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Brownian reservoir computing realized using geometrically confined skyrmion dynamics
Reservoir computing (RC) has been considered as one of the key computational principles beyond von-Neumann computing. Magnetic skyrmions, topological particle-like spin textures in magnetic films are particularly promising for implementing RC, since they respond strongly nonlinearly to external stim...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9666653/ https://www.ncbi.nlm.nih.gov/pubmed/36379941 http://dx.doi.org/10.1038/s41467-022-34309-2 |
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author | Raab, Klaus Brems, Maarten A. Beneke, Grischa Dohi, Takaaki Rothörl, Jan Kammerbauer, Fabian Mentink, Johan H. Kläui, Mathias |
author_facet | Raab, Klaus Brems, Maarten A. Beneke, Grischa Dohi, Takaaki Rothörl, Jan Kammerbauer, Fabian Mentink, Johan H. Kläui, Mathias |
author_sort | Raab, Klaus |
collection | PubMed |
description | Reservoir computing (RC) has been considered as one of the key computational principles beyond von-Neumann computing. Magnetic skyrmions, topological particle-like spin textures in magnetic films are particularly promising for implementing RC, since they respond strongly nonlinearly to external stimuli and feature inherent multiscale dynamics. However, despite several theoretical proposals that exist for skyrmion reservoir computing, experimental realizations have been elusive until now. Here, we propose and experimentally demonstrate a conceptually new approach to skyrmion RC that leverages the thermally activated diffusive motion of skyrmions. By confining the electrically gated and thermal skyrmion motion, we find that already a single skyrmion in a confined geometry suffices to realize nonlinearly separable functions, which we demonstrate for the XOR gate along with all other Boolean logic gate operations. Besides this universality, the reservoir computing concept ensures low training costs and ultra-low power operation with current densities orders of magnitude smaller than those used in existing spintronic reservoir computing demonstrations. Our proposed concept is robust against device imperfections and can be readily extended by linking multiple confined geometries and/or by including more skyrmions in the reservoir, suggesting high potential for scalable and low-energy reservoir computing. |
format | Online Article Text |
id | pubmed-9666653 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96666532022-11-17 Brownian reservoir computing realized using geometrically confined skyrmion dynamics Raab, Klaus Brems, Maarten A. Beneke, Grischa Dohi, Takaaki Rothörl, Jan Kammerbauer, Fabian Mentink, Johan H. Kläui, Mathias Nat Commun Article Reservoir computing (RC) has been considered as one of the key computational principles beyond von-Neumann computing. Magnetic skyrmions, topological particle-like spin textures in magnetic films are particularly promising for implementing RC, since they respond strongly nonlinearly to external stimuli and feature inherent multiscale dynamics. However, despite several theoretical proposals that exist for skyrmion reservoir computing, experimental realizations have been elusive until now. Here, we propose and experimentally demonstrate a conceptually new approach to skyrmion RC that leverages the thermally activated diffusive motion of skyrmions. By confining the electrically gated and thermal skyrmion motion, we find that already a single skyrmion in a confined geometry suffices to realize nonlinearly separable functions, which we demonstrate for the XOR gate along with all other Boolean logic gate operations. Besides this universality, the reservoir computing concept ensures low training costs and ultra-low power operation with current densities orders of magnitude smaller than those used in existing spintronic reservoir computing demonstrations. Our proposed concept is robust against device imperfections and can be readily extended by linking multiple confined geometries and/or by including more skyrmions in the reservoir, suggesting high potential for scalable and low-energy reservoir computing. Nature Publishing Group UK 2022-11-15 /pmc/articles/PMC9666653/ /pubmed/36379941 http://dx.doi.org/10.1038/s41467-022-34309-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Raab, Klaus Brems, Maarten A. Beneke, Grischa Dohi, Takaaki Rothörl, Jan Kammerbauer, Fabian Mentink, Johan H. Kläui, Mathias Brownian reservoir computing realized using geometrically confined skyrmion dynamics |
title | Brownian reservoir computing realized using geometrically confined skyrmion dynamics |
title_full | Brownian reservoir computing realized using geometrically confined skyrmion dynamics |
title_fullStr | Brownian reservoir computing realized using geometrically confined skyrmion dynamics |
title_full_unstemmed | Brownian reservoir computing realized using geometrically confined skyrmion dynamics |
title_short | Brownian reservoir computing realized using geometrically confined skyrmion dynamics |
title_sort | brownian reservoir computing realized using geometrically confined skyrmion dynamics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9666653/ https://www.ncbi.nlm.nih.gov/pubmed/36379941 http://dx.doi.org/10.1038/s41467-022-34309-2 |
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