Yoshitaka Shingaya
(National Institute for Materials Science)
;
Daiki Nishioka
(National Institute for Materials Science)
;
Kazuya Terabe
(National Institute for Materials Science)
;
Takashi Tsuchiya
(National Institute for Materials Science)
説明:
(abstract)Reservoir computing (RC) is promising for achieving low power consumption neuromorphic devices. In this study, we have developed an all-solid-state electric double layer (EDL) transistor using multilayer MoS2 to realize high-performance physical reservoir computing (PRC). We have demonstrated high-performance of MoS2-based Raman-ion-gating reservoir (MoS2-RIGR), in which gate voltage-dependent resonant Raman scattering spectra of MoS2 were used as computational resources in addition to drain and gate current responses. Our device achieved good performance such as >97% accuracy in various nonlinear waveform transformations and 97.8% accuracy in solving a second-order nonlinear dynamic equation. Information processing capacity was evaluated to elucidate the origin of the high performance of our system.
権利情報:
This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Yoshitaka Shingaya, Daiki Nishioka, Kazuya Terabe, Takashi Tsuchiya; Enhanced computing performance of MoS2-based Raman-ion-gating reservoir achieved by combining reservoir states from current response and resonant Raman scattering. Appl. Phys. Lett. 27 October 2025; 127 (17): 173503 and may be found at https://doi.org/10.1063/5.0266816.
キーワード: physical reservoir computing, resonant Raman scattering, MoS2, electric double layer transistor, Raman-ion-gating reservoir
刊行年月日: 2025-10-23
出版者: AIP Publishing
掲載誌:
研究助成金:
原稿種別: 著者最終稿 (Accepted manuscript)
MDR DOI: https://doi.org/10.48505/nims.5909
公開URL: https://doi.org/10.1063/5.0266816
関連資料:
その他の識別子:
連絡先:
更新時刻: 2025-11-17 12:30:04 +0900
MDRでの公開時刻: 2025-11-17 12:23:04 +0900
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MoS2 reservoir_Manuscript_clean_version.docx
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application/vnd.openxmlformats-officedocument.wordprocessingml.document |
サイズ | 1.18MB | 詳細 |