Magnetic vortex writing and local reversal seeding in artificial spin-vortex ice via all-optical and surface-probe control

TL;DR

This paper introduces all-optical and surface-probe methods for local, field-free writing of vortex states in artificial spin-vortex ice, enabling programmable control over its magnetic configurations for advanced nanomagnetic applications.

Contribution

It demonstrates novel techniques for local vortex writing in ASVI using laser illumination and MFM tips, expanding control over its magnetic states and energy landscape.

Findings

Laser writing can locally create double-vortex textures.

MFM tip can write single vortex states.

Reconfigurable energy landscape enables programmable avalanche events.

Abstract

Artificial spin-vortex ice ('ASVI') is a reconfigurable nanomagnetic metamaterial consisting of magnetic nanoislands tailored to support both Ising macrospin and vortex textures. ASVI has recently shown functional applications including reconfigurable magnonics and neuromorphic computing, where the introduction of vortex textures broadens functionality beyond conventional artificial spin ice which generally supports macrospin states. However, local control of writing vortex states in ASVI remains an open challenge. Here we demonstrate techniques for field-free magnetic vortex writing in ASVI. We expand ASVI to support metastable macrospin, single-vortex and double-vortex states. All-optical writing via focused laser illumination can locally write double-vortex textures, and surface-probe writing using an MFM tip can locally write single vortex states. We leverage this writing to tailor and explore the reconfigurable energy landscape of ASVI, demonstrating programmable local seeding of avalanche-like reversal events. The global field-free texture selective writing techniques reported here expand the suite of nanomagnetic control techniques, with a host of future applications including fundamental studies of avalanche dynamics, physical memory, and direct writing of nanomagnetic 'weights' in physical neuromorphic neural networks.