All-electric mimicking synaptic plasticity based on the noncollinear antiferromagnetic device

TL;DR

This paper demonstrates an all-electric, energy-efficient spin-orbit torque device based on noncollinear antiferromagnetic Mn3Pt that emulates synaptic plasticity and achieves high-accuracy handwritten digit recognition, advancing neuromorphic computing.

Contribution

It introduces a novel Mn3Pt-based SOT device capable of multi-state synaptic emulation and demonstrates its application in neural network training with high accuracy.

Findings

Achieves nonvolatile multi-state modulation via all-electric SOT switching.

Successfully emulates key synaptic behaviors like EPSP, IPSP, LTD, and LTP.

Neural network trained with this device reaches 94.95% accuracy in digit recognition.

Abstract

Neuromorphic computing, which seeks to replicate the brain's ability to process information, has garnered significant attention due to its potential to achieve brain-like computing efficiency and human cognitive intelligence. Spin-orbit torque (SOT) devices can be used to simulate artificial synapses with non-volatile, high-speed processing and endurance characteristics. Nevertheless, achieving energy-efficient all-electric synaptic plasticity emulation using SOT devices remains a challenge. We chose the noncollinear antiferromagnetic Mn3Pt as spin source to fabricate the Mn3Pt-based SOT device, leveraging its unconventional spin current resulting from magnetic space breaking. By adjusting the amplitude, duration, and number of pulsed currents, the Mn3Pt-based SOT device achieves nonvolatile multi-state modulated by all-electric SOT switching, enabling emulate synaptic behaviors like excitatory postsynaptic potential (EPSP), inhibitory postsynaptic potential (IPSP), long-term depression (LTD) and the long-term potentiation (LTP) process. In addition, we show the successful training of an artificial neural network based on such SOT device in recognizing handwritten digits with a high recognition accuracy of 94.95 %, which is only slightly lower than that from simulations (98.04 %). These findings suggest that the Mn3Pt-based SOT device is a promising candidate for the implementation of memristor-based brain-inspired computing systems.