Brownian reservoir computing realized using geometrically confined skyrmion dynamics

Klaus Raab, Maarten A. Brems, Grischa Beneke, Takaaki Dohi, Jan Rothörl, Fabian Kammerbauer, Johan H. Mentink () and Mathias Kläui ()
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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

Nature Communications, 2022, vol. 13, issue 1, 1-6

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-34309-2

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