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Description:

Reservoir computing (RC) is a machine learning framework suitable for processing time series data, and is a computationally inexpensive and fast learning model. A physical reservoir is a hardware implementation of RC using a physical system, which is expected to become the social infrastructure of a data society that needs to process vast amounts of information. Ion-gating reservoirs (IGR) are compact and suitable for integration with various physical reservoirs, but the prediction accuracy and operating speed of redox-IGRs using WO3 as the channel are not sufficient due to irreversible Li+ trapping in the WO3 matrix during operation. Here, in order to enhance the computation performance of redox-IGRs, we developed a redox-based IGR using a (104) oriented LiCoO2 thin film with high electronic and ionic conductivity as a trap-free channel material. The subject IGR utilizes resistance change that is due to a redox reaction (LiCoO2 ⟺ Li1−xCoO2 + xLi+ + xe−) with the insertion and desertion of Li+. The prediction error in the subject IGR was reduced by 72% and the operation speed was increased by 4 times compared to the previously reported WO3, which changes are due to the nonlinear and reversible electrical response of LiCoO2 and the high dimensionality enhanced by a newly developed physical masking technique. This study has demonstrated the possibility of developing high-performance IGRs by utilizing materials with stronger nonlinearity and by increasing output dimensionality.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: reservoir computing, ionics, redox transistor, ion-gating reservoir, neuromorphic computing

Date published: 2023-11-29

Publisher: Springer Science and Business Media LLC

Journal:

• Scientific Reports (ISSN: 20452322) vol. 13 issue. 1 21060

Funding:

• MEXT | Japan Society for the Promotion of Science JP22H04625
• MEXT | Japan Society for the Promotion of Science JP22KJ2799
• Iketani Science and Technology Foundation

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

MDR DOI:

First published URL: https://doi.org/10.1038/s41598-023-48135-z

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Updated at: 2024-12-12 16:30:42 +0900

Published on MDR: 2024-12-12 16:30:42 +0900