Skyrmion-Magnetic Tunnel Junction Synapse with Mixed Synaptic Plasticity for Neuromorphic Computing

TL;DR

This paper introduces a skyrmion-based magnetic tunnel junction synapse with mixed synaptic plasticity, enabling neuromorphic computing with adaptive learning, image recognition, and dynamic environment processing.

Contribution

It proposes a novel skyrmion-based MTJ device with controllable long- and short-term plasticity for neuromorphic applications, demonstrating improved inference accuracy and adaptability.

Findings

Achieved 90% inference accuracy with increased skyrmion density.

Demonstrated static image recognition and dynamic environment learning.

Controlled plasticity via magnetic field, SOT, and VCMA mechanisms.

Abstract

Magnetic skyrmion-based data storage and unconventional computing devices have gained increasing attention due to their topological protection, small size, and low driving current. However, skyrmion creation, deletion, and motion are still being studied. In this study, we propose a skyrmion-based neuromorphic magnetic tunnel junction (MTJ) device with both long- and short-term plasticity (LTP and STP) (mixed synaptic plasticity). We showed that plasticity could be controlled by magnetic field, spin-orbit torque (SOT), and the voltage-controlled magnetic anisotropy (VCMA) switching mechanism. LTP depends on the skyrmion density and is manipulated by the SOT and magnetic field while STP is controlled by the VCMA. The LTP property of the device was utilized for static image recognition. By incorporating the STP feature, the device gained additional temporal filtering ability and could adapt to a dynamic environment. The skyrmions were conserved and confined to a nanotrack to minimize the skyrmion nucleation energy. The synapse device was trained and tested for emulating a deep neural network. We observed that when the skyrmion density was increased, the inference accuracy improved: 90% accuracy was achieved by the system at the highest density. We further demonstrated the dynamic environment learning and inference capabilities of the proposed device.