN ENGLISH during the written test.
The final examination may be taken in one of the following ways:
Way 1. WRITTEN EXAM (closed book, no notes or usage of laptop/mobile, etc. allowed) consisting of:
1.1 The solution of some design problems based on the practical application of the course concepts and techniques -- Expected learning outcomes 1, 2, 3, 4, 5, 6 and 7.
1.2 Questions on the course concepts and techniques - Expected learning outcomes 1, 2, 3, 4, 5, 6 and 7.
WAY 2. PROJECT-BASED EVALUATION (valid only for the 1st session after the course):
2.1 (1/3 of the final grade) Evaluated through the course via short written tests - Expected learning outcomes 1, 2, 3, 4
2.2 (2/3 of the final grade) Evaluated, via a project assignment, done in team up to 2 members, demonstrating the mastering of the course concepts and techniques. During the final project presentation, each team will be asked to answer some questions on ACA topics, not only related to the project. - Expected learning outcomes 1, 2, 3, 4, 5, 6 and 7.

John Hennessy, David Patterson, Computer Architecture, A Quantitative Approach, Editore: Morgan Kaufmann, Fifth or Sixth Edition
Note: Additional information in slides and papers available through the course website.

The course has two goals; the first is to foster a deeper understanding of fundamental operating systems topics by exposing students to modern operating systems’ internals, both general purpose and embedded*. The second but equally important goal is to provide practical experience with the design and development of OS components and/or system-level programs by means of a practical project proposed by the instructors. Depending on availability, some of the advanced course topics will be covered in seminars from industry experts.
[Note*: To build a broader knowledge and understanding of embedded systems it is suggested to follow also the Embedded Systems course]

Expected Learning Outcomes (ELO)
Upon successful course completion students will:
1. Knowledge and understanding
Know the mechanisms, policies and interfaces used by modern OSes to control and share, among running programs, resources such as processor, memory and other IO devices (through the device drivers).
Be aware of the main issues associated with concurrency that might arise in multi-process and multi-threaded system-level programs.
Know mechanisms, policies and interfaces adopted by modern OSes to manage modern storage peripherals ensuring proper access control to the data.
Know mechanisms and policies adopted by modern OSes to boot up, manage power consumption, virtualize resources, ensure security.
2. Application of knowledge and understanding
Be able to write multi-process, multi-threaded concurrent software for both embedded and general purpose multi-core systems.
Be able to write device driver software to interface with hardware peripherals.
Be able, depending on the choice of the project work, to design software using market-available technologies, possibly constrained by energy and performance requirements.
3. Making judgements
Be able to determine optimal policies for resource management given the requirements of real-world applications.
Be able to select the most suitable platform, operating system (or bare-metal programming) and development tools based on the application requirements and by adopting best industrial practices.
4. Lifelong learning skills
Be able to be follow and adopt future OSes technologies by adapting his/her knowledge with minimal effort in an ever-changing scenario.
Be able to undertake career paths in OS/embedded systems development.

The topics covered by the course will be addressed both theoretically and practically. For some of the course topics (related to the implementation of OS internals) we will mainly make references to the latest versions of the Linux source code although we will touch upon other Unix systems as well (e.g., MacOS’s XNU) and embedded OSes (FreeRTOS, Miosix). Part of the course will show practical examples on industrial boards (e.g., STM32/ARM) and, depending on availability, these boards can be borrowed by the students to finalise the course development project (which is optional). Besides, the course will cover practical use cases, tutorials and short introductions to the Linux Kernel Module APIs, POSIX C API, modern C++ threading API, GIT, GCC/CLANG, GNU Make etc. Concerning processor ISAs, the course will touch mainly Intel x86, ARM and RISCV. Depending on the availability of time, other modern system level languages (e.g., Rust) will also be touched upon.
The topics covered are the following:
Introduction and background
Main goals of an Operating System and its design patterns. Mechanisms and policies for mediating and regulating the access to system resources. Taxonomies of operating systems (monolithic, microkernel, hybrid, uni-kernels) with examples.
Resource Management and concurrency
Support for multi-process and multi-threaded execution. CPU scheduling for general purpose single and multiprocessors. Modern techniques for load-balancing processes on NUMA multiprocessors. Memory consistency basics and multi-threaded synchronization. Advanced locking techniques and lockless programming. Primitives for inter-process communication (IPC). Asynchronous programming. Deadlock and starvation. Virtual memory management. Basics of software and hardware virtualization, containers.
Peripheral and persistence management
Low-level access to peripherals (memory mapped vs port-based), communication buses (e.g. PCI), modern programmable interrupt controllers and interrupt management. Deferring interrupt work. Character based and block based IO. Development of modern device drivers. Overview of modern storage devices (HD, SSD) and file systems. The filesystem-centric view of peripherals.
Run-time support
Boot loaders and kernel initialization. Kernel device discovery (ACPI and device trees). The C runtime support, structure of dynamic libraries and linking. Overview of tools for development, analysis and profiling of embedded code.

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
The evaluation of the expected learning outcomes will be carried out through a written exam during the official "appello," which will include open-ended and closed questions, as well as exercises (ELOs 1, 2, 3, 4).
Students will have the option, should they so choose, to supplement the exam evaluation with a development project on topics proposed by the instructors.

Slides, notes and other material available on the course's site https://www.vittoriozaccaria.net/#/r/aos
Remzi H Arpaci-Dusseau, Andrea C Arpaci-Dusseau, Operating Systems: Three Easy Pieces, Editore: CreateSpace Independent Publishing Platform; 1.00 edition, Anno edizione: 2018, ISBN: 198508659X https://pages.cs.wisc.edu/~remzi/OSTEP/
Note: Freely available on the web
Robert Love, Linux Kernel Development, Editore: Addison-Wesley Professional; 3rd edition, Anno edizione: 2010, ISBN: 0672329468
Daniel P. Bovet and Marco Cesati, Understanding the Linux kernel, Editore: O'Reilly, Anno edizione: 2006

Course Objectives
Light and laser technologies play a central role in modern science and engineering, enabling applications ranging from telecommunications and precision manufacturing to biomedical diagnostics, spectroscopy, remote sensing, quantum technologies, and autonomous systems.
This course provides students with a comprehensive understanding of both the physical principles and engineering aspects of laser-based optical systems. Starting from the fundamentals of wave optics, students will explore how interference, diffraction, coherence, pulse propagation, and dispersion can be exploited to develop advanced optical devices and measurement techniques.
Particular attention is devoted to modern photonic technologies such as ultrafast femtosecond lasers and optical frequency combs, which have revolutionized precision spectroscopy, optical metrology, telecommunications, and frequency measurement by enabling unprecedented resolution in both the time and frequency domains.
The course examines the operating principles, technological implementation, performance, and applications of a broad range of laser sources. Continuous-wave and pulsed laser operation, optical resonators, frequency stabilization techniques, nonlinear optical phenomena, and ultrafast laser systems are discussed together with the underlying physical and mathematical models.
A strong emphasis is placed on engineering practice. Students will learn how to analyze, model, and design optical and laser systems for real-world applications, selecting appropriate optical, electronic, and laser components and evaluating overall system performance.
To enhance the learning experience, the course integrates innovative teaching activities, including:
Hands-on experiments using a real optical interferometric setup.
Numerical simulations of optical and laser systems.
Multimedia and interactive learning materials.
A mini-project in which students investigate and present a topic related to modern photonic technologies and laser applications.
By the end of the course, students will have acquired both the theoretical foundations and practical design skills required to understand, model, and engineer advanced photonic systems, including ultrafast femtosecond lasers, optical frequency combs, and high-resolution spectroscopic instrumentation for applications requiring extreme temporal and frequency precision.

Expected Learning Outcomes
Knowledge and Understanding
Upon successful completion of the course, students will be able to:
Explain the physical and mathematical principles governing wave propagation of light, including interference, diffraction, coherence, optical pulse generation, and dispersion.
Describe the operating principles and performance of optical devices exploiting interference and diffraction phenomena.
Understand and explain the physical mechanisms underlying laser operation and the fundamental properties of laser radiation.
Describe the behaviour and characteristics of lasers operating in continuous-wave, Q-switched, and mode-locked regimes.
Understand the origin of laser intensity and frequency noise and the techniques employed for noise reduction and frequency stabilization.
Compare the technological implementations, specifications, and performance of major laser platforms, including solid-state, fiber, and semiconductor lasers.
Understand the principles of ultrafast lasers, optical frequency combs, and modern laser spectroscopy techniques.
Applying Knowledge and Understanding
Students will be able to:
Analyze and design interferometric devices and interferometry-based measurement systems.
Model laser operation using appropriate mathematical tools and dimension the main components of elementary laser systems.
Simulate laser beam propagation and optical pulse evolution through complex linear optical systems.
Evaluate the impact of dispersion, diffraction, and nonlinear effects on optical system performance.
Select and dimension optical, electronic, and laser components for a specific scientific, industrial, or technological application.
Design complete laser-based systems for advanced applications in spectroscopy, sensing, communications, and precision measurements.
Communication Skills
Students will develop the ability to communicate complex physical and engineering concepts effectively through oral presentations, technical reports, numerical simulations, mathematical models, diagrams, and graphical representations. Also the oral examination, project activities, and written reports will strengthen students’ capability to present technical results clearly and concisely, selecting the most relevant information and employing appropriate scientific terminology.

Principles of Laser Operation, Technologies and Applications
(65 hours of lectures, exercises and multimedia sessions)
1. Fundamentals of laser operation
Radiation-matter interaction and optical gain.
Optical resonators and laser dynamics.
Continuous-wave laser operation.
2. Laser physics
Absorption, spontaneous and stimulated emission.
Spectral broadening mechanisms.
Gain media and laser materials.
3. Pulsed and ultrafast lasers
Q-switching and mode-locking.
Femtosecond lasers and optical frequency combs.
Frequency stabilization and precision metrology.
4. Laser technologies
Solid-state, fiber and semiconductor lasers.
Performance characteristics and technological implementations.
5. Laser applications
Industrial, biomedical and communication systems.
Sensing and spectroscopy.
Precision measurements and quantum technologies.
Wave Optics, Interferometry and Nonlinear Optics
(35 hours of lectures, exercises and multimedia sessions)
1. Wave optics and interferometry
Coherence, optical pulses and dispersion.
Optical interferometers and Fourier-transform spectroscopy.
Thin-film interference and optical coatings.
Fabry-Perot interferometers and optical resonators.
2. Diffraction and Fourier optics
Fresnel and Fraunhofer diffraction.
Fourier optics and spatial filtering.
Fundamentals of Gaussian beam propagation.
3. Nonlinear optics
Nonlinear interaction between light and matter.
Harmonic generation and frequency conversion.
Applications to ultrafast lasers and photonic systems.

Prova scritta facoltativa (a scelta dello studente), con prove in itinere
Prova orale obbligatoria
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), individuale, senza revisioni periodiche, senza presentazione/discussione finale
Assessment will be done through an oral exam which includes questions on the topics covered in the course as well as a discussion about the delivered project reports. The questions will always cover both macro arguments of the teaching (interference and diffraction + lasers principles). The student has six exam sessions available during the academic year in the periods set by the School. Some flexibility with respect to the official day and time of the exam will be accorded to students with specific needs and justifications.

Pedrotti, F.L.; Pedrotti L.M.; Pedrotti L.S., Introduction to Optics, Editore: Addison-Wensley, Anno edizione: 2007, ISBN: 0131499335
Svelto Orazio, Principles of Lasers - V Ed., Editore: Springer, Anno edizione: 2010, ISBN: 0306457482
Paolo Laporta, Interference and interferometers, Anno edizione: 2015
Note: Lecture notes
Paolo Laporta, Elements of diffraction theory, Anno edizione: 2015
Note: Lecture notes
Goodman, J.W., Introduction to Fourier Optics, Editore: Roberts and Company Publishers, Anno edizione: 2005

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

The course aims to provide the student with the basis of radiation theory and of the main concepts of antenna engineering for the design of complex telecommunication systems.
The course provides the students with instruments to understand and apply modern design technologies and methodologies. Some design examples are provided in order to teach the students to make autonomous judgements in the antenna design process.

Knowledge and understanding (Dublin descriptor DD1)
Students will learn how to make use of mathematical models for the understanding of the physics of electromagnetic radiation; define and use mathematical modesl for the analysis of the behavior of antennas at system level and at circuit level.
Applying knowledge and understanding (Dublin descriptor DD2)
Students will be able to compute the size of an antenna or array based on specific requirements; evaluate the performance of a designed antenna at system level; apply the mathematicl models using electromagnetic simulators.
Making judgements (Dublin descriptor DD3)

• Understanding the funtamental tradeoffs that govern the design of an antenna or array, identify the complexity of different solutions from a circuit point of view; choose a specific antenna type based on the desired radiation behavior.

Antennas
The course aims to provide the student with the basis of radiation theory and of the main concepts of antenna engineering. After introducing radiation theory, the main antennas topologies are introduced with a view to their application field (telecommunications, remote sensing, telemetry). We then discuss the effect of noise and finally show some measurement technique to characterize the main parameters of antennas.
Radiation theory. Maxwell equations in differential form, Poynting vector.
Radiation from electric hertzian dipole; computation of the near field, the induction field and the radiation field. Magnetic hertzian dipole: duality. The Huygens source.
The antenna as a transducer: main circuit parameters and radiation parameters (directivity, directivity function, gain, effective area, characteristic electric and magnetic length, efficiency, equivalent circuit, polarization).
Wire antennas (short dipole, half wavelength dipole and others).
Aperture antennas (horns, parabolic antennas)
Antenna arrays introduction (array factor, coupling)
Linear arrays (broadside, endfire, collinear, log-periodic)
Leaky wave antennas
Antennas measurements.

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, con presentazione/discussione finale
The examination will consist of a written test (2 hours) with 3 numerical exercises (DD1, DD2, DD3). No mobile phone are allowed in the test, but the student is allowed to bring his/her notes or books to consult. The student will be asked to:

• solve numerically some problems related to the topics of the course (knowledge and understanding with application, DD1, DD2, DD3)
• to design (numerically) an antenna or an array for a specific goal (making judgement, DD3).
  The student will be often asked to make some reasonable approximation when the solution appears too complex to solve exactly (making judgements, DD3).
  A supplementary and optional work is the study of some wire antennas by the use of electromagnetic simulators (DD1, DD2). The final result is then presented to the rest of the classroom (DD4). This optional work will grant up to three extra points in the final evaluation.
  30 cum laude will be assigned if the total score is greater than 31. The test will be evaluated in 1-2 weeks. Student can withdraw the final mark with no limitations on the number of tests they apply for.

S. J. Orfanidis, Electromagnetic Waves and Antennas
David M. Pozar, Microwave Engineering, Editore: Addison-Wesley
Robert E. Collin, Foundations for Microwave Engineering, Editore: McGraw-Hill
Notes for the course Webeep

Il corso descrive le applicazioni dei tool di intelligenza artificiale (AI) all'analisi di segnali, immagini e dati biomedici, con particolare enfasi sulle esigenze peculiari di queste applicazioni. Verrà descritta l'attuale pratica sanitaria che utilizza AI, in particolare questioni relative alla sicurezza, agli aspetti etici e legali. Saranno trattati gli attuali sviluppi dell'AI in diverse aree: medicina di precisione, elaborazione diagnostica delle immagini e radiomica, classificazioni dei biosegnali, stadiazione e stratificazione del rischio del paziente, pianificazione chirurgica, selezione della strategia di trattamento per i pazienti oncologici.

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 network 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

The objective of the course is the development of a general and specific know-how on the estimation and control methods for the autonomous navigation of road vehicles. The course focuses on ground vehicles and, more specifically, on passenger cars and alike. The course deals with the fundamental problems of trajectory and speed-profile generation, trajectory-tracking, positioning and obstacle-detection, and provides an overview of the main sensing and SW-sensing technologies in autonomous cars.
The course aims to illustrate the basic sub-problems and technologies needed to develop a completely-autonomous road vehicle, with special attention on the estimation issues, and on the control strategies and algorithms.
The main focus will be on fully-autonomous vehicles (L4 and L5), but also conditional autonomy (L3) will be considered, with attention on human-factors (disengagement and re-engagement of human driver).
The course will present the classical sub-topics of autonomous vehicle design:

• Sensing and perception, estimation, localization
• Global planning
• Local planning
• Trajectory tracking
  These topics will be developed at different levels of detail, where the emphasis will be on estimation and control algorithms specific for road vehicles (passenger cars or alike). The case of extreme performance (driving at the very limit of the car), in a in-circuit environment will be also considered.

Dublin Descriptor
Expected Learning Result (Specific to the Course)
Knowledge and understanding
Understand the fundamental problems, methods, and technologies for autonomous vehicle navigation (L3-L5), with a specific focus on estimation issues and control strategies (e.g., trajectory generation, localization, obstacle detection).
Applying knowledge and understanding
Design and apply control and estimation algorithms to solve specific sub-problems in autonomous navigation, such as trajectory-tracking, local planning, and vehicle control at the limits of handling.
Making judgements
Critically analyze and evaluate trade-offs between different design solutions, control algorithms, and sensing technologies for autonomous passenger cars, including considerations for human-factor issues in L3 systems.
Communication skills
Communicate a complex control-design solution for a specific autonomous vehicle application (e.g., path following, speed profiling) in a synthetic and technically precise way.
Learning skills
Autonomously read, interpret, and understand specialized industrial technical documentation and research papers in the field of autonomous vehicle control and estimation.

The course will cover the main areas of estimation and control technologies for autonomous cars (sensing and localization, global and local planning, trajectory-tracking and HMI). More specifically, the course sub-topics are the following:
Overview of “ego” sensing technologies; basics of their signal-processing
Road-sensing and vehicle-states sensing for control design: estimation of road-tire available friction, lateral and longitudinal slips (used for local planning and trajectory tracking)
Map-based positioning using GNSS, IMU and road-markers
Trajectory tracking (lateral, longitudinal, and joint lateral-longitudinal).
Local planning: control-oriented generation of trajectories (obstacle-avoidance trajectories)
Global planning: control-oriented generation of trajectories (in-circuit optimal trajectories) and optimal speed-profile (for energy, comfort, speed optimization)
Partially/conditioned autonomy and human factors

Prova scritta facoltativa (a scelta dello studente), 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, con presentazione/discussione finale
The exam will be a written test in one of the official available dates or a project (group or single). The exam consists of questions on the topics presented during the course. The project requires the development of techniques starting from the one seen in class.
Grading will be on the following basis: either 100% final project (group presentations with discussion with instructor) or 100% written test on the course topics.
The instructor reserves the right to hold an oral examination in cases where the written test does is not definitive in assessing the student's knowledge.
Type of assessment
Description
Dublin descriptor
Final project
Students are required to:
show their understanding of the main problems (and the corresponding solutions) in the field of control-systems design in vehicles;
use the knowledge acquired in the course to solve problems that they have not be exposed to in class.
select and describe the most appropriate tools for a given application;
be able to communicate in a oral presentation the solution to a specific application problem (modelling or analysis or control problem).
implement the solution they propose.
1,2, 3, 4, 5
Written test
show their understanding of the main problems (and the corresponding solutions) in the field of control-systems design in vehicles;
use the knowledge acquired in the course to solve problems that they have not be exposed to in class.
select and describe the most appropriate tools for a given application;
be able to communicate in a mixed text, graphical and mathematical way the solution to a specific application problem (modelling or analysis or control problem).
1, 2, 3, 4, 5

Matteo Corno, Lecture notes

L'obiettivo di questo corso è quello di sviluppare dei casi di studio ("end-to-end") di progettazione di sistemi di controllo, con riferimento ai principali problemi di controllo della dinamica del veicolo. I casi di studio si concentrano su veicoli terrestri, con particolare riferimento alle automobili o veicoli simili. Viene data enfasi al caso in cui il veicolo è dotato di trazione elettrica o ibrida (ovvero nel quale uno o più motori elettrici possono essere utilizzati come attuatore per il controllo della dinamica del veicolo, ed i problemi di dinamica del veicolo si accoppiano a problemi di gestione e ottimizzazione dell'energia)

Fornire gli elementi conoscitivi e metodologici per affrontare in modo propositivo e progettuale le vaste problematiche del sistema motorio, sia per quanto riguarda le basi fisiologiche del movimento umano, sia per quanto riguarda le tecniche di analisi quantitativa del movimento, la modellistica, la simulazione e la stima delle grandezza biomeccaniche di interesse, e le tecniche di recupero della funzione motoria.

Ci si aspetta che lo studente, al termine del corso, abbia acquisito competenze adeguate relative alla biomeccanica del movimento umano, sappia descrivere il sistema muscolo scheletrico e conosca le sue rappresentazioni mediante modelli, conosca le basi di cinematica, statica e dinamica per la rappresentazione del movimento umano, sia competente nella misura delle variabili biomeccaniche mediante strumenti avanzati.
In particolare ci si aspetta che lo studente al termine del corso abbia:
a) DdD 1 Conoscenza dei principi fondamentali del sistema motorio e comprensione della biomeccanica del movimento umano.
b) DdD 2 Capacità di applicare e realizzare modelli biomeccanici per la rappresentazione delle strategie relative al controllo posturale e alla locomozione
c) DdD 4 Abilità comunicative e proprietà di linguaggio nella trattazione di tematiche inerenti la biomeccanica del movimento.

Programma:
1) L’uomo come macchina automotrice: organizzazione delle varie componenti funzionali; postura e movimento, sistemi di controllo, concetti di sinergia e di programma motorio, considerazioni energetiche;
2 )La struttura di sostegno: architettura dello scheletro; descrizione anatomo-funzionale dei principali complessi articolari;
3) Il sistema motore: il muscolo come attuatore; architettura del sistema muscolare e del sistema di trasmissione meccanica; modalità di contrazione e di controllo motorio; schemi dei principali circuiti riflessi;
4) Conversione di energia metabolica in meccanica; il lavoro muscolare; rendimento meccanico dell'esercizio fisico;
5) Metodi per l’analisi biomeccanica del sistema motorio: le rilevazioni e le stime antropometriche; la misura cinematica (tecniche e strumentazioni); la misura dinamometrica; misurazioni sperimentali invasive (forze intra-articolari, forze tendinee, pressioni intradiscali, pressione intra-addominale, cinematica di segmenti ossei); la registrazione elettromiografica; l’approccio modellistico; l’analisi della funzionalità locomotoria;
6) Modelli biomeccanici per lo studio del sistema motorio: modelli delle articolazioni (sistemi di riferimento, gradi di libertà, assi funzionali); modelli del muscolo (modelli per lo studio della contrazione, modelli della struttura, modelli per lo studio della cinematica muscolare); modelli del sistema di controllo motorio (modelli ‘alfa’, ‘gamma’, ‘lambda’); modelli segmentari (arto superiore, spalla, arto inferiore, il modello a pendolo inverso, il modello ‘total body’);
7) Il problema dinamico inverso: descrizione cinematica di sistemi articolati; sistemi di riferimento e matrici di rototraslazione; ricerca di assi funzionali; equazioni di equilibrio dinamico. Stima di forze muscolari e intra-articolari e approcci al superamento dell’indeterminatezza: metodi e criteri di ottimizzazione; ipotesi sulle strategie di coordinamento motorio; uso di informazioni elettromiografiche (modelli ‘EMG driven’); definizione delle situazioni di carico; modellizzazione delle strutture intra-articolari (vincoli, superfici coniugate);
8) Cenni alla soluzione del problema dinamico diretto: metodi approssimati di soluzione; simulazione del movimento umano; esempi di applicazione (con riferimento anche a software di simulazione, tecniche di ‘rendering’, grafica 3-D, animazione);
9) Applicazioni cliniche:ortopedia e chirurgia funzionale (endoprotesi, mezzi di sintesi ossea, trasposizioni muscolari); riabilitazione (ortesi e sistemi ibridi)
Esercitazioni:
Le esercitazioni sono dedicate all’approfondimento dei temi trattati durante il corso e comprenderanno dimostrazioni con l'utilizzo di tecnologia traspotabile in aula ed esercitazioni al computer.
Laboratorio:
Verranno svolte dimostrazioni relative all'utilizzo delle strumentazioni più significative per l'analisi del movimento
Bioengineering of the motor system
Automotive properties of the human motor system. Energetics: mechanical work, efficiency, methabolic to mechanic energy conversion. Functional properties of the muscle, in relation to structure, fibre types, anatomical arrangement. The motor control system: spinal reflexes, synergies, motor programmes, models. Methods for experimental investigation with and without perturbation, invasive/non invasive measurements. Kinematic description of multiple joint system: reference frames, roto-translation matrices, functional axes. The inverse dynamics problems: dynamic equilibrium equations, inertia and visco-elastic parameters estimation, internal forces estimation. Principles of the forward dynamics approach. Musculo-skeletal models for the analysis and simulation of human movement. Clinical applications (functional orthopaedic surgery, prostheses).
Testi consigliati:
L'intero contenuto del corso è disponibile on line. Inoltre si consiglia la consultazione dei testi riportati in bibliografia

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
La prova d’esame consiste in una prova scritta, della durata di 75 minuti, articolata in domande a risposta aperta ed esercizi, seguita da un’eventuale prova orale facoltativa, alla quale è possibile accedere solo previo superamento della prova scritta. In caso di svolgimento della prova orale, il voto finale sarà determinato dalla media dei voti conseguiti nelle due prove.
La conoscenza e la comprensione degli argomenti del corso sono valutate mediante domande specifiche ed esercizi applicativi.
La capacità di comprensione e applicazione delle conoscenze acquisite è verificata attraverso quesiti relativi agli argomenti affrontati durante le esercitazioni.

Carlo Albino Frigo, Bioingegneria del sistema motorio, Editore: Aracne Editore edizione: 2018, ISBN: 978-88-255-1573-2
J.A. Kapandji, Fisiologia articolare: schemi commentati di biomeccanica umana (Vol. 1-2), Editore: DEMI Roma, Anno edizione: 1974
Giovanni Legnani e Giacomo Paglieri, Fondamenti di Meccanica e Biomeccanica del Movimento, Editore: CittàStudi Edizioni, Anno edizione: 2016, ISBN: 978-88-251-7407-6

Obiettivi dell'insegnamento
L’insegnamento integrato di Biomateriali ha l’obiettivo di fornire agli studenti gli strumenti per analizzare criticamente la selezione, il comportamento e la progettazione dei biomateriali impiegati nella realizzazione di dispositivi medici per applicazioni a contatto con tessuti duri e molli. Partendo da casi clinici e applicativi, l’insegnamento guida gli studenti nel collegare i requisiti funzionali e biologici dei dispositivi alle proprietà dei materiali, alla loro struttura e storia di processo, ai fenomeni di degradazione nell’ambiente fisiologico e alle interazioni tra materiale e organismo. Il percorso didattico è organizzato secondo una struttura comune ai due moduli, articolata in quattro blocchi tematici: i) relazione tra struttura e proprietà dei materiali, ii) effetti dell’ambiente fisiologico sui materiali, iii) risposta biologica e integrazione, iv) strategie ingegneristiche e tecnologie di fabbricazione per controllare proprietà, degradazione e risposta dell’organismo. Il modulo modulo Biomateriali [2] è dedicato prevalentemente ai biomateriali polimerici e ai dispositivi per applicazioni a contatto con tessuti molli, con particolare riferimento agli ambiti cardiovascolare, oftalmologico, della chirurgia ricostruttiva e del wound healing. L’insegnamento mira a sviluppare la capacità di interpretare criticamente dati sperimentali e letteratura scientifica del settore, confrontare soluzioni alternative e formulare scelte motivate di materiali, processi, trattamenti e metodi di caratterizzazione in relazione a specifici problemi applicativi.
Learning Objectives
The integrated Biomaterials course aims to provide students with the knowledge and analytical tools required to critically evaluate the selection, performance, and design of biomaterials used in the development of medical devices intended for applications involving contact with hard and soft tissues. Drawing on application-oriented case studies, the course guides students in establishing connections between the functional and biological requirements of medical devices and the properties of biomaterials, their structure and processing history, degradation phenomena occurring in the physiological environment, and the interactions between materials and the host. The course is organized around a common framework shared by both modules and structured into four thematic blocks: i) the relationship between material structure and properties ii) the effects of the physiological environment on materials iii)biological response and tissue integration iv) engineering strategies and manufacturing technologies for controlling material properties, degradation, and the biological response. Biomaterials [2] is primarily devoted to biomaterials and medical devices for applications involving soft tissues, with particular emphasis on the cardiovascular, ophthalmic, reconstructive surgery, and wound care fields. The course aims to develop students ability to critically interpret experimental data and relevant scientific literature, compare alternative solutions, and make well-reasoned decisions regarding the selection of materials, manufacturing processes, surface treatments, and characterization methods in relation to specific biomedical applications.

Risultati di apprendimento attesi
A seguito del superamento dell’esame, lo studente:
conosce e comprende le principali classi di biomateriali impiegate in dispositivi per applicazioni a contatto con tessuti molli ed è in grado di spiegare come composizione, struttura, processo produttivo e ambiente fisiologico ne determinino proprietà, degradazione, risposta biologica e prestazioni; (DD1)
è in grado di analizzare specifici problemi applicativi, individuando i requisiti clinici, meccanici e biologici rilevanti e correlando tali requisiti alla selezione dei materiali, ai processi di fabbricazione, ai metodi di caratterizzazione e alle strategie utilizzate per controllare degradazione e interazione con l’organismo; (DD2)
è in grado di interpretare criticamente dati sperimentali e letteratura scientifica, formulare conclusioni proporzionate alle evidenze disponibili e sviluppare, individualmente o in gruppo, proposte motivate per problemi applicativi nel settore dei biomateriali, selezionando le informazioni rilevanti, organizzando risorse e tempi e comunicando in modo chiaro ed efficace il razionale, i vantaggi e i limiti delle soluzioni considerate. (DD2–DD3–DD4–DD5)
Expected learning outcomes
Upon successful completion of the course, the student will:
know and understand the main classes of biomaterials used in devices for soft-tissue applications and explain how composition, structure, manufacturing processes and the physiological environment determine their properties, degradation, biological response and performance; (DD1)
be able to analyse specific application problems by identifying the relevant clinical, mechanical and biological requirements and relating them to material selection, manufacturing processes, characterization methods and the strategies used to control degradation and host interaction; (DD2)
be able to critically interpret experimental data and scientific literature, draw conclusions proportionate to the available evidence, and develop, individually or in groups, justified proposals for application problems in the biomaterials field, selecting relevant information, organizing resources and time, and clearly and effectively communicating the rationale, advantages and limitations of the considered solutions. (DD2–DD3–DD4–DD5)

Argomenti trattati
Progettazione di polimeri stabili nell' ambiente biologico: evoluzione, composizione, caratterizzazione. Polimeri biodegradabili di sintesi: composizione, lavorazione, sterilizzazione e meccanismi di degradazione. Caratterizzazione della degradazione in vitro e in vivo. Studio delle proprietà dinamico-meccaniche dei biomateriali polimerici. Caratterizzazione reologica. Tecniche di caratterizzazione di superficie. Soluzioni bioispirate e applicazione di smart materials per dispositivi impiantabili.
Le nozioni teoriche saranno sempre applicate a diversi casi studio e casi applicativi relativi a materiali impiantabili a contatto con tessuti molli (in particolare in chirurgia cardiovascolare, oftalmologica, ricostruttiva e wound healing) che consentano di apprezzare vantaggi e limiti delle diverse strategie progettuali, nonchè delle potenzialità e delle limitazioni delle tecniche di caratterizzazione dei materiali e dell'interazione con l'ospite.
Sono anche previsti alcuni seminari per approfondire, grazie al contributo di ospiti esperti, alcune delle tematiche affrontate durante le lezioni.
Course Content
Design of polymers stable in the biological environment: evolution, composition, and characterization. Synthetic biodegradable polymers: composition, processing, sterilization, and degradation mechanisms. Techniques for the characterization of in vitro and in vivo degradation. Dynamic mechanical properties of polymeric biomaterials. Rheological characterization. Surface characterization techniques. Bioinspired approaches and application of smart materials for implantable medical devices.
The theoretical concepts will be applied to a range of case studies and practical applications involving implantable materials in contact with soft tissues, particularly in cardiovascular, ophthalmic, and reconstructive surgery, as well as wound healing. These case studies are intended to illustrate the advantages and limitations of different design strategies, as well as the strengths and constraints of material characterization techniques and the assessment of host–material interactions.
The course also includes a number of seminars delivered by invited experts, providing students with the opportunity to explore selected topics covered during the lectures in greater depth.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
Modalità di valutazione
La valutazione dell’insegnamento prevede il superamento di una prova scritta obbligatoria per ciascuno dei due moduli, secondo le modalità illustrate all’inizio del corso. Di norma, non è prevista una prova orale.
Le prove scritte comprendono quesiti a risposta multipla e domande aperte, finalizzati ad accertare la conoscenza e la comprensione degli argomenti trattati, nonché la capacità di applicare le conoscenze acquisite alla risoluzione di problemi specifici, interpretare criticamente dati sperimentali e formulare conclusioni motivate.
Gli studenti possono inoltre partecipare ad attività di gruppo facoltative, comuni ai due moduli, dedicate all’analisi critica della letteratura scientifica e di casi studio. Tali attività richiedono di reperire e selezionare informazioni da fonti scientifiche pertinenti, organizzare il lavoro di gruppo e le risorse disponibili, rispettare le scadenze previste, predisporre una presentazione ed esporla nella data concordata, comunicando in modo chiaro, efficace e rigoroso gli aspetti più rilevanti. Le presentazioni sono seguite da una discussione e da un commento da parte del docente. La valutazione di queste attività facoltative può concorrere alla determinazione del voto finale, secondo i criteri illustrati all’inizio del corso e indicate nel materiale didattico fornito.
Assessment Methods
Assessment for this course consists of a compulsory written examination for each module, according to the procedures communicated at the beginning of the course. An oral examination is not normally required.
Each written examination includes multiple-choice questions and open-ended questions designed to assess student knowledge and understanding of the course contents, as well as their ability to apply acquired knowledge to specific problems, critically interpret experimental data, and draw well-supported conclusions.
Students may also participate in optional group activities shared by both modules, focusing on the critical analysis of scientific literature and case studies. These activities require students to identify and select relevant information from appropriate scientific sources, organize group work and available resources, meet assigned deadlines, and deliver an oral presentation on an agreed date while communicating the key findings clearly, effectively, and rigorously.
Each presentation is followed by a discussion and a feedback from teachers. The assessment of these complementary activities may contribute to the final course grade, according to the criteria communicated at the beginning of the course and described in the uploaded materials.

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

Il corso fornisce una formazione sistematica sui moderni meccanismi crittografici utilizzati per le comunicazioni e la memorizzazione dei dati. Il corso si focalizza sugli algoritmi e le relative implementazioni efficienti in Software e Hardware, non trascurando l'indicazione di nuovi approcci e critto-sistemi innovativi. Saranno presentati gli aspetti matematici della crittografia moderna, preceduti dalle nozioni essenziali della teoria dei campi di Galois. La sicurezza dei più importanti critto-sistemi sarà analizzata anche dal punto di vista della loro realizzazione, presentando le vulnerabilità a cui i dispositivi Hardware e Software possono essere soggetti. Infine, verranno presentati i protocolli di comunicazione sicura più comuni (SSL/TLS, SSH, PGP, Kerberos, Onion Routing) e i protocolli per la memorizzazione di dati cifrati.

The course, held substantially in presence in the laboratory-classroom, aims to provide students the basic skills to learn using modern instrumentation for data acquisition by programmable and reconfigurable virtual systems, according to user requirements for different applications. Methods to process and analyze statistically large data sets, and transmit and save them, will also be discussed.
The course includes, in addition to the traditional classroom lectures, tutorials and project workshops (not mandatory) where students will study in deep specific topics, addressing different issues. The verification of the knowledge acquired is based on written tests during the course and on the evidence of recovery. The final evaluation will be based on a final exam where the students need to develop a custom application.

Knowledge and understanding (DdD1)
Students will learn how to:
what is the modern instrumention and the "measurement chain" used to acquire data from environment;
which are the fundamentals rules used to acquire date without losing informations;
the basic structures of a data acquisition programming language;
how to manage data set: elaborate, manipulate and save it in different kind of format.
Applying knowledge and understanding (DdD2)
Given specific project cases, students will be able to:
Detail the corresponding requirements;
To understand the requirements and to design the correct structure to manage the data;
Analize and comment on specific architectural choices;
Develop and test code fulfilling the design specifications.
Lifelong learning skills (DdD3 - DdD5)
Students will learn how to develop a real project collaborating with other students in the development of the project;
Students choosing to work on the research project, will learn how to conduct a research activity on some specific aspect engineering, developing some innovative solution and some empirical experiment.

Introduction to measurement instrumentation. The analog-to-digital conversion; resolution and accuracy limits for numeric values; effective number of bits of the A/D converter and the dithering techniques to increase resolution. Description of the Data AcQuisition systems (DAQ) performances and limits; mode of installation and use of PCI cards for the acquisition channel and the generation of control signals. Introduction to the concepts of data interpolation and fitting. How to count something: the counters.
Virtual instrumentation. The measurement instruments available on the market. The LabVIEW graphical environment. The front panel. The block diagram. Control palette and function palette, constants, indicators, and controls. How to display data. Nodes and lines. Loop structures, case, sequence. Array and cluster. SubVI. Logic functions. Development of example programs. Debugging. Waveform Data. File management. Storing and recalling data into/from a file. Development of an algorithm for the measurement of the signal frequency.
Elements of hardware and software. Data acquisition cards. Specifications. Acquisitions with buffering. Local and global variables. Built-in elements. Practical examples and their implementation.
Design and implementation of Virtual Instruments (VIs). Design and testing of a “digital voltmeter”. The acquisition section. The digital processing section. Indirect measures based on signal processing. Multichannel acquisition. Practical aspects of the multiplexer. Signal Conditioning. Development of some practical applications. Processing of multi-channel data. Testing and calibration of a virtual instrument.
Applications. Output functions. Arbitrary signal generator. AD and DA converters total error evaluation. Use of the counters. Control gate. Acquisitions for long times. Use of virtual instruments through the Internet. Queues and semaphores to administrate data.
Instrumentation programming. Communication interfaces (RS232, GPIB). The driver for stand-alone instrumentation: VISA. Communication with oscilloscopes and function generators via various communication interfaces.
Laboratory activities
Creation of virtual instruments for the acquisition from the field of experimental data: processing, graphical representation, data logging and statistical analysis (average, moving average, variance) of numerical data. Realization of programs for the measurement of analog and digital signals. Generation of analog and digital signals. Development of specific projects for students. Every student of Data Acquisition Systems held 32 hours of laboratory divided into 8 sessions during each session will be carried out individually but always under the guidance of the professor, the implementation of programs progressively more advanced. The details of the evaluation process of the workshop will be presented during the first hours of lesson of the course.

Prova scritta obbligatoria, con prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
Exams assign a score of thirty, from 0 up to the maximum grade of 30 cum laude, extracting it from the results achieved with the work in the classroom and with the project/exam in the presence, and the outcome of a short theory written test. For anyone who wants to further improve the rating will be possible to perform an oral or develop an optional data acquisition project.
In any case, examination procedures will be consistent with the rules adopted by the Politecnico di Milano and the School of Eng. Industrial and Information and the dates of each exam will be communicated well in advance.
Type of assessment
Description
Dublin descriptor
Written test
Theory of acquisition:
· Knowledge of the fundamentals of the theory of acquisition;
· Knowledge of the state of the art of measurement instrumentation;

• Knowledge of fundamentals of interpolation and fitting.
  Solution of acquisition problems
  · Development of the right structure of acquisition;
  · Estimation of the computational costs;
  · Managing data and how to elaborate them;
  · Software testing debugging and analysis exercises.
  Exercises focusing on design aspects
  · Definition of the architecture for a distributed measurement system
  · Organisation of different parallel tasks inside a project.
  1,2
  1, 2, 3, 4, 5
  Assessment of laboratory artifacts (optional)
  Assessment of the design and experimental work of acquisition developed by students individually
  2, 3, 4, 5

C. Svelto, R. Ottoboni, M. Norgia, A. Pesatori, Misure. Dai fondamenti alla strumentazione, Editore: Società Editrice Esculapio - Bologna, Anno edizione: 2022, ISBN: 8893853043
Note: Costo circa 19 Euro. Disponibile presso la Biblioteca di PoliMI.
M. Norgia, A. Pesatori, C. Svelto, "Esercizi di Misure", Editore: Società Editrice Esculapio - Bologna, Anno edizione: 2006, ISBN: 88-7488-150-9
Note: Costo circa 15 Euro. Disponibile presso la Biblioteca di PoliMI.

Given the always increasing diffusion of mobile and wearable devices (e.g., smartphones, tables, and watches), the course aims to present the main techniques and technologies to design and implement applications for these devices. More specifically, the course will address the design of "mobile" applications by considering both the problem of designing the user experience, that is, the screens used to interact with the user, and the problem of understanding the actual distribution of the components that constitute the application and their interactions.
As for technologies, the course will present the main solutions that are available today. It will cover both iOS-based (iPhone/iPad) and Android-based applications. It will also present cross-platform options as the emerging solution for the realization of cross-platform "mobile" applications.

DdD1: Knowledge and understanding
Students will learn how to:

• Identify the key elements of a modern mobile application
• Design appropriate GUIs
• Design and implement complete mobile applications using different technologies
• Select among different architectural and technological options
  DdD2: Applying knowledge and understanding
  Given specific applications, students will be able to:
• Detail concrete and efficient GUIs
• Identify and select among different architectural and technological choices
• Apply theory to assess designed solutions and available technological alternatives
• Design, document, develop and test complex mobile applications.
  DdD3: Making judgements
  Given relatively complex mobile applications, students will be able to:
• Analyze and understand the requirements associated with the mobile applications and the devices of interest
• Define the architecture of the corresponding mobile application using proper styles and patterns
• Identify and define the needed external elements, libraries, and services
  DdD4: Communication
  Students will learn to:
• Write a design document
• Conceive a technical presentation
• Present their work in front of the professor and their colleagues
  DdD5: Lifelong learning skills
  Students will learn how to
• Develop mobile applications
• Identify alternatives and select the proper technologies
• Frame and evaluate new, future technologies.

The course is roughly divided in five parts:

• The first part frames the problem and the new/many opportunities these devices offer. It also gives a brief survey of the different alternatives and "competing" solutions.
• The second part is about the design of mobile applications. It tries to identify some guidelines and recurring patterns to "ease" the design of these applications and also to help obtain quality solutions.
• The third part introduces the main novelties and characteristics of some key solutions (Flutter and React Native) for the development of cross-platform applications.
• The fourth part is is about Android and about the development of applications for this operating system, and for the family of devices (phones, tablets, watches, TVs) that use it, by means of Kotlin and Jetpack compose.
• The fifth part introduces the development of applications for iOS-based devices by using Swift and SwiftUI.

Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The exam consists of a project that must be developed by a group of up to 3 students (max). The project must be documented, presented, demoed, and discussed with the professor.
Type of assessment
Description
Dublin descriptor
Assessment of project artifacts
Assessment of the design and experimental work developed by students, either individually or in groups, as part of the project
1, 2, 3, 4, 5
Oral presentation
Assessment of the project presentation
2, 3, 4, 5

Flutter official documentation https://reactnative.dev
React Native official documentation https://reactnative.dev
Android official documentation https://www.android.com
iOS official documentation https://developer.apple.com/ios/planning/

The course aims at presenting the design of hardware accelerators used to speed up the execution of complex applications. The course will then dig into the languages to model hardware/software systems and the methods to automatically derive hardware designs for specific hardware blocks. The course targets two classes of students. Students with a software background will learn how to use FPGA-based technology from a software viewpoint, understanding how to accelerate specific computational kernels with existing methods and tools. Students with architectural and hardware backgrounds will understand how to optimize the generation of hardware architectures from software-level specifications, along with the abstraction gaps. Verification of the generated hardware and integration into complex systems will play a key role from both hardware and software viewpoints. Hands on-sessions will be dedicated to methods and tools applied to specific use-cases, ranging from machine learning models to big data applications, so that the students can practice the methods presented during the lectures.

[Knowledge and understanding] Students will learn how to:
analyze and optimize the performance of a hardware accelerator;
optimize a hardware specification with respect to a given technology;
synthesize and verify complex accelerators;
understand how to integrate accelerators in complex hardware/software systems.
[Applying knowledge and understanding] Given specific problems, students will be able to:
understand the complexity of hardware designs;
understand possible optimizations applicable to hardware accelerators;
understand how to identify potential bottlenecks in a given design;
understand how to design accelerators for modern applications
[Making judgments] Given a relatively complex accelerator, students will be able to:
understand if the obtained performance (area, delay, power, etc.) is in line with the given models;
understand if the proposed design fits the functional and non-functional requirements;
understand how to apply existing approaches to a given problem.
[Communication] Students will learn to:
present critically the existing state of the art, showing where it could be improved or where it is inconsistent;
present their work discussing the pros and the cons of the proposed solution.
[Lifelong learning skills] Students will understand how to design, analyze, evaluate, and optimize complex hardware systems, possibly interfaced with software. Use cases will be used for getting experience on real problems

Introduction to the System-on-Chip paradigm
The dark silicon era
Basic components of Systems-on-Chip
HW/SW codesign fundamentals
Design flows
Introduction to FPGA technology
Specification of heterogeneous systems
Software-level accelerator description (C/C++/OpenCL)
Templates of hardware modules (VHDL/VERILOG)
High-level synthesis
Basic concepts
Optimizations: decomposition, pipelining, replication
Design of dataflow accelerators
Interface design
Latency-insensitive design
Communication protocols (AXI, etc)
Hardware interfaces (DMA, memory controllers etc)
Software interfaces (memory-mapped registers, device drivers)
Functional verification
Reliability
HW Security
Applications and use cases
Cloud-based FPGA
Accelerators for machine learning and Big Data applications
Hands-on laboratory sessions with FPGA-based designs

Prova orale obbligatoria
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), individuale, senza revisioni periodiche, con presentazione/discussione finale
The students will be evaluated based on the following learning skills ( Dublin descriptors 1,2,3,4,5):

• knowing and understanding the terms to describe the architecture and the components of a complex digital system
• analyzing and understanding the effects of different methods for the design of heterogeneous architectures
• making judgments and proposing simple solutions for the optimization of given digital systems
• understanding the proper methods to evaluate and present the results of their own practical activity
  The students will be evaluated with an oral discussion. A part of the oral exam can be replaced with a project that may be assigned to a maximum of two people.

The goal of the course is to make students familiar with concepts of communication and information theory and with their application to the analysis and design of complex telecommunication systems. Moreover, the course provides students with the tools that are needed to understand and apply advanced technologies that are presently used in telecommunication systems. Also, understanding, analyzing and designing optical and radio communication systems is one of the objectives of the course.
The course is finalized to teach students advanced concepts of digital communication as equalization, predistortion, synchronization, MIMO systems. The goal is to make the student familiar with these concepts and with their application to digital transmitters and receivers of radio and optical communication systems. Design of equalization algorithms in the presence of dispersive channels, predistortion algorithms for memoryless non-linear channels, carrier and timing synchronization systems, MIMO receivers are among the goals of the course. Also, learning the mathematical tools needed to understand how these systems work and how their performance can be analyzed is among the objectives of the course. The goal of using commercial software tools as Simulink and Matlab to encode software for the simulation of communication systems will be achieved by laboratory sessions.
Blended learning and flipped classroom will be adopted for about 2/5 of the course (2 CFU) to show the relationship between digital communication and other courses in the field of communication and signal processing and to gain the ability of autonomously develop software programs that put together concepts from different courses applying them to a real-world project.

Dublin Descriptors
Expected learning outcomes
1 - Knowledge and understanding
Understand the general principles of advanced topics of digital communication. Analyze and understand the requirements associated to a communication system in terms of equalization, predistortion, synchronization.
2 - Applying knowledge and understanding
Identify and define at the block-diagram level a proper equalization, predistortion, synchronization scheme for the most common modulation schemes. Encode software by common commercial tools as Matlab and/or Simulink to simulate the chain of transmitter, channel, receiver of data communication systems.
3 - Making judgements
Judge if a given equalization, predistortion, synchronization scheme is appropriate for a practical radio or optical communication system. Recognize the design space and the degrees of freedom that can be exploited to reach given objectives in terms of performance of equalization, predistortion, synchronization, with specific application to practical radio and optical communication systems.
4 - Communication
Communicate to engineers in technical documents and presentations about the analysis and design of equalization, predistortion, synchronization, MIMO systems using the appropriate mathematical tools as Fourier analysis, probability theory, calculus and matrix algebra. Communicate to non-engineers by block diagrams and writings about the needs and constraints that advanced telecommunication systems are subject to in terms of receiver performance.

Equalization. Wiener’s method. Discrete-time AWGN model, FIR and unconstrained linear equalization, adaptive equalization, decision feedback equalization, channel capacity with decision feedback equalization. Maximum likelihood sequence detection by the Viterbi algorithm. Maximum a posteriori probability sequence detection by the BCJR algorithm.
Precoding. Tomlinson-Harashima precoding, partial response systems, examples.
Predistortion. AM-AM and AM-PM characteristic of power amplifiers. Adaptive predistortion of memoryless power amplifiers.
Elements of MIMO transmission. The MIMO principle, the MIMO Gaussian channel, detection for the memoryless MIMO channel, detection for the MIMO channel with memory.
Phase-lock loop. Continuous-time PLL, discrete-time PLL, first-order and second-order loop; common phase detectors, design of the loop filter in the presence of phase noise.
Carrier and timing recovery. Decision-directed phase detector, S-curve, power of M phase detector, differential encoding, feed-forward and feed-back carrier recovery. Square-law timing detector, Gardner detector; digital re-sampling.
Laboratory activities. Use of Simulink and Matlab for simulation of digital communication systems.

Prova scritta obbligatoria, 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
At the exam, the student will be required to demonstrate the achievement of the learning objectives. The exam can be taken in any one of the July, September, February sessions. The exam consists in a written part based on a question about the theory, in an oral part based on the theory and on the written exam. The oral part can include an optional (up to the student) presentation of the laboratory activities. The student will be asked to answer questions regarding the theory, to draw block diagrams of digital communication systems and to explain them by writings and equations, to compute system performance in terms of error probability and/or mean square error, to explain and comment on the code encoded during the laboratory activity (optional). The written part is finalized to check DD1,DD2,DD3. The oral part is finalized to check DD1,DD2,DD3,DD4.
The written plus oral can assign up to 26/30 points. Optional project (code plus written report) based on the laboratories: 4/30 points + possibility of laude.

Barry Lee Messerschmitt, Digital Communication
Notes of the teacher https://beep.metid.polimi.it/

The course builds on basic principles and concepts (in the field of electric circuit analysis, signals and systems, electronics, and electromagnetic fields) to provide an introduction to Electromagnetic Compatibility (EMC). The course is designed to help students develop a deeper understanding of electromagnetic interference and the prevention of it through suitable design strategies. The addressed subjects cover: nonideal behavior of components, transmission line circuit models for EMC, conducted emissions and susceptibility, radiated emissions and susceptibility, antennas (basic principles, test and measurement antennas for EMC), crosstalk, shielding, grounding, measurement instrumentation, setups and facilities for EMC testing, governmental and functional requirements, system design for EMC. Practical applications considered in this course concern EMC aspects in complex electrical and electronic systems and environments, such as those found in aerospace, automotive, electric power generation, transmission/delivery, and railway industry sectors.

The course is designed to equip students with the skills needed to develop a technological project in the biomedical field, guiding them through the following key stages:
Defining Application Requirements: Identifying specific needs of the biomedical project, such as relevant variables, signal acquisition, processing, and electronic design.
Component Selection: Choosing suitable components, whether custom or commercial, to meet the project's needs.
System Architecture Design: Planning the overall system structure, including sensors, front-end circuits, and data processing and visualization.
Hardware and Software Implementation: Developing the system using appropriate development tools and techniques for practical implementation.
Verification and Performance Evaluation: Applying suitable procedures to test the functionality and assess the performance of the developed prototype.
Technical Report Writing: Documenting the project details, methodologies, and outcomes in a comprehensive technical report.

Students will gain experience with key electronic laboratory tools (including the soldering iron, multimeter, oscilloscope, and power supply) and will learn essential engineering methods (such as application analysis, requirement definition, system design, and component selection) to develop biomedical technology projects. (Lectures and practical exercises). (DD 1)
Students will develop skills in designing electronic boards, writing firmware for microcontrollers, and building hardware prototypes using discrete components and devices for acquiring and processing biomedical signals. (Exercises and laboratory practice) (DD 1.2)
Through practical application, students will learn to apply their knowledge to define project requirements, outline key development phases, and select appropriate tools and components, with a focus on biomedical applications of interest. (Development of educational projects) (DD 3.4)
Students will learn how to collaborate in groups on project development and present a selected topic to the class in a flipped classroom format. (Development of educational projects) (DD 4.5)

The course covers the methodologies and electronic technologies for the development of systems dedicated to: i) the use of sensors and actuators in the medical field; ii) acquisition, conditioning, and processing of biomedical signals; iii) monitoring of parameters related to the person and the surrounding environment. The course is structured in two main phases.
The first phase includes lectures and exercises where the instructor presents the following topics: i) Measurement tools typically used in an electronic laboratory, such as testers/multimeters, power supplies, oscilloscopes, and signal generators; ii) Technologies for hardware and firmware design, including circuit design, PCB design, and platforms for microcontroller-based design and development; iii) Basics of analog and digital sensors and actuators; iv) Environments and platforms for software interface development; v) 3D printing technologies for the design of mechanical supports.
The second phase of the course involves group project development. Students are divided into groups and guided through the specific project topics assigned. Students will tackle the entire chain of activities required, starting from conceptual design and requirements definition, component selection, circuit design and implementation, microcontroller firmware programming, and concluding with the final testing phase of the developed prototype.
To complement the project activities and promote active learning, structured thinking, knowledge acquisition, and peer discussions, each student group will be responsible for exploring a specific topic related to the ongoing project, which will be presented and discussed in a flipped classroom format.
Interaction between the instructor and students is supported by the WeBeep platform, which provides course slides, documents such as tutorials and development examples, datasheets of general interest for HW/SW components, and submission of project reports for the final presentation.
The examination consists of evaluating a final project report and an oral presentation.
Important Note
This is a limited-access course. The mandatory procedure for access requests is here:
www.ccsbio.polimi.it/laurea-magistrale/?lang=en#Laboratory_Courses_LAB_courses

Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
The final evaluation consists of assessing a group project report and an oral presentation conducted after the course concludes. The final grade will also consider additional factors, including student commitment, attendance and participation in laboratory activities, teamwork skills, and overall mastery of the course content.

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

The aim of the course-module 1-electronics is to provide an in-depth knowledge on electronic devices, electronic circuits and systems used for the acquisition, processing, recording, Analog-Digital Conversion of audio signals.

Dublin Descriptors
Expected learning outcomes
1) Knowledge and understanding
· know and understand the physical properties of the different amplifying devices used for audio signals.
· know the advanced circuits and systems for audio signal processing.
· know and understand the origin of the electronic noise and signal distortion in the electron devices and amplifiers.
· know how to enhance the signal to noise ratio in a circuit and in complex electronic system.
2) Applying knowledge and understanding
· Analize specifications of electron devices.
· Analize specifications of audio electronics.
· Design systems for analog signal processing.
5) Lifelong learning skills
· Students will learn how to analyze and design multi-stage electronic circuits for audio signal.

Solid state electron devices for audio systems: BJT, JFET and MOSFET transistors: static and dynamic characteristics and parameters.
Thermionic valves for audio systems: diode, triode, tetrode and pentode: physics, static and dynamic characteristics and parameters.
Electronics for audio signal analog processing: single and multi stage transistor and valve amplifiers, signal transformers, preamplifiers, differential and instrumentation amplifiers, balanced input and output stages, phantom power supply, gain control, summing amplifiers, active filters, Baxandall tone control, resonant filters, graphic and parametric equalizers, voltage control amplifiers (VCA), gate amplifiers, compressors.
Mixing consoles: Analog and digital console architecture, preamplification, equalization, aux and group sections, pan pot, VCA section, mixing matrix, insert, buses, output modules.
Noise in electron devices and circuits: electronic noise: physics and models, noise power spectral densities, r.m.s. values; thermal, shot, 1/f noises. Noise characteristics of resistors, diodes, BJTs, JFETs, MOSFETs, Thermionic Valves. Noise model of amplifiers. Noise in multi-stage amplifiers.
Signal distortion in electronic circuits: distortion in BJT and FET amplifiers. Distortion analysis and measurement.
Power amplifiers: Class A, AB, C, D output stages; efficiency, distortion, output impedance. Specifications of Professional high power amplifiers.

Type of Assessment
Description
Dublin descriptor
Written test
· Solution of numerical problems on audio analog circuits
· Critical di scussion of a circuit
· Critical discussion on the data-sheet of a device, circuit or system for audio signal processing
· Questions on topics of the course’s program
1, 2
1, 2, 5
Oral test
· Discussion on problems of the written test
· Questions on topics of the course’s program
· Analytical solution of problems on circuit or system for audio signal processing

• Analysis of an audio signal processor chosen by the student (mandatory for students of Master in Electronics Engineering)
  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
Giuseppe Bertuccio, Lecture slides and notes
Note: see EEASE course on Beep web site
Glen Ballou, HANDBOOK FOR SOUND ENGINEERS, Editore: Elsevier, Anno edizione: 2008, ISBN: 978-0-240-80758-4 https://www.sciencedirect.com/book/9780240809694/handbook-for-sound-engineers
John Linsley Hood, Audio Electronics, Editore: NEWNES - ELSEVIER, Anno edizione: 1998, ISBN: 978-0-7506-4332 https://www.sciencedirect.com/book/9780750643320/audio-electronics

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

The goal of the course is to provide the students with the theoretical knowledge and the methodologies necessary for modelling, analyzing, and designing wireless links for space-borne systems, both at radio and optical frequencies, in diversified Earth-space scenarios (e.g. Earth observation missions, satellite communications - SatCom, global navigation satellite systems - GNSS). Moreover, the course provides the students with the instruments for understanding and applying the advanced technologies used in modern wireless links. The course offers a balanced compromise between theoretical aspects and practical ones: first, the physical aspects of the phenomena are duly introduced; second, the theory behind them is addressed; third: a practical mathematical framework is derived useful for system design and analysis. The course also includes brief seminars providing an overview on “real world” systems (e.g. experimental measurements) and offers a practical laboratory session focused on designing a realistic Earth-space link.

Knowledge and understanding (DD1)
Students will understand and learn
The main features of electromagnetic waves
The physical concepts governing the unguided propagation of electromagnetic waves in different materials
The effects induced by the atmosphere (troposphere and ionosphere) on the propagation of electromagnetic waves, as well as the impact of such effects on Space Communication and Navigation systems
The impact of noise on Space Communication and Navigation systems
The mathematical framework for the analysis and design of Earth-space links operating at radio and optical frequencies
The key elements and blocks composing complex communication systems relying on unguided electromagnetic wave propagation
Some elements of GNSSs
Applying knowledge and understanding (DD2)
Students will be able to:
Select the most suitable wireless link and system (e.g. operational frequency, type of satellite orbit) for a given scenario and service to be provided
Dimension the elements of a wireless link to meet target requirements, such as the link availability and quality
Analyse the performance of wireless links in diversified scenarios based on system specifications, as well as on operational and standardization constraints (e.g. ITU-R Radio Regulations)
Identify and evaluate the key tradeoffs that drive the design of wireless links
Identify the fundamental system limitations impacting the design of a wireless link

Theory:

• Introduction: contents of the course and main protagonists in the history of electromagnetic wave propagation and space-borne systems.
• The electromagnetic spectrum: frequency bands and spectrum management.
• Main features of electromagnetic waves (frequency, wavelength, polarization,...) and electromagnetic characterizations of materials (electric permittivity and magnetic permeability, conductivity,...).
• Basic propagation mechanisms at radio frequency and optical wavelengths (Free Space Optics): direct wave, reflected wave, evanescent waves.
• Propagation in the ionosphere. Refraction, attenuation, Faraday rotation, phase advance and group delay. Basic principles of radio-positioning in Global Navigation Satellite Systems (GNSS) and impact of the ionosphere on such systems.
• Propagation in the non-ionized atmosphere (troposphere) and impact on Earth-space (HAPs, LEO, MEO, GEO satellites, Deep Space probes) links. Clear air propagation: refraction and ray bending, attenuation, scintillation, tropospheric scatter. Propagation through clouds. Adverse weather disturbances: attenuation, depolarization, electromagnetic interference due to hydrometeors.
• Atmospheric and extra-atmospheric noise sources: impact on the signal detection and some concepts on ground-based passive remote sensing.
• Statistical characterization of the radio channel and system design: the link budget. Basics on fade mitigation techniques (e.g. site, frequency and time diversity, uplink power control, …).
  Practice:
• Problem solving on all the topics listed above.
• Presentation and use of the following software tools: propagation of plane waves through multi-layered materials.
• Mentions to ITU-R recommendation, propagation series.
  Laboratory activities:
• Practical session focused on designing a realistic Earth-space link, making reference to ITU-R recommendations (e.g. P-series) and transmission standards (e.g. DVB-S2).

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
The exam includes a written test and an oral test:
The written test consists in the solution of 4 problems relative to the course topics (e.g. ionospheric propagation and impact of rain on wireless links): past exams, with solutions, are available on the course webpage. This test aims at assessing the ability of the student to solve problems Earth-space link design and EM wave propagation using the tools and concepts presented during the lectures (DD1, DD2). The test is open book: the student can use any material (e.g. personal notes, books) during the written test, but NOT electronic devices. The duration of the written test is typically between 2 hours and 2 hours and 15 minutes.
The oral test is optional for those obtaining a score equal to or higher than 18 in the written exam (increase in the score of up to 3 points), and mandatory for those who obtain a score of 17 in the written exam. The oral test aims at evaluating the understanding of the physical concepts at the basis of the propagation of electromagnetic waves (DD1, DD2, DD4). The student will be typically asked to explain and discuss, in a conversational way, key concepts, rather than deriving analytical expressions.

C. Riva, G. G. Gentili, Handbook of Electromagnetics, Editore: Maggioli Editore
J.E. Allnutt, Satellite to ground radiowave propagation, Editore: IET
A. Paraboni, M. D'Amico, Radiopropagazione, Editore: McGraw-Hill
Jean-Marie Zogg, GPS - Essentials of Satellite Navigation, Editore: u-blox https://www.zogg-jm.ch/Dateien/GPS_Compendium(GPS-X-02007).pdf
L. Luini, Lecture notes of the course

The course “Embedded and Edge Artificial Intelligence” is designed to provide students with the algorithmic and technological foundations and practical skills needed to design, optimize, and deploy artificial intelligence systems specifically tailored for embedded and edge computing platforms. It focuses on the unique challenges of bringing intelligence to the edge — where memory, energy, and processing power are highly constrained — and explores how AI models can be adapted to operate efficiently on microcontrollers and low-power devices. Students will explore the full stack, from hardware architectures and sensor-level data acquisition to signal processing, model design, quantization, and inference. Particular emphasis is placed on embedded and edge machine and deep learning models, approximate and adaptive computing, early-exit strategies, and on-device learning in non-stationary environments. By the end of the course, students will be able to engineer intelligent, real-time, and context-aware AI systems directly on embedded and edge devices.

DdD1 - Knowledge and understanding
Students will acquire:

• Knowledge and understanding of the hardware architectures for embedded and edge artificial intelligence
• Knowledge and understanding of the main programming models, frameworks and toolboxes for embedded and edge artificial intelligence
• Knowledge and understanding of the distributed and federated frameworks and toolboxes for pervasive applications based on embedded and edge artificial intelligence
  DdD2 - Applying knowledge and understanding
  Given specific problems and project cases, students will be capable to:
• Design and implement pervasive applications based on embedded and edge artificial intelligence
• Apply the most suitable machine and deep learning algorithms for a given family of hardware architectures
  DdD3 - Making judgements
  Given specific problems and project cases, students will be capable to:
• Evaluate the effectiveness and efficiency of hardware architectures for embedded and edge artificial intelligence
• Compare advantages and disadvantages of the different families of machine and deep learning algorithms in pervasive applications based on embedded and edge artificial intelligence
  DdD 5 - Learning skills
  Students will be capable to learn autonomously:
• New frameworks and toolboxes for embedded and edge artificial intelligence
• How to exploit acquired knowledge and skills to consider hardwer architectures for new complex problems

01 – Intro to the course
Introduction to the course, application context of Embedded and Edge AI, teaching structure, and related research areas.
02 – Embedded and Edge Hardware
Overview of embedded system architectures, including sensors, memory, interfaces, and practical memory footprint estimations.
03 – Algorithms for Embedded and Edge AI
End-to-end data processing pipeline: preprocessing, feature extraction, sensor fusion, and trade-offs in latency and accuracy.
04 – Machine Learning for Embedded and Edge AI
Fundamentals of supervised learning for embedded and edge AI, generalization errors, model evaluation methods, and basic neural network concepts.
05 – Deep Learning for Embedded and Edge AI
Computational complexity of deep networks, CNN structures, MAC calculation, and memory analysis for embedded and edge AI
06 – Architectures for EEAI
Lightweight architectures for embedded and edge AI (e.g., SqueezeNet, MobileNet) and design strategies to reduce parameters and operations.
07 – Approximate Computing for EEAI
Quantization and approximate computing techniques to lower memory usage and energy consumption in deep models.
08 – Early Exit Neural Networks
Conditional inference strategies to reduce latency through intermediate classifiers and tailored training approaches.
09 – On-device Learning
Personalized and continuous learning directly on the device, with focus on memory constraints, ethics, and adaptability.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
The exam consists of a written test and a project.
Written test — open questions and exercises on the algorithmic and methodological topics of the course. It assesses DdD1 (knowledge of hardware architectures, programming models, frameworks and toolboxes for embedded, edge and federated AI), DdD2 (application of the course methodologies — memory footprint and MAC estimation, quantization, lightweight architectures, early-exit strategies — and selection of the algorithms most suitable for a given hardware family) and DdD3 (critical comparison of alternative architectural and algorithmic solutions). Criteria: correctness and rigour of the answers, appropriateness of the solution with respect to the stated constraints, ability to argue the trade-offs among accuracy, latency, memory and energy.
Project (mandatory, group work — up to two students, no periodic revisions, no final presentation/discussion) — design and development of an "embedded and edge AI system" for selected case studies, delivered as code and a technical report. It assesses DdD2 (implementation of a pervasive embedded/edge AI application on a real target platform under memory, energy and latency constraints), DdD3 (justification of the design choices and critical evaluation of the results in the report) and DdD5 (autonomous adoption of frameworks, toolboxes and technical documentation not covered in class). Criteria: correctness and completeness of the implementation, soundness of the experimental evaluation, degree of autonomy in the use of new tools.

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 advising of practitioners, i.e. entrepreneurs, venture capitalists, business angels, incubator/accelerator’s 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 feedbacks 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
Understand what is a business model and its building blocks
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.
Through the project work, the students will also be able to:
Test and validate the business model in the real world with potential customers
Write an effective business proposal to ask for funding (i.e., to business angels, venture capitalist, incubator programs..)
Effectively work in teams
Interact with practitioners of the financial sector
Summarize and present the results achieved in the business proposal in front of potential investors clearly and convincingly.

Introductory event: startup success stories and introduction to the course
Startups: the reference strategic framework - external and internal analysis
Startups: the reference strategic framework - value proposition canvas and business model canvas
Getting external resources: hiring, strategic alliances and financing (video clips)
Lean startup approaches

Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
Students must identify a new business idea based on an innovative technology (e.g., the result of current scientific research) or an innovative way of doing business in an incumbent business (e.g., an innovative business model), form an entrepreneurial team, create a document describing the business model and prepare an "elevator pitch".
The grades will be assigned through:

• the evaluation by the faculty of the document describing the business model;
• the evaluation by a commission including faculty members and practitioners of an "elevator pitch" aimed at presenting the business model.
  In the exam, the students must show that:
  They have a good comprehension of the challenges in creating and funding a new start-up and how financial intermediaries and other actors of the financial ecosystem may support the start-up’s development
  They understand how the business model is articulated, and are able to properly build one, tailored to their business idea
  They have effectively validated the business model developed, in the real world, and they are able to select the appropriate funding channels
  They are able to summarize and communicate the key concepts of their business model in a public context.
  During the tutoring sessions, as well as in the exam, the students must also show that:
  They are able to effectively work in teams
  They are able to take advantage of the advise provided by practitioners and guest speakers.

This course is about how to measure, represent, and communicate information efficiently. It explains why bits have become the universal currency for information exchange and how information theory underpins the design and operation of modern technologies such as smartphones, the Internet, and cloud computing. Information measures are first introduced and then applied to the analysis of the fundamental performance limits achievable in data compression and communication over noisy channels.
The goal of the course is to teach students how the concepts of source coding and channel capacity are used to model, analyze, and design modern communication systems in order to enable efficient information processing. Since these concepts constitute a fundamental foundation of virtually every digital communication system, another objective is to establish concrete links between information theoretic principles and advanced communication technologies. To this end, students will have the opportunity to work on projects that apply the concepts and methodologies learned during the course.
In addition to the foundations of classical information theory, the course provides an introduction to Quantum Information Theory. Whereas classical information theory is based on the representation, processing, and transmission of information using classical bits, quantum information theory considers information encoded in quantum bits, or qubits, which obey the principles of quantum mechanics. This part of the course introduces the fundamental concepts of quantum information and highlights the similarities and differences between the classical and quantum frameworks.
The course also includes project activities carried out in collaboration with industrial partners. Depending on the assigned topic, students will address either classical information theory problems, including source coding and channel coding, or introductory applications of quantum information theory. These projects are designed to connect the theoretical concepts presented during the course with practical engineering problems arising in industrial applications.

1 - Knowledge and understanding
Lectures and exercise sessions will allow students to:

• know how to measure information quantitatively
• know theoretical and methodological skills related to the concept of classical and quantum information
• understand properties of entropy, mutual information as applied them to information processing
  2 - Applying knowledge and understaning
  After the end of the course, students should be able to:
• understand and solve complex problems in different interdisciplinary areas
• integrate and individually find and re-adapt known solutions to growing problems
• analyse uncertainty in terms of information entropy and other information measures
• summarize and present the results achieved during the analysis and specification activities
• to communicate the results of their activity in a clear and convincing way
  3 - Making judgements
  After the end of the course, students should be able to:
• define mathematical models by considering the informational aspects of a system and the related measures
• evaluate concrete schemes for compression and communication

1 – Introduction to Information Theory
Historical background. Introduction to information theory and its applications.
2 - Entropy and Source Coding
Entropy of a memoryless source. Coding of memoryless sources. Prefix codes. Kraft inequality. Huffman codes and Shannon codes. Source coding theorems for memoryless sources. Joint entropy and conditional entropy. Chain rules. Entropy of sources with memory. Source coding theorem for memory sources. Practical source coding methods. Universal coding. Arithmetic coding.
3 - Channel Capacity
Channel models. Discrete memoryless channels. Mutual information. Relative entropy. Data processing inequality. Channel capacity. Reliable communication over discrete memoryless channels. Entropy, mutual information, and channel capacity for continuous channels. Additive white Gaussian noise (AWGN) channel. Channel coding theorem. Error exponent. Fano's inequality.
4 – From Classical to Quantum Information Theory
Motivation for quantum information theory. Quantum states. Qubits. Bra–ket notation. Unitary evolution. Quantum gates. Quantum measurement and the Born rule. Density operators. Pure and mixed states. Composite quantum systems. Entanglement. Bell states. No-cloning theorem. Quantum entropy. Properties of quantum entropy. Quantum mutual information. Coherent information.
5 – Project on Selected Topics
A voluntary project laboratory will be assigned during the semester. All projects will be related to the course topics covered in Sections 1–4 and will be carried out either in collaboration with industrial partners, who will propose engineering problems, or on topics proposed by the students and approved by the course instructors. Depending on the selected project, students will address classical information theory, including source and channel coding, or introductory topics in quantum information theory.
Projects will be carried out in groups of two or three students during the second half of the semester. Their objective is to apply the concepts learned in the course to realistic engineering problems. The evaluation will be based on the produced artifacts, such as documentation, software, or experimental results, together with a final presentation. Technical discussions with the course instructors and researchers from the participating companies will complement the project activities.

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
The written exam evaluates Knowledge and understanding (DD1) and Applying knowledge and understanding (DD2) through numerical exercises and open questions on the topics addressed in the course. It can assign up to 23 points, which form the basis for the final mark before the oral examination.
The oral examination evaluates Making judgements (DD3), Communication skills (DD4), and Learning skills (DD5). It can assign up to 10 points.
Students may choose to complete a voluntary project in place of the oral examination. The project evaluates Applying knowledge and understanding (DD2), Making judgements (DD3), and Communication skills (DD4) in the context of an applied study. It can assign up to 10 points.
The mark of 30 cum laude is awarded when the total score after the project presentation reaches 33.

Lecture notes https://webeep.polimi.it/course/view.php?id=14728
Luca Barletta, Gianluigi Liva, Maurizio Magarini, Lecture Notes on Channel Coding https://webeep.polimi.it
Note: Supplemental Material for the Courses Channel Codes for of Iterative Decoding (TUM) and Information Theory (PoliMi).
T. M. Cover, J. A. Thomas, Elements of information theory (1st or 2nd edition), Editore: John Wiley & Sons, 1991 (1st ed.), 2006 (2nd ed.). First edition available online for PoliMi students (http://onlinelibrary.wiley.com/book/10.1002/0471200611)
Sandro Bellini, Information Theory https://webeep.polimi.it/mod/folder/view.php?id=337337
D. J. C. MacKay, Information Theory, Inference, and Learning Algorithms, Editore: D. J. Cambridge University Press, 2003
R. G. Gallager, Information Theory and Reliable Communication, Editore: John Wiley & Sons, 1968

The "Laboratory of Internet of Medical Things" course is designed to equip students with the interdisciplinary skills required to develop, implement, and validate connected medical devices. The course is designed for students from all backgrounds that are related to Information Technology, in particular Biomedical Engineering, Telecommunications Engineering, Electrical and Electronics Engineering, Computer Sciences, and Automation and Control Engineering, but is also open to other backgrounds.
A key objective of the course is to enable students to design and prototype a functional medical device starting from commercial microcontrollers. This involves interfacing these microcontrollers with analog and digital sensors commonly employed in medical applications, creating circuit schematics, developing printed circuit board layouts, and designing enclosures for the devices. By mastering these skills, students will be prepared to tackle the hardware challenges inherent in IoMT device development.
In addition to hardware design, the course emphasizes the importance of sensor data acquisition and basic on-board processing. Students will learn to capture data from various sensors and perform basic processing at the firmware level. This foundation is essential for ensuring reliable data collection in real-world applications.
Wireless connectivity is another critical component of the course, where students will explore wireless IoT communication protocols. Understanding these technologies will enable students to effectively transmit sensor data to external devices like smartphones and laptops, facilitating real-time monitoring and data analysis.
The course also covers the development of applications for receiving, displaying, and storing sensor data. Students will gain hands-on experience in creating mobile or desktop applications that integrate with cloud services, providing solutions for data visualization and storage.
As the course deals specifically with IoMT, so with medical applications, students will design and implement basic validation protocols, using appropriate gold standard instruments for comparison. This practical approach to validation will allow the students to comprehend and fully appreciate the challenges and complexity of validation of devices when the final goal is a clinical-grade application.
Additionally, the course fosters transversal and soft skills. Students will work in groups to manage their projects, prepare technical reports, and present their findings, thereby honing their ability to communicate complex technical information effectively.
By the end of the course, students will have a thorough understanding of the theoretical and practical aspects of end-to-end IoMT devices development.

The expected learning outcomes are the following:
Hardware design and prototyping: the students should be able to design a simple device using a commercial microcontroller, interface the microcontroller with analog and/or digital sensors commonly used in medical applications, design a schematic of the circuit, create a printed circuit board layout and develop a case to host the device (DD1, DD2).
Sensor data acquisition and on-board processing: the students should be able to acquire data from sensors and implement a basic processing at the firmware level (DD1, DD2).
Wireless connectivity: the students should be able to implement wireless IoT communication protocols (e.g., Bluetooth/BLE, Wi-Fi) to transmit the acquired data to an external device; the student should be able to configure and deploy message application protocols for the IoT (e.g., MQTT) (DD1, DD2).
App development and data storage: the students should be able to design and develop an application (mobile or desktop) to receive and display sensor data in real-time and save such data locally. The app should also include features to visualize the history of the collected data (DD1, DD2).
Validation protocol: the students should be able to design and carry out a basic validation protocol of the biomedical signal or measurement acquired with the device they have developed. The students must choose an appropriate gold standard instrument for the validation protocol (DD1, DD2).
Presentations to the rest of the class: the students should be able to prepare intermediate project presentations for the lectures and their peers during which they explain in detail what they have done during the practical project and the relevant background/state of the art (DD4, DD5).
Project management and collaboration: the students should be able to develop project management skills to design, implement, and test the IoMT devices and applications (DD2, DD3, DD4).
Documentation and reporting: the students should be able to document the design, development, and testing processes clearly and concisely, preparing both a written technical report and an oral presentation to communicate project outcomes effectively to the class (DD3, DD4).

The topics of the classroom-taught lectures and guided practical activities will be the following:
Introduction to the course
Hardware + printed circuit boards tutorial
Laboratory instrumentation + practical session
Wireless protocols and Internetworking + BLE tutorial
Firmware
The rest of the activities is focused on the development of the practical group project, which consists in developing a connected IoMT device from all aspects (hardware, firmware, data acquisition, data storage, processing), according to what is described in the “Expected Learning Outcomes” section, and specifically:
Hardware design and prototyping
Sensor data acquisition and on-board processing
Wireless connectivity of IoMT devices
App development and data storage
Design of a validation protocol

Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
The students will be divided into groups and the evaluation of the lecturers will be based on the following activities:
Group project, which will be presented orally by the group in one of the exam calls and reported in written form (the template is provided at the beginning of the course). The project is assigned at the beginning of the course based on expressed preferences within some options provided by the lecturers.
Intermediate presentations, which will be presented orally by the group to the rest of the class. The topic of the presentations is an update on the project status.
Additional oral exam upon request of the lecturer.

J.G. Webster, A.J. Nimunkar, Medical instrumentation: application and design, Editore: Wiley, Anno edizione: 2020
R. Melen, V. Trecordi, IoT Networking, Editore: Pearson, Anno edizione: 2023

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/

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

METODI ED APPLICAZIONI DI INTELLIGENZA ARTIFICIALE IN BIOMEDICINA [C.I.]
Un'introduzione all'intelligenza artificiale in medicina. Glossario e definizioni. Tipi di dati e paradigmi di intelligenza artificiale per applicazioni biomediche. Fonti di conoscenza, data mining, qualità dei dati e pre-elaborazione. La particolarità dei dati medici: accessibilità, dati distribuiti, effetti batch e fattori confondenti in studi multicentrici. La teoria del processo decisionale in medicina: utilità, incertezza e explainable AI. Sistemi di supporto alla decisione per la biomedicina. Strumenti per AI: ampia introduzione alle reti neurali (NN), dalle tradizionali reti neurali feedforward (FFNN) e ricorrenti (RNN) ai modelli di deep learning di maggior successo come le reti neurali convoluzionali (CNN) e le memorie a lungo termine (LSTM) ). Combinazioni di classificatori per diagnostica. Fondamenti di Knowledge Engineering per la medicina: ontologie e alberi decisionali. Esempi in: medicina di precisione, elaborazione diagnostica delle immagini e radiomica, classificazioni dei segnali biologici, stadiazione e stratificazione del rischio del paziente, pianificazione chirurgica, selezione della strategia di trattamento per i pazienti oncologici. Gli esempi saranno esplorati attraverso esperienze di laboratorio.

The aim of the course is to introduce the techniques suitable for the design of microwave circuits and components, starting from the basic knowledge on ideal lines and electromagnetics. These techniques are required by the distributed nature of the circuits, usually of physical dimensions larger or much larger than the operational wavelength. Modern planar structures will be considered in particular, focusing on the practical design of passive circuits like matching networks, couplers and dividers, filters, etc.

Lectures and exercise sessions will allow students to analyze and design filters and microwave circuits of passive type (DD1, DD2). Students will learn how to use equivalent circuits (DD1, DD2, DD3) and how to implement into physical planar circuits or waveguide circuits (DD1, DD2); A final project, consisting in a filter design based on some specifications will put in practice the concepts learned in the course. The project is then presented orally (DD4).

Introduction. Circuit components from the electromagnetic point of view, distributed- vs lumped-parameter circuits.
Guiding structures. V/I waves in transmission lines. E/H waves in uniform structures with ideal conductors: TEM, TE/TM and hybrid modes; fundamental and higher order modes (recalls). Transmission lines of planar type: stripline, microstrip, slotline, coplanar waveguide, etc.; quasi-TEM approximation, effective dielectric constant, frequency dependence of line parameters (dispersion), losses.
Microwave junctions. Definition of junction and port: equivalent voltages and currents, normalized waves, reference planes. Matrix description of linear n -port junctions: S matrix, general properties, properties for reciprocal and lossless junctions, change of reference planes, relation to Z / Y matrices. ABCD matrix for cascaded 2-ports. Derivation of the equivalent circuit for n -port junctions and discontinuities by lumped elements and ideal lines.
Microwave circuits. The interconnection of lengths of guiding structures by junctions: modeling of discontinuities, higher-order mode interaction, introduction to CAD software.
Matching netwoks. Narrow and broad band matching: single and double stub, single and multiple quarter-wavelength transformers, frequency response, design examples.
Couplers and dividers. Wilkinson coupler. Directional couplers and hybrids: directivity and isolation, even and odd modes in coupled lines, design examples.
Resonators. Free and forced oscillations, external and inter-resonator couplings, the equivalent circuit of loaded resonators, impedance/admittance inverters.
Filters. The design technique by insertion-loss method: lowpass prototype filter; scaling and frequency mapping for lowpass, bandpass and stopband filters. Design examples (lowpass filters, coupled-lines bandpass filters, etc.)

Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, con presentazione/discussione finale
The final evaluation is preferably based on the design of a microwave filter, both theoretical and assisted by CAD software, to be choosen in agreement with the teacher (DD1, DD2, DD3). The result of the design is then presented orally during the exam (DD4).

David. M. Pozar, Microwave Engineering, 4.th Edition, Editore: John WIley & Sons., Anno edizione: 2011, ISBN: 978-0-470-63155-3
Appunti del corso Webeep
Gian Guido Gentili, Matteo Oldoni, Handbook of Microwave Engineering, Editore: Maggioli, Anno edizione: 2024, ISBN: 9788891675385

The general goal of the course is to enable students to extract (“learn”) from measured data useful information for automation, systems and control applications. The course covers the fundamentals of data-driven learning of dynamical systems, with a focus on black-box system identification. Both linear and non-linear systems are considered. Prediction and forecasting are emphasized in time-series analysis and modelling as important applications of system identification. Each technique is treated both theoretically and practically through application examples. Examples of AI-based variable estimation are presented.

Dublin descriptor DD1, Knowledge and understanding

• Through theoretical lectures and numerical exercises sessions, students will achieve a compalgorithms for adaptive filtering, identification and control, the Kalman filter with various extensions, subspace algorithms for state-space identification, ainformation from a finite set of observations taken from an unknown system.
  DD2, Applying knowledge and understanding
• Students will be able to apply the mentioned methods to practical estimation, identification, prediction
  DD3, Making judgements
• Thanks to the project activity, the student will learn to formulate and solve autonomously a practical identification problem,
  DD5, Lifelong learning skills
• Students will be able to apply the methodologies learned throughout the course to tackle complex modeling and estimatread current research papers and understand the issues raised by current research in the field.

The program of the course is as follows.
Discrete-time linear dynamical systems: definition and analysis of model classes for Time Series and Input/Output models (ARMA, ARMAX)
Linear optimal prediction
Black-box identification from measured data-sets of Input/Output models (algorithm, analysis, validation)
Black-box identification from measured data-sets of state-space models
Variable estimation: Kalman Filter and Extended Kalman Filter - Examples of AI-based variable estimation.
Gray-box system-identification
Frequency-domany parametric estimation of models from data
Extension to non-linear dynamical systems AI-techniques

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-aide
In particular, the written exam is designed to assess the following Dublin Descriptors:
DD1 – Knowledge and understanding: students will be asked to demonstrate their understanding of the main problems in data-driven modelinidentification issues.
DD2 – Applying knowledge and understanding: students will be required to select, justify, and apply the most appropriate identification, estim
DD3 – Making judgements: through open-ended questions, students will be evaluated on their ability to critically analyze a given modeling or id
Clarity, rigor of exposition, and proper use of technical terminology will contribute to the final evaluation.

Sergio M. Savaresi, Lecture notes
Sergio Bittanti, Model Identification and Data Analysis, Editore: Wiley, Anno edizione: 2019
M.S. Grewal, A.P. Andrews, Kalman Filtering: Theory and Practice Using MATLAB, Editore: Wiley Online Library, Anno edizione: 2008 http://read.pudn.com/downloads148/ebook/638857/Kalman%20Filtering%20-%20Theory%20and%20Practice%20Using%20MATLAB,%203rd%20Ed.pdf
Note: freely-available online
P. van Overschee, B.L. de Moor, Subspace Identification for Linear Systems: Theory, Implementation, Applications, Editore: Kluwer Academic Publishers ftp://ftp.esat.kuleuven.be/pub/SISTA/ida/reports/96-26a.pdf
L. Ljung, System Identification: theory for the user, Editore: Prentice-Hall
T.S. Soderstrom, P.G. Stoica, System Identification, Editore: Prentice-Hall

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 goal of the course is to provide students the understanding of future work and organizations scenarios and to enable them to design new forms of work and organization that are aligned with the new competitive and social environment, exploiting the opportunities offered by digital technologies. In particular, the course will contribute to the following Learning Goals:
LG2 – Identify future trends, technologies and key methodologies in a specific domain (work and organization design)
LG3 - Design solutions applying a scientific and engineering approach (Analysis, Learning, Reasoning, and Modelling capability deriving from a solid and rigorous multidisciplinary background) to face problems and opportunities in a business and industrial environment
LG5 – Interact in a professional, responsible, effective and constructive way in a working environment, also motivating group members

Lectures and case discussions will allow students to be able to:
understand the different scenarios of the future of work and organizations
analyse the changes in the society and the competitive environment that influence work and organizations
appraise the challenges and opportunities offered by digital technologies in designing work and organizations
appraise the main characteristics of emerging organizational models and the new approaches to organizational innovation
design new forms of work and organization
be an effective leader in the different new organizational scenarios
apply people management practices that are aligned with the new organization models
effectively drive organizational change.

The course covers the main challenges and future scenarios of work and organizations, the main characteristics of emerging organizational models and the new approaches to organizational innovation and change. All the topics are treated both theoretically and through company cases and guest speakers. In addition, group project work or an individual assignment (depending on student's choice) stimulates the in-depth understanding, discussion and application of some of the models presented in class to one or more cases.
The course will cover the following topics:
Emerging challenges for the future of work and organizations: society, competitive environment, new digital technologies
Future scenarios of work and organizations
The Agile Organization:
Agile principles
The Agile organization in practice: tools and methods
People leadership and management in agile organizations
The boundary-less organization:
From outsourcing to crowdsourcing
Platform work and organizations
People leadership and management in boundaryless organizations
Smart, virtual and technology-enabled organization:
Advanced technology adoption/Industry 5.0
Smart and virtual work principles, methods and tools
People leadership and management in smart and virtual work context
The purposeful organization
The role of purpose in new forms of work and organization
Leadership and people management in purposeful organizations
Organizational innovation and change
New approaches to organizational innovation and change
Agile and design thinking principles for organizational innovation

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente) o condizionata (a scelta del docente)
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
The assessment will be based on two components: an assignment and a written exam. The content of the assignment depends on students' choice of how to participate in the course, namely in one of the following two different modalities: through a group work modality, or in an individual modality.
Group work modality (standard):

• During the classes in which new forms of organization and work are introduced, students will be asked to work in teams to have the opportunity to further deepen and better understand from a critical perspective the new forms of work and organization that are presented in class. This form of active engagement with the course content allows students to develop deep insights and offers a good preparation for the written exam.
• The teamwork might differ from one class to the other but generally consists of a team discussion of a real case (either presented by a guest speaker or described in a written case study) and a plenary presentation and discussion by some teams. In preparation of the discussion, teams might be asked to look for information on both academic and grey literature papers or secondary sources before the class.
• Each team will be asked to share and present their teamwork and to act as main discussant several times during the course. Both the presentations and the discussions will be evaluated.
• Active discussion in class by all students and effective teamwork is also envisaged and evaluated through a bonus/malus system.
  Students in the group project work modality can choose to do an additional (optional) oral exam after the written exam, to potentially earn extra points to their final grade.
  Individual modality:
• Students who are not able to participate in the classes and course activities regularly have to accomplish an individual project work at the end of the course
• The individual project work has to be delivered one week before the written exam
  The lecturer can decide to add an obligatory oral exam in case there are questions about the individual assignment. This oral exam needs to be passed in order to be given access to the written exam.
  All students participate in a written exam, during which students should demonstrate:
  to know the most important future scenarios and the main characteristics of new forms of work and organization
  to be able to analyse a specific competitive setting to design the most appropriate form of work and organization
  to choose the correct approach to people leadership and management in the context of the new organization
  to be able to define the appropriate strategy to organizational change and innovation required by the new form of work or organization introduced
  All examination components need to be passed in order to receive a final grade (this applies to both students in the group project and the individual modality).

Numerical methods are ubiquitous in Machine Learning. The aim of this course is twofold:
on the one hand, to systematically address the mathematical and numerical background at
the heart of machine learning, with particular attention to applications in Science and
Engineering; on the other hand, to sustain the theoretical analysis with laboratory sessions.
Extensive hands-on sessions are planned.
The course is part of a pilot program on the use of AI tools in teaching.

Knowledge and understanding (Dublin Descriptor 1)
At the end of the course and after successfully passing the exam, the student will learn how to know, understand and be able to implement numerical methods for machine learning.
Applying knowledge and understanding (Dublin Descriptor 2)
At the end of the course and after successfully passing the exam, the student will be able to:

• Construct and analyze numerical algorithms for Machine Learning.
• Understand and master the theoretical properties of numerical algorithms for solving Machine Learning Problems.
• Write and interpret algorithms;
• Develop implementation skills and numerical verification abilities.
  Autonomy of judgment (Dublin Descriptor 3), Communication skills (Dublin Descriptor 4) and Learning ability (Dublin Descriptor 5).
  At the end of the course and after successfully passing the exam, the student will be able to:
• choose the most appropriate numerical scheme for solving a given problem;
• critically reason and interpret the results;
• model and numerically simulate problems stemming from applications in Science and
  Engineering.

1. Numerical Linear Algebra Tools
Singular value decomposition, principal components and best low rank matrix.
Eigenvalues. Least square methods. QR factorization.
Toeplitz matrices and shift invariant filters. Convolution.
Randomized SVD
2. Automatic differentiation
Wenger list, Direct Acyclic Graphs, Forward mode of AD
Dual numbers
Backward mode of AD
3. Numerical Optimization and Training of Neural Networks
Loss functions.
Stochastic gradient methods, accelerated gradient methods, coordinate descent methods.
Second order methods: Newton and Inexact Newton Methods.
4. Approximation properties of Neural Networks
Stone-Weierstrass theorem and universality results for shallow neural networks
Approximation theory of deep neural networks: error bounds, optimality
properties and regularity classes (e.g, Sobolev, Besov).
5. Sparse hints to Artificial Intelligence/Machine Learning applications.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
The exam consists of a written test, an oral colloquium and a project. The written test takes place in
the computer room and it covers all the theoretical and practical arguments
considered during the lectures and lab sessions. The problems mainly focus on definition,
application of important lemmas and theorems, and important examples. Light calculations
may be needed. The exam is open book. The maximum grade of the written test is
28/30. To get a grade > 28, students are required to take an oral exam. The assessment of the
oral exam will be based on subject knowledge matters and quality of the oral delivery.
Exam (written and oral). It covers:

• The student's ability to apply the subject-specific knowledge and techniques acquired during the course (Descriptors 1 and 2).
• The student's ability to choose the most appropriate numerical algorithms and critically interpret the results (Descriptors 2 and 3).
  Project. It covers:
• The students' ability to integrate knowledge for problem-solving, collaborate effectively in teams, and communicate results (Descriptors 3, 4, and 5).

G. Strang, Linear Algebra and Learning from Data, Editore: Wellesley-Cambridge Press, Anno edizione: 2019, ISBN: 978-0692196380
J. Nocedal, S. J. Wright, Numerical Optimization, Editore: Springer Nature, Anno edizione: 2006, ISBN: 978-0387303031
A. Griewank, A. Walther, Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, Editore: SIAM, Anno edizione: 2008, ISBN: 978-0898716597
I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, Editore: Mit Press, Anno edizione: 2017, ISBN: 978-0262035613 http://www.deeplearningbook.org/
O. Calin, Deep Learning Architectures: A Mathematical Approach, Editore: Springer Nature, Anno edizione: 2020, ISBN: 978-3-030-36723-7

Discretizzazione di problemi ai limiti: formulazione debole dell'equazione di Poisson; il metodo di Galerkin; approssimazione con elementi finiti lineari a tratti e continui e con differenze finite centrate. Modelli di trasporto di base e metodi per la simulazione numerica: il modello di deriva-diffusione (DD); tecniche di iterazione funzionale; approssimazione con elementi finiti dell'equazione non lineare di Poisson e dell'equazione di continuita; cenni al metodo dei volumi finiti e al modello a bilancio d'energia. Modelli di trasporto avanzati e metodi per la simulazione numerica: il modello idrodinamico e sua discretizzazione con differenze finite upwind; il sistema delle equazioni di Schroedinger-Poisson e il loro accoppiamento con il modello DD per la descrizione del trasporto in dispositivi nanometrici. Cenni al metodo Montecarlo.

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)

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

Photonics is emerging in various areas of communications, electronics and sensors to support the need for transparency, speed, and ability to handle huge data streams and multiplexing, as request from the evolution of communication systems today. Integrated photonics devices are penetrating many fields other than communication and optical interconnects: optical signal processing for 5G, lidar, automotive, exascale computers, biosensing, imaging and much more. The course examines in detail the integrated optics sector, focusing on theoretical, technological and applicative aspects. We discuss various issues related to the perspective of photonics technologies, the actual and future markets and potentialities, the generic foundry scheme and, in detail, down to photonics devices: waveguides, passive devices such as filters and (de)multiplexers, modulators, integrated lasers and photodetectors, amplifiers and technologies and materials. When communications occur on very small scales as inside electronics chip, chip-to-chip or board-to-board then it comes to optical interconnects, an emerging field that will be the lifeblood of the future high speed electronic. An introduction to the emerging field of programmable photonics is given, the course is an excellent complement of the 5cr course "Photonic Computing".

Dublin Descriptors
Expected learning outcomes
1 - Knowledge and understanding
Students will learn how to:
Understand mathematical and physical models for the analysis of photonics devices
Define and use models for the synthesis of photonics devices
Learn technologies related to integrated photonic devices
Identify the required components and their use in practical systems
2 - Applying knowledge and understanding
Students will be able to:
Analyze and design most of the integrated photonic building blocks
Understand the physical processes behind a mathematical description
Determine the photonics devices to build a photonic system application
Acquire a professional language to interact with various levels (technology, components, systems)
3 - Making judgements
Students will be able to:
Identify the most suitable technology for a given photonic application
Identify the technological limits and the physical limits in photonics
Recognize the design space and its degrees of freedom that can be exploited to define new applications
Have a perception on the roadmap of integrated photonics in the future
Lifelong learning skills: be aware of a Key Enabling Technology

In particular, the course covers:
The perspective of photonics integration. Integrated optic for optical communications, sensors and optical interconnects (electronics). The generic foundry scheme in photonics. Technological platforms: glass on silicon, Lithium Niobate, Indium Phosphide, Silicon Photonics. The ubiquitous market of photonics devices. Potentialities and limits.
Photonic devices: Integrated-optic components. The propagation in planar guided-wave structures. Guided, radiative and leaky modes. The coupled mode theory. Bent waveguides, directional couplers and Y. Star couplers. Filters, (de)multiplexer and AWG. Switch. Integrated-optic modulators: phase and amplitude, novel modulation formats. Travelling wave electrodes. Grating: uniform, apodized and chirped. Fiber Bragg gratings. Isolator and circulator. Integrated Lasers and Phototodetectors.
Materials and technologies: Materials for integrated optic circuits. Electro-optical and magneto optical effects. Technologies for integrated optic circuits: silica and silicon nitride, Lithium Niobate, Indium Phosphide and Silicon Photonics. The photorefractive effect and fiber Bragg gratings. Technological processes for the realization of passive components. Layer deposition, photolitography and etching. The packaging issue. The CleanRoom. The generic foundry approach.
Photonic circuits: The circuits for the photonic networks and switching. Components for Wavelength Division Networks. Filters. The wavelength routers. Switching fabric for optical signals. Add-drop and cross connects. Principles of optical signal processing. Components and circuits for EDFA. Gain and noise figure. Principles of photonic computing.
Applications: Application in communication and electronics. Examples of microwave photonics. Optical beam forming networks.The optical interconnect problem. Analysis of the electrical interconnect and the optical alternative. Limits, advantages, constraints. From short range rack to rack to intra-chip links. Integration technologies for combining optoelectronics, optics, and electronic integrated circuits: Silicon Photonics and Indium phosphide. Monolithic vs hybrid solutions. CMOS compatible solutions.
Examples of data-intensive on-chip communication are considered and discussed. The control and calibration layer: how to manage a Photonic Integrated Circuit. Programmable photonics. Applications scenario. Circuits for Free Space Optical communication.

Prova orale obbligatoria
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
Oral discussion on the entire program.
Optional tasks assigned during the course. Selected tasks are presented by the students to the class in short presentations.
Periodic optional Socrative tests.

On line resources: www.polimi.it
Note: Updated material in Beep
Lecture notes, slides, additional material and software available on line.
Lecture notes in italian and english
Beep website Photonic Devices www.polimi.it
Note: Log in your Beep account

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

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

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 ‘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

Aim of the course is to give to the students the knowledge of the principles at the basis of Quantum Communications. The applications of these principles to some relevant examples of quantum communication protocols, like quantum key distribution for secure transmission of information, also exploiting the quantum resource of entanglement (as in quantum teleportation and in quantum repeaters), will be presented.

Dublin descriptor DD1 (knowledge and understanding). Students will learn the basic quantum principles for the understanding of the applications of quantum communications.
Dublin descriptor DD2 (applying knowledge and understanding). Students will be able to analyze and describe quantum circuits for manipulating qubits as combination of quantum gates and representing them by means of the Bloch sphere. Students will acquire the capability of implementing the main quantum communication protocols and to calculate the performance of quantum key distribution systems.
Dublin descriptor DD5 (lifelong learning skills). Students will acquire the tools for comprehending the main aspects of present and future applicative developments in the field of quantum communications.

Theoretical bases: Quantum states, observables and quantum measurement. Uncertainty principle. Complementary observables. Quantum bits (qubits). Dirac notation. Single-qubit quantum gates. Pauli operators. Measurement of qubits. Bloch sphere representation. Pure and mixed states. Density operator. Von Neumann entropy. Entanglement of qubits. Partial trace. Bell states. Bell inequality. Multiple-qubit gates. CNOT gate. No-cloning theorem. Accessible information and Holevo bound.
Quantum communications protocols: Quantum key distribution (QKD). BB84 protocol. Quantum bit error rate (QBER), secure key rate and security threshold. Trusted nodes. Examples of physical implementation of QKD. Photon-number splitting attack and QKD with decoy states. Entanglement-based QKD. E91 and BBM92 protocols. Superdense coding. Quantum teleportation. Entanglement swapping. Entanglement distillation. Entanglement distribution. Quantum repeaters.

Prova scritta obbligatoria, senza prove in itinere
The examen is written, with open questions and exercises on the topics explained during the lectures. It is possible to use at the examen only a standard calculator and a printed formulario. In detail the examen comprises:

• exercises to be solved numerically, in order to assess the expected learning outcomes related to Dublin descriptors DD1 and DD2;
• questions of theoretical type with open response, in order to assess the expected learning outcomes related to Dublin descriptors DD1, DD2 and DD5.
  The maximum score of the examen is 33 and the grade of 30 cum laude is obtained when the score is at least 32.

Reference notes for theory/examples/exercises presented on the blackboard during the lessons https://webeep.polimi.it/login/index.php
M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information, Editore: Cambridge University Press, Anno edizione: 2000

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

The course provides the theoretical foundations and practical skills required to understand electromagnetic (EM) sensing, with a particular focus on radar imaging and its environmental, industrial, and commercial applications.
The course covers the complete radar imaging chain, from sensor technologies and antenna arrays to image formation, signal processing, calibration, and integration with Geographic Information Systems (GIS). The fundamentals of wave propagation, scattering, radiometry, and optical sensing are introduced and compared with those of Radar. Advanced radar concepts, including Synthetic Aperture Radar (SAR), multistatic radar, and Multiple-Input Multiple-Output (MIMO) systems, are presented together with interferometric imaging techniques.
Through lessons, but also dedicated laboratory sessions, students learn - and practice - how radar images are generated, processed, and interpreted, and how to exploit radar-specific information such as moving target detection, millimetric deformation measurements, surface roughness and moisture estimation, and imaging through optically opaque media.
Applications include geohazard monitoring, climate and environmental observation, infrastructure monitoring, automotive sensing, and health monitoring from space. Laboratory activities provide hands-on experience, including processing and analysis of real data. Through project-based activities, students develop practical skills in radar signal processing, image interpretation, and problem solving. Guest seminars from industry professionals may complement the course.

Dublin Descriptors
Expected learning outcomes
1 - Knowledge and understanding
Students will gain clear understanding about:
the physics behind electromagnetic remote sensing technologies
sensors principles, with attention to distributed sensors and sensors arrays
signal processing methods for the treatment of electromagnetic remote sensing data
specific properties of Radar, Lidar, Optical images, and the possible applications
applications based on cohernt images, interferometry and tomography
2 - Applying knowledge and understanding
Students will be able to:
Design a Radar and remote sensing survey
Define a data processing flow chart for processing Radar data.
Simulate electromagnetic acquisitions and process them using Matlab
Get remote sensing data from image providers/space agencies, integrate and process in a GIS
3 - Making judgements
Students will be able to:
Identify qualitatively and quantitatively pros and cons of different remote sensing methods and products serving their professional activities
Understand the principles that govern the design of Radar remote sensing systems
Define a data processing flow chart for processing Radar data.
Recognize the design space and its degrees of freedom that can be exploited to define new technologies
4 - Communication
Students will learn to:
Write a technical document on a specific case study (e.g.: design and implementation of a remote sensing survey, algorithm development, system analysis, etc.)
5-Lifelong learning skills
Students will learn to:
Manage a project, starting from definition of specfics, to solution development, validation and verification

Remote sensing principles, methods and applications: from IR to visible. How electromagnetic sensors observe the Earth, from optical to microwave systems. Fundamentals of image formation, radiometric measurements, calibration, and georeferencing. Overview of current Earth observation missions and applications..
Coherent imaging. Principles of coherent sensing, including wave propagation, polarimetry, speckle, coherence, and radiometric resolution. Lidar, Radar and Sonar coherent imaging. Real and synthetic antenna arrays, beamforming, and image formation, with applications to high-resolution imaging and change detection
RADAR basics. Principles of Doppler and pulsed radar, ranging, waveform design, mono and bistatic configurations, radar cross section, and the radar equation. Radar SAR: image interpretation, and the principles underlying the design of radar imaging solutions for specific applications.
Advanced radar systems and applications: Interferometric, tomographic, polarimetric, and coherent change detection techniques. Applications to digital elevation model generation, millimetric deformation monitoring, infrastructure health assessment, geohazard and environmental monitoring, and volumetric imaging. MIMO radar, 3D imaging, and super-resolution methods for industrial and automotive sensing.

Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The exam is oral and assesses the students' understanding of the fundamentals of radar and electromagnetic imaging.
Part of the course is dedicated to an application-oriented project, carried out individually or in small groups. AI-based tools may be used to support the project.
The oral examination may include a discussion of the project to assess the students' understanding of the underlying principles ( DD1 ), their ability to interpret results and relate the proposed solution to real-world engineering applications ( DD2 ), their capability to design innovative remote sensing systems and solutions for specific applications ( DD3 ), and their critical evaluation of the proposed approach with respect to alternative solutions ( DD4 ). The examination also assesses the students' communication skills in presenting and discussing their project ( DD5 ).
During the course, interactive quiz sessions are used to help students assess their progress, reinforce key concepts, and receive immediate feedback on their learning.

Andrea Monti-Guarnieri, Electromagnetic Imaging, Anno edizione: 2015 https://polimi365-my.sharepoint.com/:b:/g/personal/10111669_polimi_it/EQjmg-9bGbNIlTWAkCjIUWgBq9loKAyqHNuN7xH90AFpQw?e=cpadXT
A. Ferretti, A. Monti Guarnieri, C. Prati, F. Rocca, D. Massonnet, InSAR Principles: Guidelines for SAR Interferometry Processing and Interpretation, Editore: ESA TM-19

Questo corso mira a fornire agli studenti le necessarie competenze per valutare l'affidabilità sia a livello dispositivo (elettrico/elettronico e non) che di sistemi complessi utilizzando sia gli strumenti statistici e probabilistici che le moderne procedure di analisi delle informazioni. In particolare, vengono presentate le tecniche relative all'analisi dell'affidabilità dei sistemi e vengono discusse anche le metodologie per il miglioramento dell'affidabilità. Le tecniche di affidabilità, necessarie per implementare le strategie di monitoraggio e prognostica, sono tra i fattori abilitanti strategici definiti dal paradigma Industry 4.0. Per questo motivo, questo argomento è considerato strategico nel processo di sviluppo di tecnologie intelligenti. Per alcuni argomenti del corso verranno adottati alcuni approcci innovativi di insegnamento [IT] (ad es. flipped classroom and blended learning). Tali argomenti sono indicati con l'acronimo IT nel programma.

The course presents the main phenomena, system architectures and components in a radio frequency system.
Satellites, radars, mobile phones and in general any communication system operating at radio frequencies (from MHz up to hundreds of GHz) need a dedicated frontend which handles the baseband signals and their conversion from/to the required carrier frequency, involving amplification, monitoring, mixing... up to the antenna. All such operations are carried out by components and subsystems, whose design along the transmission and reception chain are described in the course.
Specific phenomena addressed during the course include: thermal noise, non-linear distortion (compression, intermodulation, harmonic mixing, out-of-band and in-band components...), power transfer, mismatch, efficiency, power consumption, interference...
The course also provides the basic elements of a wireless radio link budget to balance transmit power, antenna type, modulation, data rate, sensitivity to cover a specific distance. Several digital, mixed and analog architectures are presented.
Specific subsystems and components are also discussed with guidelines for their design: practical transmission lines and cables, amplifiers, oscillators, couplers, dividers...
The course brings together and touches several other neghboring topics, not prerequisite, such as modulation, error correction, antennas, radio propagation, electronic circuits, microwave engineering, taught in other courses, and provides a cohesive view of how each aspect plays a role in achieving a real radio communication.
Techniques for modelling, analysis and design are provided at system and component level, leveraging theoretical foundations and engineering rules along with professional computer softwares for system and device simulation.
Interactive teaching will be used to foster attendance and keeping a steady study pace.

At the end of the course, students will:
Knowledge and understanding (DD1):

• know the name, purpose, parameters and equivalent simplified models of the main blocks, subsystems and components in a radio frequency frontend system
• know their interactions and phenomena in terms of performance and energy efficiency at system-level
• know the design flow and tradeoffs in a radio frequency system and the basic design techniques for the main components
  Applying Knowledge and Understanding (DD2)
• apply the principles of thermal noise and non-linear modelling and radio frequency circuits to design a transmitting/receiving frontend at radio frequencies and assess specifications, capabilities, efficiency and fundamental limitations/tradeoffs
• apply the principles of impedance transformations to design matching or transformation networks for amplifiers and other microwave components
• apply the concepts of system-level design to commercial CAD-EDA software packages for microwave design or to autonomosly-developed softwares
  Judgement and Critical Thinking (DD3)
• design specific radiofrequency blocks (e.g. single and multi-stage amplifiers) using the available transistor parameters and designing interconnections with lumped or distributed elements leveraging the available degrees of freedom and taking autonomous reasonable tradeoffs and design decisions
• gain practical judgment capabilities of reasonable requirements and specifications for the blocks in a radio frequency system

Specific topics (1st part):
Introduction to microwave communication systems. Fundamentals of frequency conversion: the super heterodyne radio receiver. Block diagram of the RF front-end in receivers and transmitters. Basic blocks: Antenna, Amplifier (low noise and power), Mixer, Local Oscillator. Recalls on signals in communication systems: carrier and complex (baseband) modulation.
Introduction to antennas: guided and radiated electromagnetic waves. System aspects of antennas. Fundamental parameters: directivity, radiation pattern, gain, effective area, efficiency, radiation impedance.
The radio link: Friis equation (link budget)
Noise in RF front-end. Definition of the noise temperature. Noise in frequency conversion (SSB and DSB). Evaluation of the equivalent noise temperature of the receiver. Comparison with the direct conversion receiver. Noise generated at antenna output.
Linear and non-linear distortion: definitions and characterization. Transfer function: no- distortion condition for amplitude and group delay. Weak non-linearity in 2-port networks: 1dB compression point and 3th order intercept point.
The analog frontend in the whole communication system: bringing all elements together, from networks and bitstream, to radio waves.
Transceivers architectures: frequency-division and time-division duplexing, orthogonal polarizations, MIMO
System-level simulations with industry-level computer-assisted EDA tools
Specific topics (2nd part):
Matrix characterization of n-port networks. Classical Z and Y matrices and limits of their use in high frequency circuits. Definition of power waves. Scattering matrix: definitions and properties.
Introduction to microwave circuits. Main differences with lumped-element circuits. Conventional voltage and currents. Interconnections and their effects. Discontinuities: meaning and representation. Analysis with CAD techniques.
Impedance matching: basic concepts (matching and maximum power transfer). Matching with lumped elements (L network). Single stub and double stub tuners.
Coupled transmission lines: analysis with the conventional lumped model; even and odd mode parameters. Directional couplers and combiners.
Introduction to the design of narrow band microwave amplifiers. Maximization of transducer gain and stability issues. Evaluation of optimum loads with the Smith Chart. CAD techniques for non-linear analysis of amplifiers: the Harmonic Balance method.
Microwave Oscillators: topological configuration and evaluation of optimum load with the Smith Chart. The dielectric oscillators: scheme and dimensioning criteria.
Overview of semiconductor active devices at microwave frequencies. Basics on fabrication technologies of RF and microwave circuits
Component-level design and simulation with industry-level computer-assisted EDA tools
Industry seminars
Interactive exercises and hands-on experience

Prova scritta obbligatoria, con 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
Valutazione continua facoltativa (a scelta dello studente)
The final grade is given from 0 to 30 e Lode and must be at least 18 points to be sufficient. The score is made by:

• up to 26 points: written test. Either as midterm test+endterm test or as full exam (DD1, DD2). An oral exam can be required by the teacher based on the result of the written test.
• up to 3 additional points: [optional] homework (one intermediate homework in October [flipped classroom, participative teaching, DD2, DD3]; one project at home after end of course [project teaching, DD2, DD3], to be turned in before the chosen exam session)
• up to 4 additional points: [optional] interactive classroom activities (2 interactive graded exercise sessions [graded/self-assessment teaching, DD3]; physical attendance to industry seminars (DD1))
  The additional points will be added to the written test only if the written test is >= 15.
  The written test can be taken either as midterm (13 points, usually in November) + endterm (13 points, only at the first exam session in January), and both parts must be >=7.
  Alternatively, the written test can be taken as full exam, covering the full program of the course, giving up to 26 points. The full exam can be taken in any of the 5 sessions (from the first exam session in January to the last session August-September of next year).
  The written tests are based on numerical exercises and problems similar to those discussed during the course. The purpose of the exam is to assess the student's ability to solve problems and design RF systems and microwave circuits. During the tests students can use books, notes and their own computer (without Internet connection). A laptop is indeed necessary for the exercises of the second part, and thus is needed for the endterm and any of the full exams.

Michel Steer, Microwave and RF Design, A System Approach, Editore: Scitech
David M. Pozar, Microwave Engineering, Editore: John Wiley & Sons
Slides by the teacher
Note: Published and updated on WeBeep of the course
Recorded classes
Note: These will be made available on Webeep before the midterm exam and at the end of the lectures.

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

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/

Statistical signal processing provides the foundation for the informed development and critical use of many AI systems. Time series analysis is needed in several interdisciplinary areas of engineering such as audio/video, digital communications, finance, mechanical vibration analysis, imaging and remote sensing, GPS navigation systems, machine learning aiged problem solving, just to mention some.
The teaching activity aims to balance the fundamentals of discrete-time signals and the estimation theory with a set of engineering use-cases. Goal is to strength the capability of student to have a pragmatic, but still rigorous, way to solve stochastic signal related problems. More specifically, students will learn to approach an engineering problem that involves stochastic signals, formalize it into a mathematical model where all the unknowns are stochastic and/or deterministic variables, and find the most appropriate solving method always validated by the knowledge of the statistical bounds in idealistic connditions. The theory on statistical signal processing (60hours) are interleaved with numerical exercising (30hours) on simple settings to consolidate the concepts.
Labs and practical problems are first discussed in class providing real data, then after few days students can prepare for solving the assigned tasks by using Matlab in collaborative Labs where students are randomly grouped in 3-4 to compare and improve their problem-solving capability under the supervision of a tutor. The grouping within the same class enables students to experience intra/inter-group dynamics with other unknown and randomly chosen students, this small-scale simulation of their professional life aims to better refine their mutual professional relationships.
Part of the laboratory activity is in cooperation with industry-teams dedicated to one topic, to be defined during the semester (past was Google data analytics team)

Lectures and exercise sessions will allow the student to (DdD1):
Consolidate the theoretical tools to handle with finite-length single/multiple signals
Have solid knowledge of the estimation theory for statistical signal processing
Know what method to choose depending on statistical properties of stochastic part.
Handle with adaptive time-varying systems and design Bayesian parameters tracking
Introduction to spectral analysis
2D signals and properties
Handle multiple signals from arrays, and extract the relevant parameters
Extract a pattern from data in supervised/unsupervised forms
Design the Matlab flowchart for some essential kernels such as estimation of frequency and delays, extraction signals from noise by filtering and by convolution/deconvolution matrixes.
The project laboratory part of innovative-teaching will allow the student to (DdD2):
Analyze the requirements and write the algorithm flow
Write Matlab-code that solves the required problem
Evaluate multiple solutions for the same problem and develop a criticism.
Write a short document structured as a technical report
Work in team with other students on the same working-group and learn to interact each-other
Overall the teaching will give the student at the end of the course and after passing the exam the capability to design estimators and processing methods for limited-length data, to evaluate the reliability and accuracy of the algorithms, and tools for manipulating and classifying multiple-dimensions discrete signals. At the end of the course and after passing the exam, the student will be able to solve problems involving stochastic stationary or time-varying signals, and to evaluate the cost/benefit tradeoff with a deep knowledge of the corresponding statistical bounds. The laboratory activity in the classification and prediction is in cooperation with Google within the framework Google adopts ADSP course
Outcomes of the students:
Knowledge and understanding: students will learn how to manipulate signals using algebraic tools; apply the estimation theory and bounds to signals related problems; manipulate multidimensional signals for estimation and decision.
Applying knowledge and undestanding: students will be able to define a statistical model for signals' related problems and their solution with fundamental estimation theory methods; solve a broad range of statistical signals' related engineering problems.
Making judgements: students will be able to evaluate the fundamental limits on statistical signal processing applications even in presence of uncomplete/inaccurate conditions; tailor and adapt the statistical methods to complex problems.
Communication: student will learn how to describe a method and sinthetize the limits and results; write a technical report.

The course focuses on statistical signal processing and covers the following topics:
Review of basics: matrix and linear algebra; quadratic and constrained optimization problems.
Introduction to the estimation problem and models: definitions, performance, sufficient statistics, linear and non-linear models.
Estimators: best linear unbiased estimation (BLUE), maximum likelihood estimation (MLE), least squares method. Cramer Rao lower bound.
Bayesian estimators: a-posteriori estimation (MAP, MMSE and LMMSE); Wiener filter; linear prediction and Yule-Walker equations.
Adaptive filters: LMS, RLS methods, convergence analysis and step-size selection.
Bayesian tracking: dynamic model and Kalman filter; examples of positioning.
2D signals properties and physical filters
Array processing and beamforming methods.
Pattern and sequence recognition: Bayesian classification of signals in noise, linear discriminant and support vectors, clustering methods, supervised classification, deep learning methods and updates.
Montecarlo simulation and numerical analysis on some use cases.
Labs and practical problems are assigned during the semester and solutions are discussed within the workshops dedicated to these problems.

Prova scritta obbligatoria, con prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
The exam is in written form with at least one small project complete of the reporting activity and, optionally, the oral exam. More specifically, the written exam lasts 2.5h and it covers the entire theory part of the program with exercises to spot the degree of understanding of the concepts. Exercises are more than necessary to pass the exam, the sum of total marks allotted for all the exercises in written exam usually ranges in 42-45, so each student can choose what exercises to do and how much in depth according to her/his skill. Each exercise is in increasing-difficulty, and marks are ranked accordingly. Marks of the entire written exam saturate to 24. During written students can use the course textbook and for computations, students can use computers only with Matlab.
In parallel, the students have to develop at least one of the 3 projects (called Homeworks, or Hw, within the teaching jargon as they can be freely carried out at home): Hw#1, Hw#2, Hw#3. During the teaching semester, the Hws’ text and data are released to the students so that they can start solving Hws at home while studying theory and exercising. Solving the Hw during the teaching semester is recommended as let the students take benefit of the Project Laboratory as Hws have similar structure except should be completed independently on their own. Hws are of incremental complexity, with increasing marks 1,2,3, so that max Hw marks is 1+2+3=6. Hw#1 is mandatory and there is no restrictions on when to solve the Hws, but students can deliver the Hw-reports only after passing the written exam >17.
For students that wish to have to oral exam, the written exam should be passed with unsaturated mark >27, with at least Hw#1. Oral exam is a discussion that aims to evaluate the maturity and the knowledge of the topics when solving some use cases; during oral the student can use the textbook and notes. Notice that 30Lode is only by Oral exam.
There is an intermediate written exam, typically the end of October, covering half of the program. The second half can be completed in any written exam dates till the end of the winter session of exams. The written exam can be repeated anytime without penalty.

U.Spagnolini, Statistical Signal Processing in Engineering, Anno edizione: 2017, ISBN: 978-1-119-29397-2
Note: Note:
Notes/slides on the book can be downloaded from the folder of the course shared with students during the semester

L'insegnamento tratta argomenti relativi a due aspetti specifici e basilari concernenti l'utilizzo delle discipline ICT nell'ambito dei sistemi di automazione: le reti di comunicazione industriali ed i sistemi a logica programmabile (PLC). Le conoscenze impartite sono fondamentali nella progettazione e nello sviluppo dei sistemi di automazione moderni. L'affermarsi delle reti di comunicazione (Fieldbus e Industrial Ethernet) e di interfacce standard tra i sistemi hanno reso necessario lo sviluppo e l'applicazione di specifici protocolli industriali, trattati a livello teorico ed applicativo (in particolare Controller Area Network, ProfiBus DP, ModBus e CANopen).
1.
PLC e Sistemi di produzione integrati. Architetture dei sistemi per l'automazione. Architettura di fabbrica CIM. Controllori Logici Programmabili (PLC). Linguaggi di programmazione per PLC: Ladder, Sequential Function Chart, Function Block Diagram e Instruction List. Esercitazioni pratiche su calcolatore relative alla programmazione microPLC.
2.
Automazione e comunicazione industriale. Dispositivi per l'automazione negli impianti di produzione industriale. Introduzione ai sistemi di controllo distribuiti ed embedded. Mezzi trasmissivi cablati, modello di riferimento ISO/OSI, Bus di campo con assegnazione decentralizzata e centralizzata, topologie principali delle reti, tecniche di accesso al Bus e tecniche di rilevamento e di correzione degli errori. Reti di comunicazione nell'ambito dell'automazione e problematiche relative allo scambio di informazioni in tempo reale. Reti di campo (Fieldbus) per sistemi embedded: definizione, caratteristiche, funzionamento, analisi, struttura dei Frame, sincronizzazione, gestione errori, latenze, tempi di trasmissione, prestazioni e limiti in riferimento alle soluzioni più diffuse, ad esempio: PROFIBUS DP, CAN, CANopen, MODBUS. Reti Industrial Ethernet: definizione, caratteristiche, utilizzo, struttura Frame, prestazioni; introduzione alle reti Ethernet in riferimento alle soluzioni più diffuse (cenni EtherNet/IP, PROFINET, Ethernet Powerlink).
Analisi progettuale e ottimizzazione di reti industriali in riferimento alle soluzioni più diffuse.

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

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

The course aims at giving students the fundamentals of electronic properties of solids starting from a microscopic quantum approach. The main topics are: phenomenological properties and definition of solid materials; crystals and their symmetry; the Schroedinger equation for a crystal: Born-Oppenheimer and mean-field approximations; electronic states: free electron model, periodic potential and Bloch theorem, band structure, quasi-free electrons, tight binding approximation; accelerated motion of electrons and holes.

This module is part of a course that offers a comprehensive collection of fundamentals tools for for audio and acoustic signal analysis and processing, as well as synthesis and rendering. This specific module focuses on tools for audio analysis and processing.
A wide range of applications will be covered, including time and pitch scaling, sound restoration and improvement, music information retrieval, sound classification, acoustic source localization and tracking, adaptive processing, etc.

The student:

• knows the fundamentals of audio and acoustic processing required for designing and implementing audio processing chains.
• knows the fundamentals of audio signal modeling, psycoacoustics and audio restoration
• knows the basics of statistical audio processing and adaptive linear filtering with applications of Wiener filtering and Linear Predicting Coding.
• implements algorithms for single and multichannel audio processing using MATLAB; knows the main algorithms for sound source localization and separation
• works in small groups and is able to deliver information about algorithm design and the obtained results

Module 1 - Digital audio analysis and processing
In this first part we cover relevan applications of digital audio signal processing for various applications, ranging from audio restoration to adaptive audio processing and array processing. After an introduction to acoustics and psycho-acoustics, we will cover:
Sinusoidal modeling - resolution of periodic signals and windowing, spectral interpolation, sinusoidal peak tracking and continuation. Speech modeling (synthesis through analysis). Channel and phase vocoder design.
Statistical audio processing - Wiener filtering: problem statement, orthogonality principle, Wiener-Hopf eqs., error performance surface. Typical applications of Wiener filtering: echo cancellation, noise cancellation, etc. Linear Prediction Coding: problem statement and application. Kalman filtering: problem statement, Bayesian model. Equalization: typical DSP structures for equalization.
Audio restoration - noise modeling, removal of typical audio artifacts: clicks, crackles, background noise, hissing noise, wow and flutter, nonlinear distortions. Statistical filtering and equalization
Fundamentals of microphone array signal processing - data model, non parametric methods (Delay-and-sum beamforming). Parametric methods (MUSIC, ESPRIT). Multichannel Blind Sound Source Separation (BSS)

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
The student is required to do two homework assignments (which will count for 40% of the final grade) and a final exam (which will count for 60% of the final grade). Students also have the opportunity to improve their grade by developing a (non compulsory) project (necessary condition to aspire to a "cum laude"), which will grant them a grade increment.
The project must be preliminarily negotiated with a course instructor and can be either a software application or a research paper. Projects can be combined for the two modules, and can be done in collaboration with other students. Projects can also be combined with projects of other courses, upon explicit approval of all involved instructors.

Mark Kahrs, Karlheinz Brandenburg, Applications of Digital Signal Processing to Audio and Acoustics, Editore: Springer-Verlag, Anno edizione: 1998, ISBN: 9781475783865
Simon Haykin, ADAPTIVE FILTER THEORY, Editore: Pearson, Anno edizione: 2014, ISBN: 9780273764083
Simon J. Godsill, Peter J. W. Rayner, Digital Audio Restoration: A Statistical Model Based Approach, Editore: Springer-Verlag, Anno edizione: 1998, ISBN: 9783540762225
Petre Stoica, Randolph Moses, Introduction to Spectral Analysis, Editore: Prentice Hall, Anno edizione: 1997, ISBN: 9780132584197

This module is part of a course that offers a comprehensive collection of fundamentals tools for for audio and acoustic signal analysis and processing, as well as synthesis and rendering. This module focuses on tools for audio and acoustic signal synthesis and rendering.
A wide range of applications will be covered, including musical sound synthesis, sound effects, sound reverberation, 3D audio capturing and rendering, etc.

The student will acquire the fundamental tools of audio and acoustic signal processing and will acquire the ability to use such tools for applications of audio and acoustic signal analysis, synthesis, processing, improvement and restoration.

Sound synthesis and spatial processing
In this second part we introduce techniques for sound synthesis and spatialization/reverberation; and for a high-end rendering of audio signals. Using numerical methods and signal processing tools, we discuss traditional techniques for musical sound synthesis such as wavetable, additive, subtractive, granular and nonlinear synthesis techniques; more advanced sound synthesis solutions such as modal synthesis and sound synthesis through physical modelling with particular reference to finite elements, digital waveguide networks, wave digital filters, etc.
We also introduce techniques for digital audio enhancement/improvement such as sound effects, reverberation and spatialization. Finally, we discuss advanced sound rendering techniques based on multi-channel processing and spatial (array) processing such as ambisonics, acoustic beamforming/projection and wavefield synthesis.
Sound synthesis: additive and subtractive synthesis, granular synthesis, wavetable synthesis, nonlinear distortion (ring modulation, phase/frequency modulation), fractal synthesis.
Physical sound modelling for timbral synthesis: modal synthesis and functional transformation method, finite elements, Cordis-Anima, digital waveguides, wave digital structures, object-based modelling.
High-end sound effects and sound processing: modulated digital delay lines, sound effects, reverberation and spatialization algorithms (DSP solutions, geometric methods, physical modelling methods, perceptual techniques), binauralization, head-related transfer function, multi-channel processing, ambisonics.
Space-time processing, clusters or arrays of loudspeakers, 3D audio: vector-based amplitude panning, uniform and non-uniform speaker arrays, beamforming and acoustic projection, polar emission shaping, wavefield synthesis, ambisonics rendering, higher-order loudspeakers.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
The student is required to do two homework assignments (which will count for 40% of the final grade) and a final exam (which will count for 60% of the final grade). Students also have the opportunity to improve their grade by developing a (non compulsory) project (necessary condition to aspire to a "cum laude"), which will grant them a grade increment.
The project must be preliminarily negotiated with a course instructor and can be either a software application or a research paper. Projects can be combined for the two modules, and can be done in collaboration with other students. Projects can also be combined with projects of other courses, upon explicit approval of all involved instructors.

Mark Kahrs, Karlheinz Brandenburg, Applications of Digital Signal Processing to Audio and Acoustics, Editore: SpringerVerlag, Anno edizione: 1998, ISBN: 9781475783865

This course addresses the fundamentals of radiowave propagation, with particular emphasis on wireless and mobile systems. An engineering approach is adopted: excessive formalism is avoided while theoretical instruments are given, so that the student can address real problems starting from a solid background.

After passing the exam:
the student will learn the fundamental physical principles of electromagnetic wave propagation, the fundamental properties and parameters of antennas (as transducers), the effects of the interaction of electromagnetic waves with the environment in a nomadic/mobile scenario (DD1);
the student will be able to identify the most relevant propagation modes for a given scenario, select and apply the correct empirical/statistical and physical/deterministic prediction models, predict system performance (DD2);
the student will be able to identify the most important design parameters of the air-segment of a wireless communication system, choose and apply the most effective methods to counteract propagation impairments (DD2);
the student will be able to plan a PMR radio network using a professional software tool, independently choosing the network configuration and presenting the project in a VIVA (DD3, DD4, DD5).

Introduction
Electromagnetic waves (frequency, wavelength, polarization). Nature of the waves: direct wave, reflected wave, diffracted wave, scattered wave. Multipath radio channel; mean delay and delay spread.
Antennas
Antennas as transducers. Antenna parameters: directivity, gain, directivity function, effective area, effective length, impedance. Equivalent circuit. Link budget in free space (Friis equation). EIRP.
Nomadic systems
Time dispersion; wideband and narrowband channel; LOS (Rice distribution) and NLOS (Rayleigh distribution); space correlation. Fading countermeasures (multiple antennas, frequency hopping, etc.). Applications: civic WiFi networks, digital broadcasting, etc.
Mobile systems
Frequency dispersion; Doppler effect and Doppler shift; correlation in time and frequency; fading countermeasures. Applications: mobile cellular networks, inter-vehicular networks, etc.
Indoor propagation
Reverberating environment; penetration through walls, floor and ceiling made of common indoor materials (marble, glass, concrete, wood, etc.); dependence on frequency; fading countermeasures. Applications: WiFi, domotics, UWB systems, sensor networks, etc.
Models and methods
Optical geometry; empirical propagation models; deterministic models (ray tracing and launching, etc.); radio coverage prediction; numerical tools (examples).

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
Objective of the exam
The objective of the exam is to assess the student's ability to:

• identify the most relevant propagation mode(s) for the given scenario (DD1, DD2),
• quantitatively describe the interaction of electromagnetic rays with the environment (DD1, DD2),
• quantitatively evaluate the effects of the interaction of direct, reflected and/or diffracted electromagnetic rays (DD1, DD2),
• quantitatively assess the impact of countermeasures on system's performance (DD1, DD2),
• use a professional software tool for radio planning and discuss the project in a VIVA (DD3, DD4, DD5).
  Assessment Procedure
  The assessment will be based on a written exam (DD1, DD2) and the radio coverage planning project (DD3, DD4, DD5), both mandatory.
  Written exam (worth up to 26 points) (DD1, DD2)
  The written exam consists of three numerical exercises that must be solved in two hours. Each exercise is worth 9 points, independently from its complexity. The best strategy is therefore to start solving the simplest exercise, moving afterwards to more and more complex ones.
  The written exam is "open book", i.e. during the exam the student can freely consult textbooks, notes, etc. Programmable scientific calculators can be used; the use of smartphones, tablets, notebooks and laptops is not allowed. Copies of past exams texts can be found on BeeP.
  Project (worth up to 5 points) (DD3, DD4, DD5)
  The project consists of a radio planning exercise, carried out using a professional software. Projects are assigned to groups of 3 students that will have to plan a radio network according to the requirements, prepare a short report and present the project in an oral discussion (viva).

J. D. Parsons, The Mobile Radio Propagation Channel (2nd Edition), Editore: Wiley, Anno edizione: 2000
C.A. Levis, J.T. Johnson, F.L. Teixeira, Radiowave Propagation, Editore: Wiley, Anno edizione: 2010
A. Paraboni, M. D'Amico, Radiopropagazione, Editore: McGraw-Hill, Anno edizione: 2002
Note: In italiano - in Italian