B5722 - EMBEDDED SENSORS

Conoscenze e abilità da conseguire

Al termine del corso, lo studente matura le conoscenze per la comprensione e l'utilizzo di sensori elettronici. In particolare, lo studente impara a analizzare i principali problemi della sensoristica e ad interpretare i dati provenienti dai sensori per la successiva elaborazione dell'informazione. Mediante esempi di progettazione concreti, gli strumenti sono focalizzati all'esigenza di un continuo sviluppo tecnologico in grado di soddisfare le crescenti esigenze della progettazione nel campo dell'informatica e delle infrastrutture ICT. Successivamente lo studente sara' in grado di apprendere le principali tecniche di implementazione dei sensori coniugando le conoscenze dei sistemi informatici pregresse con alcuni concetti di progettazione elettronica che saranno introdotti nel corso.

Contenuti

PART I (6 CFU)

Program for:

• SENSORI INTELLIGENTI [Laurea Magistrale in Ingegneria Elettronica e dell'Informazione]
• SENSORS & NANOTECHNOLOGY [Laurea Magistrale in Biomedical Engineering]
• EMBEDDED SENSORS [Laurea Magistrale in Ingegneria e Scienze Informatiche]

The goal of Part I is to give students fundamental know-how in sensor system design, approached from the perspective of behavioral building blocks and their technological implementation.

FUNDAMENTALS

· Introduction of the course, example of research.

· Fundamentals on sensors. Signals and information. Sensor as an information classifier. Interface, raw data and signal processing (Machine Learning).

· Basics on measurements. Characterization and operating mode. The concept of distributions and estimation. DC measurements. Digits and full-scale. Random and systematic errors. Introduction to the resolution, precision and accuracy concepts. The role of time and AC measurements. Power spectral distributions.

· Ideal sensor modeling. Interface as a black-box. The quasi-static function and its limits. The concept of sensitivity. Time and space quantization, the A/D converter. Acquisition chains and gain plots. Signal bandwidth.

· Recall of mathematical tools. Random signals, power and correlations. Random and ergodic processes.

· Non-ideal sensor modeling. The resolution/uncertainty duality. The law of propagation of errors.

· Comparing signal with noise. The concept of minimum detectable signal. The signal-to-noise ratio and the dynamic range. Systematic error modeling.

· The quantization process. The concepts of number of resolution levels and effective number of bits. Relationship with Information Theory. Resolution degradation in acquisition chains. Example of designs. The role of bandwidth, oversampling and non-aliasing filters.

· Feedback sensor design with examples.

· Sensor trade-offs and example of design.

ELECTRONIC INTERFACE DESIGN

[This section is OPTIONAL and NOT REQUIRED for EMBEDDED SENSORS students]

· Introduction of noise in circuit design. PSD and noise density. Thermal noise shot noise ad flicker noise. KTC noise. Superimposition of noise powers in uncorrelated signals. Equivalent noise bandwidth. Concept of input-referred noise. Input referred noise in BJT and MOS devices. Noise in OPAMPs.

Resistance sensors with examples. Strain-gauges. RTDs e PRTs.Thermistors, NTC e PTC. Magnetic sensors.

· Capacitive sensors. Kelvin guard ring. Charge amplifier. Differential capacitive sensing. Capacitive accelerometers. Noise in charge amplifiers. Correlated double sampling (CDS).

· Lock-in e chopper sensing. Complex impedance measurements by lock-in sensing.

· Introduction to optical sensors. The photodiode. Charge and voltage photodiode readout in storage mode. Passive pixel CMOS sensors (PPS) and active pixel (APS) sensors. APS with correlated doble sampling.

· Sensor networks.

SENSORS ECOSYSTEMS

· Brief overview of embedded sensor networks. I2C and SPI.

· Brief overview of wireless sensor communications and networks.

TOWARDS MACHINE LEARNING EMBEDDED SENSORS

· The concept of predictive models. Measured vs. predicted measurements plots.

· Dealing with multiple variables: scatter plots and correlation matrix in multivariate analysis.

· Reduction of variables using decomposition techniques. The PCA.

· Reduction to output values, introduction to the PLS technique

· Example of application: embedded spectral sensors

PART II (NANOTECHNOLOGY SECTION, 3 CFU)

Program for:

• SENSORS & NANOTECHNOLOGY [Laurea Magistrale in Biomedical Engineering]

Part II is offered exclusively to students in Biomedical Engineering and is intended to introduce the physical foundations of biosensor transduction principles at the micro- and nanoscale. These concepts will then be applied in the context of protocols and instruments for biomedical laboratories. This part will also include a hands-on laboratory experience at the end of the course.

· Photons, electrons and energetic interactions with matter. Fluorescence, phosphorescence and Raman scattering. Photon-electron transduction. Black body emission. Quantum efficiency and spectral sensitivity of transduction in semiconductors. Responsivity.

· Basics of geometrical optics. Refraction and diffraction. Optical resolution and its limits. Optical microscope. Basics of electron microscopy. Basics of atomic force microscope (AFM).

· The origin of noise. Brownian noise. Thermal noise and its derivation. Poisson processes. Shot noise and related derivation. The flicker noise and its derivation. Physical origin of flicker noise.

· Introduction of biosensing principles. Ion-electron transduction. Electrical polarization of the matter. Metal-liquid interface and interfacial states. Electrical models of ion/electron interfaces and electro impedance spectroscopy.

· Introduction to nanotechnology. Nanosensors: nanopores, nanotubes, nanowires, graphene, nanodots.

· Lab experience on electro-impedance or Raman spectrometry.

Testi/Bibliografia

Main texbook

M. Tartagni, Electronic Sensor Design Principles, Cambridge Press, 2021

------------------------------------------------------------------------------------

Physical principles:

R. Feynman et al., The Feynman Lectures on Physics, Addison Wesley, 1963

Noise in electronics:

P. Gray, R. Meyer, Analysis and Design of Analog Integrated Circuits, Wiley 1993

Ion/electron interface, micro- and nano-fabrication:

H. Morgan, N. Green, AC Electrokinetics: colloids and nanoparticles, RSP Press, 2001

M. Madou, Fundamentals of Microfabrication, CRC Press, 2002

Electronic interfaces:

D. Johns, K. Martin, Analog Integrated Circuit Design, Wiley, 1997

B. Razavi, Design of Analog CMOS Integrated Circuits, McGraw-Hill, 2000

Machine learning and multivariate analysis:

Kevin Dunn, “Process improvement using data”, [Online]. Available: https://learnche.org/pid/

K. P. Murphy, Probabilistic machine learning: an introduction. Cambridge, Massachusetts: The MIT Press, 2022.

Metodi didattici

Il corso e' basato su lezioni frontali in presenza tenute dal docente e coadiuvate da slide che seguiranno il programma e disponibili su Virtuale. La parte per Ingegneria Biomedica prevede un'esperienza di laboratorio.

Modalità di verifica e valutazione dell'apprendimento

L’esame consiste in un colloquio orale di 40-60m su entrambi i moduli didattici. Particolarmente valutate saranno le capacitа critiche dello studente sugli argomenti del programma e nelle sue capacita’ di contestualizzare e argomentare i concetti. L'esame potra’ essere svolto in lingua italiana o inglese. In entrambi i casi saranno valutate l’appropriatezza della terminologia utilizzata dallo studente.

 

 

Alle Studentesse e agli Studenti con DSA o disabilità temporanee o permanenti si raccomanda di contattare per tempo l’ufficio di Ateneo responsabile, all'indirizzo https://site.unibo.it/studenti-con-disabilita-e-dsa/it). Sarà cura dell'ufficio proporre eventuali adattamenti, che dovranno comunque essere sottoposti, con un anticipo di 15 giorni, all’approvazione del/la docente, che ne valuterà l'opportunità anche in relazione agli obiettivi formativi dell'insegnamento.

Strumenti a supporto della didattica

Slides will be available on Virtuale.

Orario di ricevimento

Consulta il sito web di Marco Tartagni