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Electrical Characterization of Memristors for Neuromorphic Computing

Shallcross, Austin David
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1639669968010977

Abstract Details

, Master of Science in Electrical Engineering, University of Dayton, Electrical and Computer Engineering.
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.
Vamsy Chodavarapu (Committee Member)
Sabyasachi Ganguli (Committee Member)
Guru Subramanyam (Advisor)
Engineering
Memristor; RRAM; Neuromorphic; Oxygen vacancy; PCM

Recommended Citations

Citations

  • Shallcross, A. D. (2021). Electrical Characterization of Memristors for Neuromorphic Computing [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1639669968010977

    APA Style (7th edition)

  • Shallcross, Austin. Electrical Characterization of Memristors for Neuromorphic Computing . 2021. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1639669968010977.

    MLA Style (8th edition)

  • Shallcross, Austin. "Electrical Characterization of Memristors for Neuromorphic Computing ." Master's thesis, University of Dayton, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1639669968010977

    Chicago Manual of Style (17th edition)

dayton1639669968010977
78
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This open access ETD is published by University of Dayton and OhioLINK.