Engineering and exploiting self-driven domain wall motion in ferrimagnets for neuromorphic computing applications

TL;DR

This paper demonstrates how engineered ferrimagnet materials can enable self-driven domain wall motion, facilitating energy-efficient neuromorphic computing functionalities like leaky integration and passive reset, with tunable speed and behavior.

Contribution

It introduces a novel approach to achieve neuromorphic functionalities using locally engineered ferrimagnets with minimal complexity, combining experiments and simulations.

Findings

Tuning feature size and composition controls domain wall speed.

Integration with spin-orbit torque enables neuromorphic behaviors.

Ferrimagnets serve as scalable platforms for domain wall-based computing.

Abstract

Magnetic domain wall motion has recently garnered significant interest as a physical mechanism to enable energy-efficient, next-generation brain-inspired computing architectures. However, realizing all behaviors required for neuromorphic computing within standard material systems remains a significant challenge, as these functionalities often rely on competing interactions. Here, we demonstrate how spontaneous domain wall motion in response to locally engineered lateral exchange coupling in transition metal-rare earth ferrimagnets can be leveraged to achieve numerous neuromorphic computing functionalities in devices with minimal complexity. Through experiments and micromagnetic simulations, we show how tuning the feature size, material composition, and chiral interaction strength controls the speed of self-driven domain wall motion. When integrated with spin-orbit torque, this control gives rise to behaviors essential for neuromorphic computing, including leaky integration and passive resetting of artificial neuron potential. These results establish locally engineered ferrimagnets as a tunable, scalable, and straightforward platform for domain wall-based computing architectures.