Stochastic Domain Wall Dynamics for Machine Learning

Abstract

Magnetic materials are widely used for long-term data storage but recent advancements have increased their potential as both working memory and computing architectures. In particular, devices based on magnetic domain walls (DWs) have been shown to be able to perform logic operations and can readily store information. However, the stochasticity of DW pinning limits the feasibility of creating technologically viable devices. Here,we demonstrate how stochasticity can be changed from a technologically inhibitive behaviour into a functional property by exploiting it to implement machine learning algorithms that could be used in specialised neuromorphic devices.