AM|基于铁磁/反铁磁异质结的神经元与突触特性模拟

文章链接：https://onlinelibrary.wiley.com/doi/full/10.1002/adma.201900636

摘要

人类大脑中有效的信息处理是通过神经元和突触的动力学来实现的，从而激发了人工尖峰神经网络的有效实施。本文研究了反铁磁体/铁磁体异质结构中自旋轨道转矩转换的动力学，以证明该材料系统具有形成异步尖峰神经网络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开关，被当作脉冲宽度(1 s到1 ns)、振幅、数目和脉冲到脉冲间隔的函数来检查。基于这一动力学和系统的独特的能力表现出二进制或模拟行为根据设备大小,关键功能的神经元突触(spike-timing-dependent可塑性)和(漏integrate-and-fire)复制相同的材料,相同的工作原理的基础上。这些结果为基于自旋式神经形态硬件的研究开辟了道路，该硬件可以像人类大脑一样高效地执行认知任务。

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.