Granular Spintronics-based Reservoir Computing for Temporal Applications

This paper presents a novel approach to reservoir computing (RC) using Granular Vortex-Based Magnetic Tunnel Junctions (GV-MTJs) for temporal applications. GV-MTJs, with their unique magnetic domain configurations and granular structures, provide the necessary fading memory and non-linear dynamics essential for RC. The vortex core’s oscillatory motion within the device allows for temporal correlation of inputs, giving fading memory, while grain-induced non-linear resistance and frequency variations enhance data dimensionality. Our findings indicate that varying device parameters can affect the relaxation time and gyrotropic frequency in both simulation and experiments. Relaxation times range from 100-140 ns and frequencies from 250-100 MHz. Through experiments, the classification error was reduced by 27% for the best sample, others showed limited potential. Due to signal application speed constraints, the fading memory is not fully utilized. However, the inherent RC capabilities of GV-MTJs are validated. This paper highlights the promise of GV-MTJs in neuromorphic computing and suggests avenues for future research to optimise their use in practical applications.