Engineering and Exploiting Self-Driven Domain Wall Motion in Ferrimagnets for Neuromorphic Computing Applications.

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 current-induced spin-orbit torques, 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.