FinFETs have enabled advanced scaling. TCAD based variability studies have been performed as critical metric for device / technology evaluation. However, the extent of computing resources needed for 3D simulations to establish VT distribution is intense. Further, circuit analysis requires simpler models in compact form. In this talk, we will discuss the journey from analytical formulation to compact expressions for Line Edge Roughness (LER) and Metal Grain Granularity (MGG). Next, we will move to next generation Si electronics to enable Spiking Neural Network to mimic the brain and perform energy efficient cognitive tasks. Our group has invented and experimentally demonstrated the SOI MOSFET based Neuron using Floating Body Effect with excellent area and energy performance. We study transient variability in the devicrs, and relate it to impact ionization process. Finally we show how the noise can improve unsupervised learning in a network.
Udayan Ganguly received the B.Tech. degree in Metallurgical Engineering from the IIT Madras, in 2000 and the M.S. and Ph.D. degrees in Materials Science and Engineering at Cornell University, Ithaca, NY, in 2005 and 2006 respectively. In 2006, Udayan joined Applied Materials to serve as the technical lead for Flash Memory Applications Development at Applied Materials’ Front End Product Division, Sunnyvale, CA. He has joined Dept. of Electrical Engineering in 2010. His research interests are in semiconductor device physics and processing technologies for advanced memory, computing, and neuromorphic systems.