Wataru Namiki
(National Institute for Materials Science)
;
Daiki Nishioka
(National Institute for Materials Science)
;
Takashi Tsuchiya
(National Institute for Materials Science)
;
Tohru Higuchi
;
Kazuya Terabe
(National Institute for Materials Science)
説明:
(abstract)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.
権利情報:
キーワード: Reservoir computing, Magnetic property tuning, Planar Hall effect, Redox, Solid-state electrolyte, Lithium ion
刊行年月日: 2024-04-17
出版者: American Chemical Society (ACS)
掲載誌:
研究助成金:
原稿種別: 査読前原稿 (Author's original)
MDR DOI: https://doi.org/10.48505/nims.4620
公開URL: https://doi.org/10.1021/acs.nanolett.3c05029
関連資料:
その他の識別子:
連絡先:
更新時刻: 2024-08-05 12:30:30 +0900
MDRでの公開時刻: 2024-08-05 12:30:31 +0900
| ファイル名 | サイズ | |||
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20230724_manuscript_Namiki et al_submittedver.pdf
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サイズ | 2.38MB | 詳細 |