Teaching

A. Undergraduate Courses

1. EE1204 - Engineering Electromagnetics - 2 credits (Core)

with Dr. Jose Titus who covers magnetostatics.

Course contents (Electrostatics): Intro/Motivation: electrostatics governs transistor technology!, primer to vector calculus, Columb's & Gauss' law, Electrostatic potential and energy, Electrostatics in conductors (screening) and insulators (polarization), Numerically solving electrostatics (Poisson's Eqn) in simulators.

2. EE2301 - Electronic Devices and Circuits Lab - 2 credits (Core Lab)

Lab modules: Lab familiarisation, measurement methodology, Passive and Active Filters, Diodes circuits, Solar cell efficiency, Junction capacitance of Solar Cells, BJT and MOSFET characterisation, mini-project(s).

B. Postgraduate Courses

1. EE5181 - Semiconductor Devices Fundamentals - 3 credits (PG Core, UG elective)

with Dr. Oves Badami

Course contents:

Part 1: Intro/Motivation, Basics of quantum mechanics (Scrodinger's equation, particle in a potential well, tunneling), Band theory of solids (band gap, effective mass), Equilibrium carrier statistics (DOS, Fermi-Dirac Distribution), Non-equilibrium conditions (Quasi-Fermi level), generation/recombination, carrier continuity. pn junctions: band diagrams, ideal and non-ideal I-V characteristics. basics of heterojunctions.

Part 2: Metal/Semiconductor junctions (Schottky-Mott theory, image force lowering, fermi-level pinning), BJTs bipolar junction transistors (basics with band diagrams, mechanism of gain, device engineering to improve gain), MOSCAPs (modes of operation, ideal and non-ideal CV characteristics), MOSFETs (long channel characteristics, short channel effects (SCE), device engineering to mitigate SCE)

2. EE5520 - Neuromorphic devices for AI/ML - 2 credits (PG, UG elective) New Course

Course contents: Motivation: Current computing paradigm can NOT sustainably support AI/ML. Understanding the von-Neumann architecture, memory organisation. von-Neumann data bottleneck.

Memory centric compute paradigm for AI/ML. Current memory devices (SRAM, DRAM, NAND flash), future memory devices (eNVMs, memristors, PCM, ferroelectric, spintronic).

Hardware architectures for AI/ML: artificial and spiking neural networks. Neuronal behaviour: Leaky Integrate and Fire, Synaptic behaviour: spike time-dependent plasticity (STDP), Mimicking synaptic and neuronal behaviour through devices.