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

https://mdr.nims.go.jp/datasets/5af5897b-2841-4dc0-af29-2b7f95209b46

## File

- [2025_ACSAEM7_4616.pdf](https://mdr.nims.go.jp/filesets/f0d997ca-fc7c-4344-9aa6-04f402f42c68/download) ([Detail](https://mdr.nims.go.jp/filesets/f0d997ca-fc7c-4344-9aa6-04f402f42c68.md))

## Id

5af5897b-2841-4dc0-af29-2b7f95209b46

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-05-30T07:15:48.542067Z

## Updated at

2025-05-30T23:30:25.108786Z

## Published at

2025-05-30T23:22:50.124721Z

## Doi



## First published url

https://doi.org/10.1021/acsaelm.5c00529

## Date published

2025-05-27

## Recorded date published

2025-5-27

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Bias Sweep-Induced Analog Memristor Behavior, Using a Cuprous Iodide Thin
    Film, for Neuromorphic Computing
  title_type: original
  lang: en

## Description

- description: 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.
  description_type: abstract
  lang: und

## Creator

- name: Rajesh Deb
  role: author
- name: Samapika Mallik
  role: author
  orcid: https://orcid.org/0000-0001-9281-9416
- name: Yamineekanta Mishra
  role: author
- name: Roshan Padhan
  role: author
- name: Satyaprakash Sahoo
  role: author
  orcid: https://orcid.org/0000-0001-9766-3713
- name: Kazuya Terabe
  role: author
  orcid: https://orcid.org/0000-0003-3988-3456
- name: Tohru Tsuruoka
  role: author
  orcid: https://orcid.org/0000-0002-4322-4309
- name: Saumya R. Mohapatra
  role: author
  orcid: https://orcid.org/0000-0002-6753-5312

## Contact agent



## Publisher

organization: American Chemical Society (ACS)

## Managing organization



## Keyword

- subject: analog memristor
  schema: not_defined
- subject: mixed ionic-electronic conductor
  schema: not_defined
- subject: cuprous iodide
  schema: not_defined
- subject: artificial synapse
  schema: not_defined
- subject: artificial neural network
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin



## Embargo



## Journal

- title: ACS Applied Electronic Materials
  issn: '26376113'
  volume: '7'
  issue: '10'
  start_page: 4616
  end_page: 4627

## Conference



## Related item



## Funding

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

## Instrument



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## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



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## Computational method



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## Fileset

- id: f0d997ca-fc7c-4344-9aa6-04f402f42c68
  filename: 2025_ACSAEM7_4616.pdf
  content_type: application/pdf
  size: 7784944
  md5: '0447019355baae78d06b68347c86c1bf'

## Thumbnail

fileset_id: f0d997ca-fc7c-4344-9aa6-04f402f42c68
filename: 2025_ACSAEM7_4616.pdf