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利用自旋轨道扭矩开关动力学在反铁磁体/铁磁体异质结构中实现的人工神经元和突触

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

作者信息

Kurenkov Aleksandr, DuttaGupta Samik, Zhang Chaoliang, Fukami Shunsuke, Horio Yoshihiko, Ohno Hideo

机构信息

Laboratory for Nanoelectronics and Spintronics, Research Institute of Electrical Communication, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577, Japan.

Center for Science and Innovation in Spintronics (Core Research Cluster), Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577, Japan.

出版信息

Adv Mater. 2019 Jun;31(23):e1900636. doi: 10.1002/adma.201900636. Epub 2019 Apr 16.

Abstract

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.

摘要

人类大脑中高效的信息处理是通过神经元和突触的动力学实现的,这激发了人工脉冲神经网络的有效实现。在此,研究了反铁磁体/铁磁体异质结构中自旋轨道扭矩切换的动力学,以展示该材料系统形成用于异步脉冲神经网络的人工神经元和突触的能力。由单个电流脉冲或脉冲序列驱动的磁化切换,作为脉冲宽度(1秒至1纳秒)、幅度、数量和脉冲间间隔的函数进行研究。基于这种动力学以及该系统根据器件尺寸展现二元或模拟行为的独特能力,突触(脉冲时间依赖可塑性)和神经元(泄漏积分发放)的关键功能在同一材料中基于相同工作原理得以重现。这些结果为基于自旋电子学的神经形态硬件开辟了一条道路,该硬件能够以人类大脑的效率执行认知任务。

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