Bindal Namita, Ian Calvin Ang Chin, Lew Wen Siang, Kaushik Brajesh Kumar
Department of Electronics and Communication Engineering, Indian Institute of Technology, Roorkee, 247667, India.
School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
Nanotechnology. 2021 Mar 4;32(21). doi: 10.1088/1361-6528/abe261.
Magnetic skyrmions are potential candidates for neuromorphic computing due to their inherent topologically stable particle-like behavior, low driving current density, and nanoscale size. Antiferromagnetic skyrmions are favored as they can be driven parallel to in-plane electrical currents as opposed to ferromagnetic skyrmions which exhibit the skyrmion Hall effect and eventually cause their annihilation at the edge of nanotracks. In this paper, an antiferromagnetic skyrmion based artificial neuron device consisting of a magnetic anisotropy barrier on a nanotrack is proposed. It exploits inter-skyrmion repulsion, mimicking the integrate-fire (IF) functionality of a biological neuron. The device threshold represented by the maximum number of skyrmions that can be pinned by the barrier can be tuned based on the particular current density employed on the nanotrack. The corresponding neuron spiking event occurs when a skyrmion overcomes the barrier. By raising the device threshold, lowering the barrier width and height, the operating current density of the device can be decreased to further enhance its energy efficiency. The proposed device paves the way for developing energy-efficient neuromorphic computing in antiferromagnetic spintronics.
由于其固有的拓扑稳定的类粒子行为、低驱动电流密度和纳米级尺寸,磁斯格明子是神经形态计算的潜在候选者。反铁磁斯格明子更受青睐,因为它们可以平行于面内电流驱动,而铁磁斯格明子会表现出斯格明子霍尔效应并最终在纳米轨道边缘导致其湮灭。本文提出了一种基于反铁磁斯格明子的人工神经元器件,该器件由纳米轨道上的磁各向异性势垒组成。它利用斯格明子间的排斥力,模拟生物神经元的积分-发放(IF)功能。由势垒可钉扎的最大斯格明子数表示的器件阈值可根据纳米轨道上采用的特定电流密度进行调节。当一个斯格明子克服势垒时,相应的神经元尖峰事件就会发生。通过提高器件阈值、降低势垒宽度和高度,可以降低器件的工作电流密度,从而进一步提高其能量效率。所提出的器件为在反铁磁自旋电子学中开发节能神经形态计算铺平了道路。