# A Four‐Terminal TiO                    <sub>                      2−                      <i>x</i>                    </sub>                    Memristor Architecture Enabling Multidimensional Associative Learning

https://mdr.nims.go.jp/datasets/662c7f24-c57d-4635-ba54-e46527404431

## File

- [Yamamoto 2026 PSSA TiO2-x memristor 4 terminal affiliation modified.pdf](https://mdr.nims.go.jp/filesets/0ef32ac9-c34b-4927-9d00-d3581772e748/download) ([Detail](https://mdr.nims.go.jp/filesets/0ef32ac9-c34b-4927-9d00-d3581772e748.md))

## Id

662c7f24-c57d-4635-ba54-e46527404431

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-09-16T06:10:45.490185Z

## Updated at

2026-09-16T07:06:29.080536Z

## Published at

2026-09-16T09:26:07.050201Z

## Doi



## First published url

https://doi.org/10.1002/pssa.70507

## Date published

2026-09-09

## Recorded date published

2026-9-9

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: A Four‐Terminal TiO                    <sub>                      2−                      <i>x</i>                    </sub>                    Memristor
    Architecture Enabling Multidimensional Associative Learning
  title_type: original
  lang: en

## Description

- description: 'Associative learning, a key higher-order function of biological synapses,
    remains challenging to implement in hardware due to limitations in existing memristor
    architectures. Here we develop a four-terminal TiO2-x memristor enabling two-dimensional
    control of oxygen vacancy distributions, exhibiting stable non-filamentary resistive
    switching accompanied by visible electrocoloring with voltages. Using a single
    four-terminal device, we demonstrate bidirectional Pavlovian conditioning encompassing
    both learning and forgetting processes. Furthermore, by arranging multiple memristors
    based on this architecture, multidimensional associative learning of two-dimensional
    image data is realized without involving any external circuits or computing units.
    These findings indicate that the proposed memristor architecture functions as
    a self-contained physical learning element capable of acquiring and updating associations
    between stimuli. This work thus provides a conceptual framework for neuromorphic
    hardware that implements higher-order associative functions, advancing beyond
    conventional software-based artificial neural networks. '
  description_type: abstract
  lang: und

## Creator

- name: Ryohei Yamamoto
  role: author
- name: Yusuke Hayashi
  role: author
  orcid: https://orcid.org/0000-0001-5672-1497
  organization: National Institute for Materials Science
- name: Zhuo Diao
  role: author
- name: Tetsuya Tohei
  role: author
- name: Akira Sakai
  role: author

## Contact agent



## Publisher

organization: Wiley

## Managing organization



## Keyword

- subject: memristor
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/
  date_licensed: 2026-09-01

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: physica status solidi (a)
  issn: '18626319'
  volume: '223'
  issue: '17'
  article_number: e70507

## Conference



## Related item



## Funding

- identifier: JP19K04468
  funder_name: Japan Society for the Promotion of Science
- identifier: JP20H00248
  funder_name: Japan Society for the Promotion of Science
- identifier: JP21K18723
  funder_name: Japan Society for the Promotion of Science
- identifier: JP23H01687
  funder_name: Japan Society for the Promotion of Science
- identifier: JP23K26380
  funder_name: Japan Society for the Promotion of Science
- identifier: JP24K00926
  funder_name: Japan Society for the Promotion of Science
- identifier: JP24K21620
  funder_name: Japan Society for the Promotion of Science

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



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## Custom property



## Fileset

- id: 0ef32ac9-c34b-4927-9d00-d3581772e748
  filename: Yamamoto 2026 PSSA TiO2-x memristor 4 terminal affiliation modified.pdf
  content_type: application/pdf
  size: 1472146
  md5: 9fbecb175b356e3cc30b831e33e9ed68

## Thumbnail

fileset_id: 0ef32ac9-c34b-4927-9d00-d3581772e748
filename: Yamamoto 2026 PSSA TiO2-x memristor 4 terminal affiliation modified.pdf