Iono-Magnonic Reservoir Computing With Chaotic Spin Wave Interference Manipulated by Ion-Gating

Physical reservoirs are a promising approach for realizing high-performance artificial intelligence devices utilizing physical devices. Although nonlinear interfered spin-wave multi-detection exhibits high nonlinearity and the ability to map in high dimensional feature space, it does not have sufficient performance to process time-series data precisely. Herein, development of an iono-magnonic reservoir by combining such interfered spin wave multi-detection and ion-gating involving protonation-induced redox reaction triggered by the application of voltage is reported. This study is the first to report the manipulation of the propagating spin wave property by ion-gating and the application of the same to physical reservoir computing. The subject iono-magnonic reservoir can generate various reservoir states in a single homogenous medium by utilizing a spin wave property modulated by ion-gating. Utilizing the strong nonlinearity resulting from chaos, the reservoir shows good computational performance in completing the Mackey-Glass chaotic time-series prediction task, and the performance is comparable to that exhibited by simulated neural networks.