Spin-wave reservoir chips with short-term memory for high-speed estimation of external magnetic fields

Harnessing spin waves for high-speed computing: In this work an innovative spin-wave reservoir chip, utilizing ferromagnetic permalloy thin films, demonstrates exceptional capabilities. By strategically manipulating spin-wave interference, the authors achieve a multi-input--multi-output reservoir capable of memory retention, nonlinearity enhancement, and accurate magnetic field estimation. This spintronic hardware paves the way for high-speed applications in reservoir computing and signal processing.

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Publication Details

Journal
Physical Review Applied
Published
2024-08-29
DOI
https://doi.org/10.1103/physrevapplied.22.024072
Citations
7
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
2.43

Funders

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article

Spin-wave reservoir chips with short-term memory for high-speed estimation of external magnetic fields

Shoki Nezu, Koji Sekiguchi, Sho Nagase
7 citations
Physical Review Applied
Neural Networks and Reservoir Computing
2.43
article

Spin-wave reservoir chips with short-term memory for high-speed estimation of external magnetic fields

Shoki Nezu, Koji Sekiguchi, Sho Nagase
article en
7 citations

Abstract

Harnessing spin waves for high-speed computing: In this work an innovative spin-wave reservoir chip, utilizing ferromagnetic permalloy thin films, demonstrates exceptional capabilities. By strategically manipulating spin-wave interference, the authors achieve a multi-input--multi-output reservoir capable of memory retention, nonlinearity enhancement, and accurate magnetic field estimation. This spintronic hardware paves the way for high-speed applications in reservoir computing and signal processing.

Physical Review AppliedVol. 22(2)
Yokohama National University (JP)
Japan Society for the Promotion of Science London, Japan Society for the Promotion of Science
Affordable and clean energy
Openalex Percentile: Top 10%
Neural Networks and Reservoir Computing
2.43
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