Abstract

Neuromorphic computing comprises of systems that are based on the human brain or artificial neural networks, with the promise of creating a brain inspired ability to learn and adapt, but technical challenges, such as developing an accurate neuroscience model of the functionality of the brain to building devices to support these models, are significantly hindering the progress of neuromorphic systems. This has paved the way for artificial neural networks (ANN) to meet these criteria. The memristor has become an emerging candidate to realize ANN through emulation synapse and neuron behavior. In this work, we are fabricating an Ag/MoS­2/Au threshold switching memristor (TSM), to emulate four critical behaviors of neurons - all-or-nothing spiking, threshold-driven firing, post firing refractory period and stimulus strength-based frequency response. We will also test the innate stochastic behavior of these devices to see if they are voltage dependent, making them a possible application in the integrate and fire neuron. Continuing to emulate biological synapses using memristors can help solve many optimization and machine learning problems, which in turn, can make electronics as energy-efficient as our brain.

Thesis Completion

2019

Semester

Fall

Thesis Chair/Advisor

Roy, Tania

Degree

Bachelor of Science in Electrical Engineering (B.S.E.E.)

College

College of Engineering and Computer Science

Department

Electrical and Computer Engineering

Degree Program

Electrical Engineering

Language

English

Access Status

Open Access

Release Date

12-1-2019

Download

Share

COinS