Course 1: Semiconductor Device Physics
Core content
- Materials physics: energy bands, charge carriers, doping
- Junctions: metal-semiconductor, p-n, tunnel
- Devices: Schottky diode, p-n junction diode, tunnel diode, bipolar junction transistors, field effect transistors, semiconductor memory devices
Specialist knowledge
- Metal oxide and 2D semiconductors
- Memristors, Spintronics and Ferroelectric Devices
Course 2: Future Computing Devices and Systems
In this course, students will get a broad overview of future computing devices and architectures mostly focusing on devices and architectures for brain-inspired or neuromorphic computing. Upon successfully finishing this course, students will:
- Have an understanding of the challenges traditional computing is facing as CMOS scaling is coming to an end and needs for complete paradigm shifting in computing in the era of big-data and artificial intelligence (AI).
- Have a broad overview of the basic principles of neuromorphic computing and the differences with traditional computer architectures and computing paradigms;
- Have an understanding of information processing and encoding in the brain and the physical and computational motivation for e.g. near and in-sensor data processing, comparison of deep neural network and spiking neural network, rate vs temporal coding, analog vs. spiking behavior, event-driven information processing, and the differences between brain and silicon implementations and considerations;
- Learn to analyze challenges arising from practical hardware and how to mitigate them in circuit and systems level.