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Physical reservoir computing is a promising way to develop an efficient artificial intelligence using physical devices exhibiting nonlinear dynamics. Although magnetic materials have advantages in miniaturization, the need for a magnetic field and large electric current result in high electric power consumption and a complex device structure. To resolve these issues, we propose a redox-based physical reservoir utilizing the planar Hall effect and anisotropic magnetoresistance, which are phenomena described by different nonlinear functions of the magnetization vector, that does not need a magnetic field to be applied. The expressive power of this reservoir based on a compact all-solid-state redox transistor is higher than previous physical reservoir. The normalized mean square error of the reservoir on a second-order nonlinear equation task was 1.69×10-3, which is lower than that of a memristor array (3.13×10-3) even though the number of reservoir nodes was fewer than half that of the memristor array.

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  This document is the unedited Author’s version of a Submitted Work that was subsequently accepted for publication in Nano Letters, copyright © 2024 American Chemical Society after peer review. To access the final edited and published work see https://doi.org/10.1021/acs.nanolett.3c05029

Keyword: Reservoir computing, Magnetic property tuning, Planar Hall effect, Redox, Solid-state electrolyte, Lithium ion

Date published: 2024-04-17

Publisher: American Chemical Society (ACS)

Journal:

• Nano Letters (ISSN: 15306984) vol. 24 issue. 15 p. 4383-4392

Funding:

• Precursory Research for Embryonic Science and Technology JPMJPR23H4
• Japan Society for the Promotion of Science JP21J21982
• Japan Society for the Promotion of Science JP22H04625
• National Institute for Materials Science
• Yazaki Memorial Foundation for Science and Technology
• Iketani Science and Technology Foundation

Manuscript type: Author's version (Submitted manuscript)

MDR DOI: https://doi.org/10.48505/nims.4620

First published URL: https://doi.org/10.1021/acs.nanolett.3c05029

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Updated at: 2024-08-05 12:30:30 +0900

Published on MDR: 2024-08-05 12:30:31 +0900