ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Iono-Magnonic Reservoir Computing With Chaotic Spin Wave Interference Manipulated by Ion-Gating.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Applied spintronics: From spin transport to spin-charge interconversion and emerging devices.Science advances · 2026Review
- Memristive Physical Reservoir Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Two Orders of Magnitude Reduction in Computational Load Achieved by Ultrawideband Responses of an Ion-Gating Reservoir.ACS nano · 2025Article
- Magneto-Ionic Physical Reservoir Computing in Perpendicularly Magnetized Heterostructures.Nano letters · 2025Article
- Iono-Magnonic Reservoir Computing With Chaotic Spin Wave Interference Manipulated by Ion-Gating.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
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.
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Registered trials
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