A spintronics neuron device based on voltage-induced strain is proposed. The stochastic switching behavior, which can mimic the firing behavior of neurons, is obtained by using two voltage signals to control the in-plane magnetization of a free layer of magneto-tunneling junction. One voltage signal is used as the input, and the other voltage signal can be used to tune the activation function (Sigmoid-like) of spin neurons. Therefore, this voltage-driven tunable spin neuron does not necessarily use energy-inefficient Oersted fields and spin-polarized current. Moreover, a voltage-control reading operation is presented, which can achieve the transition of activation function from Sigmoid-like to ReLU-like. A three-layer artificial neural network based on the voltage-driven spin neurons is constructed to recognize the handwritten digits from the MNIST dataset. For the MNIST handwritten dataset, the design achieves 97.75% recognition accuracy. The present results indicate that the v...

中文翻译：

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电压驱动的自适应自旋电子神经元，用于节能型神经形态计算

提出了一种基于电压感应应变的自旋电子学装置。通过使用两个电压信号来控制磁隧道结自由层的面内磁化强度，可以模拟神经元的放电行为。一个电压信号被用作输入，另一电压信号可以被用来调节自旋神经元的激活功能（类似于Sigmoid）。因此，此电压驱动的可调自旋神经元不一定使用能量效率低的奥斯特场和自旋极化电流。此外，提出了一种电压控制读取操作，可以实现激活功能从类Sigmoid过渡到ReLU类。基于电压驱动的自旋神经元的三层人工神经网络被构造为从MNIST数据集中识别手写数字。对于MNIST手写数据集，该设计可实现97.75％的识别精度。目前的结果表明，...