Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/12351
Title: Experimental Validation of Switching Dependence of Nanoscale Y2O3 Memristors on Electrode Symmetry via Physical Electrothermal Modeling
Authors: Gautam, Mohit Kumar
Chaudhary, Sumit
Hindoliya, Lokesh Kumar
Mukherjee, Shaibal
Keywords: electrode variation;linearity factor;memristor;synaptic behavior;Y2O3
Issue Date: 2023
Publisher: American Chemical Society
Citation: Gautam, M. K., Kumar, S., Chaudhary, S., Hindoliya, L. K., Kumbhar, D. D., Park, J. H., Htay, M. T., & Mukherjee, S. (2023). Experimental Validation of Switching Dependence of Nanoscale Y2O3 Memristors on Electrode Symmetry via Physical Electrothermal Modeling. ACS Applied Electronic Materials. Scopus. https://doi.org/10.1021/acsaelm.3c00598
Abstract: In this work, the impact of symmetric and asymmetric electrodes on the resistive switching (RS) behavior of the nanoscale Y2O3-based memristor is investigated with experiments. In addition, the extracted switching parameters are validated with systemic modeling. Memristor growth is deployed by utilizing a dual ion beam sputtering (DIBS) system, and simulation is carried out in a semiconductor physics-based tool, i.e., COMSOL Multiphysics with a defined MATLAB script. The performed simulation work is based on the minimum free energy of the used materials at an applied certain voltage. The simulated results exhibit a stable pinched hysteresis loop in the RS responses either in symmetric or asymmetric electrode combinations with an efficient ON/OFF current ratio and show a close match with the experimental results. Moreover, the simulated devices show synaptic plasticity functionalities in terms of potentiation and depression processes with an almost ideal linearity factor for both electrode combinations similar to the realistic experimental data. Therefore, the present work efficiently depicts the suitability of the electrode material with the Y2O3 switching layer to enhance electrical performance to integrate into the artificial synapse and neuromorphic computations. © 2023 American Chemical Society.
URI: https://doi.org/10.1021/acsaelm.3c00598
https://dspace.iiti.ac.in/handle/123456789/12351
ISSN: 2637-6113
Type of Material: Journal Article
Appears in Collections:Department of Electrical Engineering

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