Artificial Neuron and Synapse Realized in an Antiferromagnet/Ferromagnet Heterostructure Using Dynamics of Spin–Orbit Torque Switching

• Published in: Advanced materials (Weinheim) Vol. 31; no. 23; pp. e1900636 - n/a
• Main Authors: Kurenkov, Aleksandr, DuttaGupta, Samik, Zhang, Chaoliang, Fukami, Shunsuke, Horio, Yoshihiko, Ohno, Hideo
• Format: Journal Article
• Language: English
• Published: Germany Wiley Subscription Services, Inc 01.06.2019
• Subjects: Antiferromagnetism; antiferromagnets; Brain; Cognitive tasks; Data processing; Dynamics; Ferromagnetism; Heterostructures; Materials science; Neural networks; Neurons; Orbital mechanics; Pulse duration; Spiking; Spin dynamics; Spintronics; Switching theory; Synapses; Torque; spintronics; antiferromagnets; synapses; neurons; neural networks
• ISSN: 0935-9648; 1521-4095
• DOI: 10.1002/adma.201900636

Efficient information processing in the human brain is achieved by dynamics of neurons and synapses, motivating effective implementation of artificial spiking neural networks. Here, the dynamics of spin–orbit torque switching in antiferromagnet/ferromagnet heterostructures is studied to show the capability of the material system to form artificial neurons and synapses for asynchronous spiking neural networks. The magnetization switching, driven by a single current pulse or trains of pulses, is examined as a function of the pulse width (1 s to 1 ns), amplitude, number, and pulse‐to‐pulse interval. Based on this dynamics and the unique ability of the system to exhibit binary or analog behavior depending on the device size, key functionalities of a synapse (spike‐timing‐dependent plasticity) and a neuron (leaky integrate‐and‐fire) are reproduced in the same material and on the basis of the same working principle. These results open a way toward spintronics‐based neuromorphic hardware that executes cognitive tasks with the efficiency of the human brain. Control of spintronics‐based binary and analog devices by pulses down to 1 ns and its applications are studied. It is found that the response of the binary device reproduces the behavior of a biological neuron while the analog device responds like a synapse. This is the first implementation of both elements based on the same material and working principle.