Tunable Spike-Timing-Dependent Plasticity in Magnetic Skyrmion Manipulation Chambers

TL;DR

This paper introduces a skyrmion-based neuromorphic device that implements spike-timing-dependent plasticity through a three-chamber design, enabling adaptive learning by controlling skyrmion counts via current timing.

Contribution

It presents a novel three-chamber skyrmion device that encodes synaptic weights and demonstrates STDP behavior through micromagnetic simulations, advancing neuromorphic hardware design.

Findings

Device successfully mimics Hebbian plasticity.

Skyrmion count controlled by spike timing.

Device maintains state after writing.

Abstract

Magnetic skyrmions, as scalable and non-volatile spin textures, can dynamically interact with fields and currents, making them promising for unconventional computing. This paper presents a neuromorphic device based on skyrmion manipulation chambers to implement spike-timing-dependent plasticity (STDP), a mechanism for unsupervised learning in brain-inspired computing. STDP adjusts synaptic weights based on the timing of pre- and post-synaptic spikes. The proposed three-chamber design encodes synaptic weight in the number of skyrmions in the center chamber, with left and right chambers for pre-synaptic and post-synaptic spikes. Micromagnetic simulations demonstrate that the timing between applied currents across the chambers controls the final skyrmion count (weight). The device exhibits adaptability and learning capabilities by manipulating chamber parameters, mimicking Hebbian and dendritic location-based plasticity. The device's ability to maintain state post-write highlights its potential for advancing adaptable neuromorphic devices.