Reconfigurable Training, Vortex Writing and Spin-Wave Fingerprinting in an Artificial Spin-Vortex Ice

Abstract

Strongly-interacting artificial spin systems are moving beyond mimicking naturally-occurring materials to find roles as versatile functional platforms, from reconfigurable magnonics to designer magnetic metamaterials. Typically artificial spin systems comprise nanomagnets with a single magnetisation texture: collinear macrospins or chiral vortices. By tuning nanoarray dimensions we achieve macrospin/vortex bistability and demonstrate a four-state metamaterial spin-system ‘Artificial Spin-Vortex Ice’ (ASVI). ASVI is capable of adopting Ising-like macrospins with strong ice-like vertex interactions, in addition to weakly-coupled vortices with low stray dipolar-field. The enhanced bi-texture microstate space gives rise to emergent physical memory phenomena, with ratchet-like vortex training and history-dependent nonlinear training dynamics. We observe vortex-domain formation alongside MFM tip vortex-writing. Tip-written vortices dramatically alter local reversal and memory dynamics. Vortices and macrospins exhibit starkly-differing spin-wave spectra with analogue-style mode-amplitude control via vortex training and mode-frequency shifts of ∆f = 3.8 GHz. We leverage spin-wave ‘spectral fingerprinting’ for rapid, scaleable readout of vortex and macrospin populations over complex training-protocols with applicability for functional magnonics and physical memory.

Spectral fingerprinting: microstate readout via remanence ferromagnetic resonance in artificial spin ice

“…Our demonstration here has concentrated on ASI, spectral fingerprinting is ideally suited across a range of interacting nanomagnetic systems particularly the burgeoning range of 3D artificial spin systems where single macrospin imaging is inherently much harder [28][29][30]. Additionally, spectral fingerprinting is an attractive state readout solution for recent neuromorphic and wave computation schemes harnessing the vast set of microstate spaces for next-generation computing [4][5][6][7], with it already having been demonstrated for reservoir computing [31].…”

“…Rapid, low-energy microstate-readout solutions are crucial to the progression of such neuromorphic computation hardware. The spectral fingerprinting approach described here is ideally matched to these tasks, with an experimental demonstration of ASI reservoir computation enabled FMR spectroscopy detailed in Gartside et al [31].…”