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Modeling and Experimental Characterization of Memristor Devices for Neuromorphic Computing

Zaman, Ayesha
http://rave.ohiolink.edu/etdc/view?acc_num=dayton159636782366637

Abstract Details

2020, Doctor of Engineering, University of Dayton, Electrical and Computer Engineering.
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.
Guru Subramanyam, Ph.D. (Committee Chair)
Tarek Taha, Ph.D. (Committee Member)
149 p.
Electrical Engineering
Neuromorphic device; Memristor; Elastic Trap Assisted Tunneling; Charge transport, Resistive Switching

Recommended Citations

Citations

  • Zaman, A. (2020). Modeling and Experimental Characterization of Memristor Devices for Neuromorphic Computing [Doctoral dissertation, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton159636782366637

    APA Style (7th edition)

  • Zaman, Ayesha. Modeling and Experimental Characterization of Memristor Devices for Neuromorphic Computing . 2020. University of Dayton, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton159636782366637.

    MLA Style (8th edition)

  • Zaman, Ayesha. "Modeling and Experimental Characterization of Memristor Devices for Neuromorphic Computing ." Doctoral dissertation, University of Dayton, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton159636782366637

    Chicago Manual of Style (17th edition)

dayton159636782366637
827
© 2020, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.