Title:  Filamentary and Ferro-Electric Semiconductor Junction Devices For Brain-Inspired Computing: from Physics to Deep Learning

Committee: 

Dr. Vogel, Advisor        

Dr. Doolittle, Chair

Dr. Datta

Abstract: The objective of the proposed research is to advance our understanding of brain-inspired computing for sustainable energy-efficient artificial intelligence through materials, device, and system-level investigation. The pervasive usage of artificial intelligence to improve the quality of life has led to a massive demand for energy. One of the primary reasons for the tremendous energy consumption in traditional von-Neumann architecture is the continuous data transfer between the memory and the processing unit. Brain-inspired analog and in-memory computing aim to solve this issue by allowing calculation and memory at the same place, similar to nature’s astounding computing machine: the human brain. However, its widespread adoption is prohibited by its non-ideal behavior arising from the need for more material, device, and system-level optimization. This dissertation aims to develop a deeper understanding of brain-inspired synaptic devices and take strides toward their ideal behavior to unlock their full potential. The first aim of this dissertation focuses on developing a fundamental understanding of the HfOx synaptic device physics and the impact of doping on its characteristics. Using this knowledge, the second aim of the dissertation aspires to improve the synaptic devices through barrier layer, electrode material, and active layer material optimization and corroborate the proposed hypotheses using simulation. The third aim builds on the insight gained from the device-level research to improve the system-level performance of an IBM analog brain-inspired chip for deep learning.