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説明:

Physical reservoir computing is a promising approach to realize high-performance artificial intelligence systems utilizing physical devices. Recently, it has been experimentally found that nonlinear interfered spin wave multidetection shows excellent performance for processing nonlinear time-series data due to its outstanding features: nonlinearity, short-term memory, and the ability to map in high dimensional space. However, said performance is considerably inferior to reservoir computing utilizing an optical circuit with a large volume. Herein, we develop reservoir computing with nonlinear interfered spin wave coupled with light switching, namely optomagnonic reservoir computing. The spin wave was modulated through a crystal field transition that occurred in two different Fe3+ sites of Y3Fe5O12 by visible light switching, and it was found that the spin wave modulated by visible light switching dramatically reduced normalized mean square errors to 4.96 × 10-3, 0.163, and 3.66 × 10-5 for NARMA2, NARMA10, and second-order nonlinear dynamical equation tasks. Said excellent performance results from the strong nonlinearity caused by chaos and large memory capacity induced by reservoir states diversified by visible light switching.

権利情報:

• In Copyright
  

キーワード: Reservoir computing, Spin wave, Nonlinear interference, Light switching

刊行年月日: 2024-05-22

出版者: Elsevier BV

掲載誌:

• Materials Today Physics (ISSN: 25425293) vol. 45 101465

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.4622

公開URL: https://doi.org/10.1016/j.mtphys.2024.101465

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更新時刻: 2024-08-05 12:30:41 +0900

MDRでの公開時刻: 2024-08-05 12:30:41 +0900