Design of Spintronics-based Neuronal and Synaptic Devices for Spiking Neural Network Circuits

Abstract

Topologically stable magnetic skyrmion has a much lower depinning current density that may be useful for memory as well as neuromorphic computing. However, skyrmion-based devices suffer from the Magnus force originating from the skyrmion Hall effect, which may result in unwanted skyrmion annihilation if the magnitude of the driving current gets too large. A design of an artificial neuron and a synapse using a synthetic antiferromagnetically coupled bilayer device, which nullifies the Magnus force, is demonstrated in this work. The leak term in the artificial leaky integrate-and-fire neuron is achieved by engineering the uniaxial anisotropy profile of the neuronal device. The synaptic device has a similar structure as the neuronal device but has a constant uniaxial anisotropy. The synaptic device also has a linear and symmetric weight update, which is a highly desirable trait of an artificial synapse. Neuronal and synaptic devices based on magnetic domain-wall (DW) motion are also studied and compared to skyrmionic devices. Our simulation results show the energy required to perform such operation in DW or skyrmion-based devices is on the order of a few fJ.