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Brownian reservoir computing realized using geometrically confined skyrmion dynamics

Author

Listed:
  • Klaus Raab

    (Johannes Gutenberg-Universität Mainz)

  • Maarten A. Brems

    (Johannes Gutenberg-Universität Mainz)

  • Grischa Beneke

    (Johannes Gutenberg-Universität Mainz)

  • Takaaki Dohi

    (Johannes Gutenberg-Universität Mainz)

  • Jan Rothörl

    (Johannes Gutenberg-Universität Mainz)

  • Fabian Kammerbauer

    (Johannes Gutenberg-Universität Mainz)

  • Johan H. Mentink

    (Radboud University, Institute for Molecules and Materials)

  • Mathias Kläui

    (Johannes Gutenberg-Universität Mainz
    Graduate School of Excellence Materials Science in Mainz)

Abstract

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.

Suggested Citation

  • Klaus Raab & Maarten A. Brems & Grischa Beneke & Takaaki Dohi & Jan Rothörl & Fabian Kammerbauer & Johan H. Mentink & Mathias Kläui, 2022. "Brownian reservoir computing realized using geometrically confined skyrmion dynamics," Nature Communications, Nature, vol. 13(1), pages 1-6, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34309-2
    DOI: 10.1038/s41467-022-34309-2
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    References listed on IDEAS

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    1. Jacob Torrejon & Mathieu Riou & Flavio Abreu Araujo & Sumito Tsunegi & Guru Khalsa & Damien Querlioz & Paolo Bortolotti & Vincent Cros & Kay Yakushiji & Akio Fukushima & Hitoshi Kubota & Shinji Yuasa , 2017. "Neuromorphic computing with nanoscale spintronic oscillators," Nature, Nature, vol. 547(7664), pages 428-431, July.
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    Cited by:

    1. Oleksii M. Volkov & Oleksandr V. Pylypovskyi & Fabrizio Porrati & Florian Kronast & Jose A. Fernandez-Roldan & Attila Kákay & Alexander Kuprava & Sven Barth & Filipp N. Rybakov & Olle Eriksson & Sebas, 2024. "Three-dimensional magnetic nanotextures with high-order vorticity in soft magnetic wireframes," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Takaaki Dohi & Markus Weißenhofer & Nico Kerber & Fabian Kammerbauer & Yuqing Ge & Klaus Raab & Jakub Zázvorka & Maria-Andromachi Syskaki & Aga Shahee & Moritz Ruhwedel & Tobias Böttcher & Philipp Pir, 2023. "Enhanced thermally-activated skyrmion diffusion with tunable effective gyrotropic force," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    3. Yiming Sun & Tao Lin & Na Lei & Xing Chen & Wang Kang & Zhiyuan Zhao & Dahai Wei & Chao Chen & Simin Pang & Linglong Hu & Liu Yang & Enxuan Dong & Li Zhao & Lei Liu & Zhe Yuan & Aladin Ullrich & Chris, 2023. "Experimental demonstration of a skyrmion-enhanced strain-mediated physical reservoir computing system," Nature Communications, Nature, vol. 14(1), pages 1-10, December.

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