Iono–Magnonic Reservoir Computing With Chaotic Spin Wave Interference Manipulated by Ion‐Gating
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 ha...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
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| Series: | Advanced Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/advs.202411777 |
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| Summary: | 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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| ISSN: | 2198-3844 |