Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore.
Centre for Applied Research in Electronics, Indian Institute of Technology Delhi, New Delhi, India 110016.
Nano Lett. 2022 Nov 9;22(21):8437-8444. doi: 10.1021/acs.nanolett.2c02409. Epub 2022 Oct 19.
Spintronics has been recently extended to neuromorphic computing because of its energy efficiency and scalability. However, a biorealistic spintronic neuron with probabilistic "spiking" and a spontaneous reset functionality has not been demonstrated yet. Here, we propose a biorealistic spintronic neuron device based on the heavy metal (HM)/ferromagnet (FM)/antiferromagnet (AFM) spin-orbit torque (SOT) heterostructure. The spintronic neuron can autoreset itself after firing due to the exchange bias of the AFM. The firing process is inherently stochastic because of the competition between the SOT and AFM pinning effects. We also implement a restricted Boltzmann machine (RBM) and stochastic integration multilayer perceptron (SI-MLP) using our proposed neuron. Despite the bit-width limitation, the proposed spintronic model can achieve an accuracy of 97.38% in pattern recognition, which is even higher than the baseline accuracy (96.47%). Our results offer a spintronic device solution to emulate biologically realistic spiking neurons.
自旋电子学由于其能量效率和可扩展性，最近已经扩展到神经形态计算领域。然而，具有概率“尖峰”和自发重置功能的生物逼真自旋电子神经元尚未得到证明。在这里，我们提出了一种基于重金属 (HM)/铁磁体 (FM)/反铁磁体 (AFM) 自旋轨道扭矩 (SOT) 异质结构的生物逼真自旋电子神经元器件。由于 AFM 的交换偏置，自旋电子神经元在发射后可以自动重置。由于 SOT 和 AFM 钉扎效应的竞争，发射过程本质上是随机的。我们还使用我们提出的神经元实现了受限玻尔兹曼机 (RBM) 和随机积分多层感知机 (SI-MLP)。尽管存在位宽限制，但所提出的自旋电子模型在模式识别中可以达到 97.38%的准确率，甚至高于基线准确率（96.47%）。我们的结果为模拟生物逼真的尖峰神经元提供了一种自旋电子设备解决方案。
