Electronics 2
A.Y. 2024/2025
Learning objectives
The lessons will introduce students to the analysis of both deterministic and stochastic signals (noise), to sampling and data conversion techniques (A/D and D/A), and to digital filtering.
Expected learning outcomes
At the end of the teaching semester, the student will know:
1. the analysis techniques for stochastic processe and the mathematical models for noise in electronic devices;
2. the sampling process, the aliasing effect and the relationship between sampling frequency and signal bandwidth (Shannon's theorem);
3. how to analyze sampled-data circuits using the Z-transform;
4. the principles of data conversion, both from analog to digital domain, and from digital to analog domain;
5. the non-idealities and the limitations of data converters, with reference to common converter architectures;
6. the operation of digital filters, and their analysis using signal flow diagrams and Z-transform;
7. how to design a discrete-time filter starting from the continuous-time prototype, and the relationship between the continuous-time and the discrete-time frequency responses;
8. the effects of the finite word length in digital filters.
1. the analysis techniques for stochastic processe and the mathematical models for noise in electronic devices;
2. the sampling process, the aliasing effect and the relationship between sampling frequency and signal bandwidth (Shannon's theorem);
3. how to analyze sampled-data circuits using the Z-transform;
4. the principles of data conversion, both from analog to digital domain, and from digital to analog domain;
5. the non-idealities and the limitations of data converters, with reference to common converter architectures;
6. the operation of digital filters, and their analysis using signal flow diagrams and Z-transform;
7. how to design a discrete-time filter starting from the continuous-time prototype, and the relationship between the continuous-time and the discrete-time frequency responses;
8. the effects of the finite word length in digital filters.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
All lectures can be given remotely through the web.
Course syllabus
1. Signals and systems: Fundamentals of signal theory.
2. Random variables, stochastic processes, and noise: Fundamentals of probability theory; cumulative function; probability density function; moments of a distribution. Stochastic processes; stationary processes; ergodic processes; correlation, autocorrelation, power spectral density. Thermal noise, shot noise, flicker noise.
3. Sampling and Z-transform: Sampling; Shannon theorem, aliasing. Sample&Hold circuit. Z-transform: definition and properties; frequency response in z-domain; s-to-z conversion; stability in z-domain.
4. Switched-capacitor circuits: Switched capacitors (SC); SC integrator; SC structures insensitive to parasitic capacitances.
5. Discrete-time filters: Moving average filters, auto-regressive filters; signal flow diagrams; canonical forms.
6. Quantization; Analog-to-digital and digital-to-analog conversion; input-output characteristic of an ideal converter; quantization error. Non-idealities of converters: offset error, gain error, non-linearity errors; non-motonicity. Examples of analog-to-digital and digital-to-analog converters.
7. Oversampling converters: Oversampling; noise-shaping; Σ∆ converters.
8. Digital filters and Discrete Fourier Transform: Digital filters and effects finite word-length of data and coeffcients. Discrete Fourier Transform; Fast Fourier Transform.
9. Introduction to integrated circuit fabrication technologies.
2. Random variables, stochastic processes, and noise: Fundamentals of probability theory; cumulative function; probability density function; moments of a distribution. Stochastic processes; stationary processes; ergodic processes; correlation, autocorrelation, power spectral density. Thermal noise, shot noise, flicker noise.
3. Sampling and Z-transform: Sampling; Shannon theorem, aliasing. Sample&Hold circuit. Z-transform: definition and properties; frequency response in z-domain; s-to-z conversion; stability in z-domain.
4. Switched-capacitor circuits: Switched capacitors (SC); SC integrator; SC structures insensitive to parasitic capacitances.
5. Discrete-time filters: Moving average filters, auto-regressive filters; signal flow diagrams; canonical forms.
6. Quantization; Analog-to-digital and digital-to-analog conversion; input-output characteristic of an ideal converter; quantization error. Non-idealities of converters: offset error, gain error, non-linearity errors; non-motonicity. Examples of analog-to-digital and digital-to-analog converters.
7. Oversampling converters: Oversampling; noise-shaping; Σ∆ converters.
8. Digital filters and Discrete Fourier Transform: Digital filters and effects finite word-length of data and coeffcients. Discrete Fourier Transform; Fast Fourier Transform.
9. Introduction to integrated circuit fabrication technologies.
Prerequisites for admission
The knowledge of the topics illustrated in Electronics 1 is required.
Teaching methods
Traditional, with lectures in classroom.
Teaching Resources
Lecture notes are provided by the teacher through the Arial website.
Assessment methods and Criteria
The final test is an oral examination.
The student will be asked questions on the topics of the lectures, and he/she must demonstrate deep knowledge of the the topics and the ability of making comparisons and critical evaluation of different circuit solutions.
The student will be asked questions on the topics of the lectures, and he/she must demonstrate deep knowledge of the the topics and the ability of making comparisons and critical evaluation of different circuit solutions.
FIS/01 - EXPERIMENTAL PHYSICS - University credits: 6
Lessons: 42 hours
Professor:
Liberali Valentino
Professor(s)