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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.

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• In Copyright
  

Keyword: Reservoir computing, Spin wave, Nonlinear interference, Light switching

Date published: 2024-05-22

Publisher: Elsevier BV

Journal:

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

Funding:

Manuscript type: Author's original (Submitted manuscript)

MDR DOI: https://doi.org/10.48505/nims.4622

First published URL: https://doi.org/10.1016/j.mtphys.2024.101465

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Updated at: 2024-08-05 12:30:41 +0900

Published on MDR: 2024-08-05 12:30:41 +0900