Title
Electrical Characterization of Memristors for Neuromorphic Computing
Date of Award
2021
Degree Name
M.S. in Electrical and Computer Engineering
Department
Department of Electrical and Computer Engineering
Advisor/Chair
Guru Subramanyam
Abstract
This thesis studied an emerging electronic device, the memristor, to gain a fundamental understanding of the switching characteristics of different device structures. Memristors with a thin film hafnium oxide (HfO2) switching layer and a phase change material (PCM), germanium telluride (GeTe), thin film switching layer are studied. This work investigated a variety of electrical characterization experiments to determine the core functionally, robustness, and neuromorphic attributes of the two different memristor devices. The electrical biasing comprised of endurance, stability, plasticity, multi-state, and synaptic plasticity characterization. The HfO2 based memristors were determined to have multiple stable resistance states when restricting the applied current at different values. These limits were 10 µA, 15 µA, 30 µA, 50 µA, and 300 µA and as the allowed current increased the lower the measured resistance would be. This study also explored transmission electron microscopy (TEM) to determine structural changes of the GeTe memristors due to electrical and thermal stimuli. The TEM results for the (PCM) showed similar structural changes near the GeTe and top electrode interface when comparing the results from both stimuli.
Keywords
Engineering, Memristor, RRAM, Neuromorphic, Oxygen vacancy, PCM
Rights Statement
Copyright 2021, author.
Recommended Citation
Shallcross, Austin David, "Electrical Characterization of Memristors for Neuromorphic Computing" (2021). Graduate Theses and Dissertations. 6982.
https://ecommons.udayton.edu/graduate_theses/6982