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

Gartside, Jack Carter; Stenning, Kilian D.; Vanstone, Alex; Holder, Holly H.; Arroo, Daan M.; Dion, Troy; Caravelli, Francesco; Kurebayashi, Hidekazu; Branford, Will R.

doi:10.1038/s41565-022-01091-7

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

Journal Article · Thu May 05 00:00:00 EDT 2022 · Nature Nanotechnology

DOI:https://doi.org/10.1038/s41565-022-01091-7· OSTI ID:2282531

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Imperial College, London (United Kingdom)
Imperial College, London (United Kingdom); Kyushu Univ., Fukuoka (Japan)
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

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. Here, 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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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: 89233218CNA000001; AC52-06NA25396

OSTI ID:: 2282531

Report Number(s):: LA-UR-21-29487

Journal Information:: Nature Nanotechnology, Vol. 17, Issue 5; ISSN 1748-3387

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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

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