Yu Ziyang, Shen Maokang, Zeng Zhongming, Liang Shiheng, Liu Yong, Chen Ming, Zhang Zhenhua, Lu Zhihong, You Long, Yang Xiaofei, Zhang Yue, Xiong Rui
Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University Wuhan 430072 P. R. China
School of Optical and Electronic Information, Huazhong University of Science and Technology Wuhan 430074 P. R. China
Nanoscale Adv. 2020 Feb 7;2(3):1309-1317. doi: 10.1039/d0na00009d. eCollection 2020 Mar 17.
Spintronics exhibits significant potential for a neuromorphic computing system with high speed, high integration density, and low dissipation. In this article, we propose an ultralow-dissipation skyrmion-based nanodevice composed of a synthetic antiferromagnet (SAF) and a piezoelectric substrate for neuromorphic computing. Skyrmions/skyrmion bubbles can be generated in the upper layer of an SAF with a weak anisotropy energy ( ). Applying a weak electric field on the heterostructure, interlayer antiferromagnetic coupling can be manipulated, giving rise to a continuous transition between a large skyrmion bubble and a small skyrmion. This thus induces a variation of the resistance of a magnetic tunneling junction that can mimic the potentiation/depression of a synapse and the leaky-integral-and-fire function of a neuron at a cost of a very low energy consumption of 0.3 fJ. These results pave a way to ultralow power neuromorphic computing applications.
自旋电子学在具有高速、高集成密度和低功耗的神经形态计算系统中展现出巨大潜力。在本文中,我们提出了一种基于超低功耗斯格明子的纳米器件,它由一个合成反铁磁体(SAF)和一个用于神经形态计算的压电衬底组成。在具有弱各向异性能量( )的SAF上层中可以产生斯格明子/斯格明子气泡。在异质结构上施加弱电场,可以操纵层间反铁磁耦合,从而在大斯格明子气泡和小斯格明子之间产生连续转变。这进而引起磁隧道结电阻的变化,该变化能够以0.3 fJ的极低能耗模拟突触的增强/抑制以及神经元的漏电积分发放功能。这些结果为超低功耗神经形态计算应用铺平了道路。