Laser-written reconfigurable energy landscapes and programmable Moir\'e spin textures

TL;DR

This paper presents a laser-assisted method for creating reconfigurable, nanoscale magnetic textures in thin films, enabling programmable energy landscapes and artificial Moiré spin patterns for advanced spintronic applications.

Contribution

It introduces a scalable, non-contact laser technique to non-destructively control magnetic energy landscapes with nanoscale precision, allowing reprogrammable spin textures and artificial lattices.

Findings

Demonstrated grayscale, reprogrammable control of magnetic energy profiles.

Created magnetic patterns with tunable hysteresis and switching thresholds.

Realized artificial Moiré spin textures through geometric superposition.

Abstract

Magnetic textures are central to emerging spintronic and unconventional computing technologies due to their rich dynamics, topological properties and nanoscale dimensions. A major challenge remains achieving tunable, reversible, and spatially resolved control over these textures and their evolution as a function of external stimuli, by spatially reprogramming the magnetic energy landscape that governs their nucleation and stability. Here, we exploit a focused laser-assisted local field cooling technique that establishes a fast, non-contact and scalable platform for grayscale spin texture engineering. By non-destructively controlling the exchange-bias anisotropy with nanoscale resolution in thin-film heterostructures, this approach enables grayscale, reprogrammable control of the local energy profile, which we use to create magnetic patterns with highly controlled hysteresis, field-dependent readability and tunable switching thresholds. Leveraging this capability, we demonstrate information encoding with magnetic field-gated readability, and artificial spin metamaterials, stabilizing spin lattices with field-reconfigurable symmetries and creating artificial Moir\'e spin textures via the geometric superposition of twisted magnetic potentials. These results establish a versatile, reprogrammable platform that bridges the gap between application-oriented magnetic memory and fundamental studies of emergent order in artificial lattices.