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.

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 electronics and biotechnology.

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 the teaching objectives is evaluated through participation in activities and assignments during the course, as well as a final exam. There may be group work during the course that will contribute to the final grade (2-3 points max). The final exam may be written, oral, or through a presentation of an assigned task.

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

English

written and oral