Title

Modeling and Experimental Characterization of Memristor Devices for Neuromorphic Computing

Author

Ayesha Zaman

Date of Award

1-1-2020

Degree Name

D.E. in Engineering

Department

Department of Electrical and Computer Engineering

Advisor/Chair

Advisor: Guru Subramanyam

Abstract

This thesis presents systematic study on the fundamental understanding of an emerging electronic device; memristor. First, different metal-switching layer-metal combinations were examined to explore the most stable memristor characterization. Each device consisted of top and bottom electrodes using reactive and inert metal contacts respectively. Next, charge transport mechanisms through such devices were investigated. Bilayer lithium niobate based memristor devices were fabricated and characterized as a model system for device physics study. This work demonstrates analysis of simple, steady state current conduction process through bilayer lithium niobate based memristor both for high and low resistance states. It is suggested when the device is in a high resistance state, deep trap energy level within the memristor switching layer initiate the device conductivity. The elastic trap assisted tunneling (ETAT) mechanism agrees with the experimental measurements in the high resistive region.The ohmic conduction mechanism agrees with the experimental measurements in the low resistive region for room temperature measurements. Memristor conductivity at high resistance state was found insignificantly affected with thermal variation and fits reasonably well for ETAT mechanism without taking the phonon assisted effects into account. The low resistance state conductivity is suggested to be because of space charge limited current (SCLC) conduction mechanism. Multiple memristor devices were investigated to corroborate the applicability of the proposed charge transport mechanism using theoretical framework and experimental validation. Lastly, several techniques are reported for characterizing stable, multiple or intermediate resistance states from different memristor device combinations for neuromorphic computing applications.

Keywords

Electrical Engineering, Neuromorphic device, Memristor, Elastic Trap Assisted Tunneling, Charge transport, Resistive Switching

Rights Statement

Copyright 2020, author

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