BIOELECTRONICS

This course is part of the elective exams for the Master's degree in physical engineering. The course offers an in-depth exploration of cutting-edge technologies at the intersection of electronics and biology. Through a comprehensive curriculum, students will delve into research in bioelectronics focusing on three main areas: More than Moore Devices, Biosensors, and Neuromorphic Devices.

The course begins by examining More than Moore Devices, which encompass a diverse array of electronic components and systems that extend beyond traditional Moore's Law scaling. Students will explore innovative technologies such as flexible electronics, bio-compatible materials, and bio-integrated devices. Through lectures and hands-on projects, they will gain insights into the fabrication techniques, materials selection, and applications of these advanced electronic systems in biomedical engineering.

Next, the curriculum delves into Biosensors, crucial tools for detecting biological molecules and signals with high sensitivity and specificity. Participants will study the principles underlying biosensor operation, including transduction mechanisms, sensor design, and signal processing techniques. They will also explore various types of biosensors, such as electrochemical, optical, and piezoelectric sensors, and their applications in healthcare, environmental monitoring, and food safety.

Lastly, the course addresses Neuromorphic Devices, which aim to mimic the structure and function of the human brain's neural networks. Students will investigate emerging neuromorphic computing architectures and devices, such as memristors, spiking neural networks, and neuromorphic sensors. Through theoretical discussions and practical exercises, they will learn about the principles of neuromorphic computing, its advantages over traditional computing paradigms, and its potential applications in artificial intelligence, robotics, and cognitive neuroscience.

By the end of the program, graduates will be equipped with a deep understanding of More than Moore Devices, Biosensors, and Neuromorphic Devices, empowering them to contribute to advancements in healthcare, biotechnology, and beyond.

1. Knowledge and Understanding
- Understand the limitations of ultrascaled CMOS devices.
- Comprehend the physical principles underlying More-than-Moore devices, neuromorphic systems, and biosensors.
- Know the fundamental technologies required for the fabrication of such devices.

2. Ability to Apply Knowledge and Understanding
- Apply concepts covered in class to analyze new devices and potentially suggest improvements.

3. Judgment Autonomy
- Assess the logical consistency of results, both in theoretical contexts and with experimental data.
- Identify potential errors through critical analysis of the methods used.

4. Communication Skills
- Communicate acquired knowledge accurately using appropriate terminology, both orally and in writing.
- Engage respectfully and constructively with the instructor and peers, particularly during group projects.

5. Learning Skills
- Take effective notes, selecting and organizing information based on its importance and relevance.
- Demonstrate sufficient independence in gathering data and information pertinent to the issue being investigated.

Basic concepts of physics, chemistry, and electronic devices.

The course begins by examining More than Moore Devices, which encompass a diverse array of electronic components and systems that extend beyond traditional Moore's Law scaling. Students will explore innovative technologies such as flexible electronics, bio-compatible materials, and bio-integrated devices. Through lectures and hands-on projects, they will gain insights into the fabrication techniques, materials selection, and applications of these advanced electronic systems in biomedical engineering.

Next, the curriculum delves into Biosensors, crucial tools for detecting biological molecules and signals with high sensitivity and specificity. Participants will study the principles underlying biosensor operation, including transduction mechanisms, sensor design, and signal processing techniques. They will also explore various types of biosensors, such as electrochemical, optical, and piezoelectric sensors, and their applications in healthcare, environmental monitoring, and food safety.

Lastly, the course addresses Neuromorphic Devices, which aim to mimic the structure and function of the human brain's neural networks. Students will investigate emerging neuromorphic computing architectures and devices, such as memristors, spiking neural networks, and neuromorphic sensors. Through theoretical discussions and practical exercises, they will learn about the principles of neuromorphic computing, its advantages over traditional computing paradigms, and its potential applications in artificial intelligence, robotics, and cognitive neuroscience.

Wan, Qing, and Yi Shi, eds. Neuromorphic Devices for Brain-inspired Computing: Artificial Intelligence, Perception, and Robotics. John Wiley & Sons, 2022.

Iacopi, Francesca, and Francis Balestra, eds. More-than-Moore Devices and Integration for Semiconductors. Springer Nature, 2023.

The achievement of course objectives is assessed through participation in activities (quizzes) conducted during the course and a final oral exam.

The exam consists of two parts completed in a single interview, conducted in English:
1. A 20-25 minute seminar on a topic pre-assigned by the instructor (student suggestions are welcome). The student must present the general concepts of the topic accurately and comprehensively, providing examples at the level of the lectures and textbook. Students are encouraged to find original examples, applications, and connections with other topics to demonstrate a high level of understanding. A PowerPoint presentation is ideal for the seminar, though a blackboard presentation is also an option for those who prefer.

2. Two or three questions on the core material of the course as presented during the lectures. The student must respond (with blackboard support if necessary) to demonstrate an understanding of the basic concepts and knowledge of the course. Both theoretical and experimental aspects will be considered equally important.

A successful exam (score of 27-30/30) will be awarded when the student demonstrates a solid and broad mastery of the concepts discussed during the lectures. An average score (22-26/30) will reflect a fairly complete understanding of individual topics but with limited connections between them. A passing score (18-21/30) corresponds to a minimal knowledge of individual concepts.

Students who attend lectures can earn additional points by participating in quizzes or assignments given in class. This bonus will be added to the oral exam score and/or may reduce the number of core questions asked during the exam.

classroom lectures, group assignments, exercises to be carried out in class or at home

English

written and oral