Article Bias Sweep-Induced Analog Memristor Behavior, Using a Cuprous Iodide Thin Film, for Neuromorphic Computing

Rajesh Deb ; Samapika Mallik ORCID ; Yamineekanta Mishra ; Roshan Padhan ; Satyaprakash Sahoo ORCID ; Kazuya Terabe SAMURAI ORCID ; Tohru Tsuruoka SAMURAI ORCID ; Saumya R. Mohapatra ORCID

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Rajesh Deb, Samapika Mallik, Yamineekanta Mishra, Roshan Padhan, Satyaprakash Sahoo, Kazuya Terabe, Tohru Tsuruoka, Saumya R. Mohapatra. Bias Sweep-Induced Analog Memristor Behavior, Using a Cuprous Iodide Thin Film, for Neuromorphic Computing. ACS Applied Electronic Materials. 2025, 7 (10), 4616-4627. https://doi.org/10.1021/acsaelm.5c00529

Description:

(abstract)

This study introduces a memristor device, made up of a well-known MIEC cuprous iodide (CuI), for artificial synaptic applications. A cross-point structured Cu/CuI/Pt device initially shows digital bipolar RS under bias voltage sweeping, which is characterized by well-separated SET and RESET voltages with a high ON/OFF resistance ratio of ~105. After 100 bias sweeping cycles, the device completely changes, showing analog RS behavior without any well-defined SET and RESET voltage, and exhibits a continuous current trajectory under bias sweeps. In comparison to the digital RS mode, the analog RS mode exhibits minimal cycle-to-cycle variability with a reduced ON/OFF ratio of ~10. The current conduction mechanism underlying digital switching behavior is ascribed to the formation and dissolution of a Cu filament. The analog RS behavior arises from charge trapping/detrapping at defect sites created during digital RS cycles. The device showing analog RS exhibits long-term and short-term plasticity, similar to biological synapses under voltage pulse applications. Utilizing the long-term plasticity data, artificial neural network simulations demonstrate an image recognition accuracy of ~93% for handwritten digits. Furthermore, the device successfully replicates paired-pulse facilitation/depression and spike timing-dependent plasticity.

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Keyword: analog memristor, mixed ionic-electronic conductor, cuprous iodide, artificial synapse, artificial neural network

Date published: 2025-05-27

Publisher: American Chemical Society (ACS)

Journal:

  • ACS Applied Electronic Materials (ISSN: 26376113) vol. 7 issue. 10 p. 4616-4627

Funding:

  • Ministry of Education, Culture, Sports, Science and Technology JPMXP1224NM5068
  • Department of Science and Technology, Ministry of Science and Technology, India SR/FST/PSI-212/2016(C)
  • Japan Society for the Promotion of Science 24K02917

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1021/acsaelm.5c00529

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Updated at: 2025-05-31 08:30:25 +0900

Published on MDR: 2025-05-31 08:22:50 +0900

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