Voltage control of domain walls in magnetic nanowires for energy efficient neuromorphic devices

TL;DR

This paper explores voltage-controlled domain wall devices in magnetic nanowires for energy-efficient neuromorphic computing, demonstrating potential for scalable, real-time learning neural networks using micromagnetic modeling.

Contribution

It introduces a novel voltage-controlled domain wall device architecture for neuromorphic applications, combining spin torques and strain control to program synaptic weights.

Findings

Successful micromagnetic modeling of domain wall control at room temperature.

Feasibility of scaling devices despite thermal noise.

Potential integration with CMOS for energy-efficient neural networks.

Abstract

An energy-efficient voltage controlled domain wall device for implementing an artificial neuron and synapse is analyzed using micromagnetic modeling in the presence of room temperature thermal noise. By controlling the domain wall motion utilizing spin transfer or spin orbit torques in association with voltage generated strain control of perpendicular magnetic anisotropy in the presence of Dzyaloshinskii-Moriya interaction, different positions of the domain wall are realized in the free layer of a magnetic tunnel junction to program different synaptic weights. The feasibility of scaling of such devices is assessed in the presence of thermal perturbations that compromise controllability. Additionally, an artificial neuron can be realized by combining this DW device with a CMOS buffer. This provides a possible pathway to realize energy efficient voltage controlled nanomagnetic deep neural networks that can learn in real time.