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Title: Intercalation-based Memristors for Temporal Computation and Neuromorphic Architectures
Committee:
Dr. Alan Doolittle, ECE, Chair, Advisor
Dr. Shimeng Yu, ECE
Dr. Asif Khan, ECE
Dr. Saibal Mukhopadhyay, ECE
Dr. Eric Vogel, MSE
Abstract: Neuromorphic computation is a comprehensive solution to the issues hindering computer evolution; thus, researchers have been testing various materials and architectures for viability as a memristor. Filamentary memristor technology has been at the forefront of this effort but fall short in analog dynamic ranges and programming energy scalability. LiNbO2 has been shown to have a large analog continuum of resistance states, with both volatile and non-volatile intercalation-based memristivity. However, the scalability of the LiNbO2 memristor system has not yet been fully explored, with the majority of
studies featuring wide aspect ratios and large active areas greater than 50 m. Preliminary studies have shown that smaller LixNbO2 devices will have improved memristor performance. Additionally, attempts to utilize non-volatile memristors in truly neuromorphic computing architectures have been scarce. The objective of the outlined research is to investigate the scalability of LixNbO2 memristive technology, including performance at smaller length scales, and explore neuromorphic computing architectures through the development and application of a flux-linkage controlled memristor model for intercalation-based memristors in functional circuitry and memristive networks.
