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Reconfigurable training and reservoir computing in an artificial spin-vortex ice via spin-wave fingerprinting

Nature Nanotechnology volume 17, pages 460–469 (2022)Cite this article

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Abstract

Strongly interacting artificial spin systems are moving beyond mimicking naturally occurring materials to emerge as versatile functional platforms, from reconfigurable magnonics to neuromorphic computing. Typically, artificial spin systems comprise nanomagnets with a single magnetization texture: collinear macrospins or chiral vortices. By tuning nanoarray dimensions we have achieved macrospin–vortex bistability and demonstrated a four-state metamaterial spin system, the ‘artificial spin-vortex ice’ (ASVI). ASVI can host Ising-like macrospins with strong ice-like vertex interactions and weakly coupled vortices with low stray dipolar field. Vortices and macrospins exhibit starkly differing spin-wave spectra with analogue mode amplitude control and mode frequency shifts of Δf = 3.8 GHz. The enhanced bitextural microstate space gives rise to emergent physical memory phenomena, with ratchet-like vortex injection and history-dependent non-linear fading memory when driven through global magnetic field cycles. We employed spin-wave microstate fingerprinting for rapid, scalable readout of vortex and macrospin populations, and leveraged this for spin-wave reservoir computation. ASVI performs non-linear mapping transformations of diverse input and target signals in addition to chaotic time-series forecasting.

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Fig. 1: Artificial spin-vortex ice.
Fig. 2: Reconfigurably directed vortex evolution and spin-wave spectra.
Fig. 3: Simulated spatial magnon mode-power maps and heatmap.
Fig. 4: Vortex-to-macrospin conversion and complex field-cycling sequences.
Fig. 5: ASVI reservoir time-series transformation and prediction.

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Data availability

The datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request.

Code availability

The code used in this study is available from the corresponding author upon reasonable request.

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Acknowledgements

W.R.B. and J.C.G. were supported by the Leverhulme Trust (RPG-2017-257 to W.R.B.). A.V. was supported by the EPSRC Centre for Doctoral Training in Advanced Characterisation of Materials (grant no. EP/L015277/1). T.D. was supported through a International Research Fellow of the Japan Society for the Promotion of Science (Postdoctoral Fellowships for Research in Japan). The work of F.C. was carried out under the auspices of the NNSA of the US DOE at LANL under contract no. DE-AC52-06NA25396, and with the economic support of the LDRD (grant no. PRD20190195). Simulations were performed at the Imperial College London Research Computing Service (https://doi.org/10.14469/hpc/2232). We thank L. F. Cohen of Imperial College London for enlightening discussion and comments, and D. Mack for excellent laboratory management. We thank B. Rogers, K. Everschor-Sitte and J. Love for valuable discussions regarding the reservoir computation scheme.

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Author notes

  1. These authors contributed equally: Jack C. Gartside, Kilian D. Stenning, Alex Vanstone.

Authors and Affiliations

  1. Blackett Laboratory, Imperial College London, London, UK

    Jack C. Gartside, Kilian D. Stenning, Alex Vanstone, Holly H. Holder & Will R. Branford

  2. Department of Materials, Imperial College London, London, UK

    Daan M. Arroo

  3. London Centre for Nanotechnology, Imperial College London, London, UK

    Daan M. Arroo & Will R. Branford

  4. London Centre for Nanotechnology, University College London, London, UK

    Troy Dion & Hidekazu Kurebayashi

  5. Solid State Physics Lab., Kyushu University, Fukuoka, Japan

    Troy Dion

  6. Theoretical Division (T4), Los Alamos National Laboratory, Los Alamos, NM, USA

    Francesco Caravelli

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Contributions

J.C.G., K.D.S. and A.V. conceived the work. J.C.G. drafted the manuscript, other than the reservoir computation section, with contributions from all authors in the editing and revision stages. K.D.S. drafted the reservoir computation section with editing contributions from J.C.G. J.C.G., K.D.S. and A.V. performed the FMR measurements. J.C.G. and H.H.H. performed the MFM measurements. J.C.G. and K.D.S. fabricated the ASVI. A.V. performed CAD design of the structures. A.V. and J.C.G. performed MOKE measurements of coercive fields. K.D.S. implemented the reservoir computation scheme. T.D. wrote the code for the simulation of the magnon spectra and performed micromagnetic simulations of mode dispersion relations and spatial mode profiles. T.D. performed mode character analysis and identification. D.M.A. wrote the code for the simulation of the magnon spectra. F.C. provided valuable insight into the direction of the reservoir computation scheme. H.K. contributed the analysis of spin-wave dynamics. W.R.B. oversaw the project and provided critical feedback and direction throughout.

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Correspondence to Jack C. Gartside.

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Gartside, J.C., Stenning, K.D., Vanstone, A. et al. Reconfigurable training and reservoir computing in an artificial spin-vortex ice via spin-wave fingerprinting. Nat. Nanotechnol. 17, 460–469 (2022). https://doi.org/10.1038/s41565-022-01091-7

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