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説明:

Physical reservoir computing (PRC) is helpful for power reduction in machine learning technology, although the challenge is to improve computational performance. In this study, we developed a PRC device utilizing ion-electron coupled dynamics in an electric double layer transistor (EDLT) consisting of monolayer graphene channels and a Li+ conducting inorganic oxide thin film. The ambipolar transfer characteristics of graphene channels in the EDLT obtained complex and diverse drain current responses, providing high information processing capacity and high PRC performance in the nonlinear autoregressive moving average (NARMA) task.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: physical reservoir computing, ion-gating reservoir, information capacity analysis

刊行年月日: 2025-02-01

出版者: IOP Publishing

掲載誌:

• Applied Physics Express (ISSN: 18820786) vol. 18 issue. 2 024501

研究助成金:

• JFE 21st Century Foundation
• Ministry of Education, Culture, Sports, Science and Technology JPMXP1224NM5236
• Precursory Research for Embryonic Science and Technology JPMJPR23H4
• Japan Society for the Promotion of Science JP24KJ0229

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.35848/1882-0786/adb19b

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更新時刻: 2025-12-11 12:30:03 +0900

MDRでの公開時刻: 2025-02-25 16:30:30 +0900