Linear Weight Update Synaptic Responses in Ferrimagnetic Neuromorphic Devices

Abstract

Ferrimagnetic materials with antiparallel exchange coupling, exhibit spin-orbit-torque-induced dynamics, offering an emerging platform for realizing neuromorphic devices, such as artificial synapses. However, the state-of-the-art artificial synapses based on ferrimagnet suffer from poor analog switching linearity, which serves as a bottleneck for achieving complex tasks with high accuracy in neuromorphic computing. Here, an artificial synapse is reported with high-weight update linearity in a compensated ferrimagnetic crossbar device. In particular, the linear weight update of the synapses is enhanced by engineering the current density distribution. Using experimentally derived device parameters, handwritten digit recognition can be achieved with an accuracy of over 95% in a three-layer fully connected artificial neural network. The work provides a universal method to improve the synaptic linearity, which also paves the way for applying the spin-orbit device in neuromorphic computing.