Biased field-free skyrmion-based neural network and reconfigurable logic operations

Abstract

Spin-texture based devices have recently gained significant attention for their potential in designing and developing logic circuits and neural networks, leading us to explore beyond conventional computing paradigms. Among various spin textures, magnetic skyrmions are found to be promising candidates due to their topological stability, nanoscale size, and low-current-driven dynamics. However, the practical realization of skyrmion-based devices remains challenging due to the reliance on external magnetic fields or electrostatic gating, which complicates device architecture and limits on-chip integration. In this study, we investigate spin current-driven, field-free skyrmion-induced spiking dynamics and logic operations in bilayer nanotrack consisting of a ferromagnetic layer interfaced with a heavy metal. We first investigate the nucleation dynamics of skyrmions and identify two distinct states: stable configuration and dynamical state exhibiting breathing mode oscillations. These oscillations give rise to regular periodic auto-spiking behavior in dynamical skyrmions with a tunable spiking frequency controlled by the applied spin-polarized current. Exploiting this behavior, we designed a skyrmion-based spiking neural network demonstrating a classification accuracy of 87.50% on the Modified National Institute of Standards and Testing handwritten digit dataset. Furthermore, we have also examined the dynamics of stable skyrmion in a device with stepped geometry which introduces a potential barrier and enables both AND and OR logic operations within the same device utilizing spin–orbit torque. This dual functionality of skyrmion-based device enabling both neuromorphic computing and versatile logic operations within a single device offers a promising pathway toward highly efficient, field-free spintronic devices.