Voltage-Controlled Domain Wall Motion-Based Neuron and Stochastic Magnetic Tunnel Junction Synapse for Neuromorphic Computing Applications

IF 2 Q3 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE IEEE Journal on Exploratory Solid-State Computational Devices and Circuits Pub Date : 2021-12-23 DOI:10.1109/JXCDC.2021.3138038
Aijaz H. Lone;S. Amara;H. Fariborzi
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Abstract

This work discusses the proposal of a spintronic neuromorphic system with spin orbit torque-driven domain wall motion (DWM)-based neurons and synapses. We propose a voltage-controlled magnetic anisotropy DWM-based magnetic tunnel junction (MTJ) neuron. We investigate how the electric field at the gate (pinning site), generated by the voltage signals from pre-neurons, modulates the DWM, which reflects in the nonlinear switching behavior of neuron magnetization. For the implementation of synaptic weights, we propose a 3-terminal MTJ with stochastic DWM in the free layer. We incorporate intrinsic pinning effects by creating triangular notches on the sides of the free layer. The pinning of the domain wall and intrinsic thermal noise of the device lead to the stochastic behavior of DWM. The control of this stochasticity by the spin orbit torque is shown to realize the potentiation and depression of the synaptic weight. The micromagnetics and spin transport studies in synapses and neurons are carried out by developing a coupled micromagnetic non-equilibrium Green’s function ( MuMag-NEGF ) model. The minimization of the writing current pulsewidth by leveraging the thermal noise and demagnetization energy is also presented. Finally, we discuss the implementation of digit recognition by the proposed system using a spike time-dependent algorithm.
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基于电压控制畴壁运动的神经元和随机磁隧道连接突触在神经形态计算中的应用
这项工作讨论了基于自旋轨道扭矩驱动域壁运动(DWM)的神经元和突触的自旋电子神经形态系统的建议。我们提出了一种基于电压控制磁各向异性DWM的磁隧道结(MTJ)神经元。我们研究了由前神经元的电压信号产生的栅极(钉扎位点)电场如何调制DWM,这反映在神经元磁化的非线性切换行为中。为了实现突触权重,我们提出了一种在自由层具有随机DWM的3端MTJ。我们通过在自由层的侧面创建三角形缺口来结合固有的钉扎效应。畴壁的钉扎和器件的固有热噪声导致了DWM的随机行为。自旋轨道力矩对这种随机性的控制可以实现突触重量的增强和抑制。通过建立耦合微磁非平衡格林函数(MuMag-NEGF)模型,对突触和神经元的微磁学和自旋输运进行了研究。还提出了利用热噪声和消磁能量来最小化写入电流脉宽的方法。最后,我们讨论了所提出的系统使用尖峰相关算法来实现数字识别。
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来源期刊
CiteScore
5.00
自引率
4.20%
发文量
11
审稿时长
13 weeks
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