Doctoral Dissertations
Date of Award
8-2022
Degree Type
Dissertation
Degree Name
Doctor of Philosophy
Major
Mechanical Engineering
Major Professor
Stephen A. Sarles
Committee Members
Doug Aaraon, Joshua Sangoro, Joshua Yang
Abstract
A new class of electronic device has emerged which bear the potential for low powered brain like adaptive signal processing, memory, and learning. It is a non-linear resistor with memory coined as memristor. A memristor is a two-terminal electrical device which simultaneously changes its resistance (processing information) and store the resistance state pertaining to the applied power (memory). Therefore, it can collocate memory and processing much like our brain synapse which can save time and energy for information processing. Leveraging stored memory, it can thereby help future engineered systems to learn autonomously from past experiences. There has been a growing interest in understanding the working dynamics of these devices, exploring materials and engineering material combinations to build them, developing methods to integrate them in electronic systems, and building software for its operation with other traditional electronic devices, therefore encompassing multiple disciplines of science and engineering.
In Chapter 1, I review literature on memristive devices from their varied properties to different functionalities, different materials used, and different applications. I discuss the progress in the development of these devices, the gaps that persist, specific research objectives and state my approach to address some of the research gaps. Across chapters 2 to 5, I describe my work on different types of memristive devices and systems to address these research gaps. I demonstrated electrical synapse inspired device synchronizing firing of two neurons in chapter 2, revealed effect of low molecular weight amphiphilic block copolymers on the equilibrium properties of lipid membranes in chapter 3.
Chapter 4 presents a hardware alone system combining materials for distributed sensing (dimpled flexible sheet) and non-volatile memory (PEDOT:PSS thin film memristor) for distributed sensing, memory, and learning. This prototype can be physically trained to learn different input patterns and generate responses accordingly very similar to our biological tactile sensing and reaction system. Chapter 5 reveals the previously unknown resistive switching mechanism of the Cu/PEDOT:PSS thin film memristors.
As a summary of the contribution of this dissertation is to understand the contribution of material architecture and composition on resistive switching and memory in emerging biomolecular and polymeric neuromorphic devices.
Recommended Citation
Koner, Subhadeep, "Brain inspired organic electronic devices and systems for adaptive signal processing, memory, and learning.. " PhD diss., University of Tennessee, 2022.
https://trace.tennessee.edu/utk_graddiss/7328
Included in
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