The application of spin–orbit torque (SOT) devices to neuromorphic computing platforms is focused on the development of hardware circuit architectures. However, the inter-device variability, the integration modes of devices and peripheral circuits, and appropriate application scenarios are still unclear, limiting the development of SOT devices in neuromorphic computing. To solve this problem, this paper first proposes a circuit compensation scheme for the difference in resistance values of SOT devices, which solves this variability problem at the circuit level. Moreover, a synergistic scheme with the circuit is developed based on the correspondence between the multistate resistance characteristics of the SOT devices and a convolutional algorithm. To achieve this, a multichannel SOT convolutional kernel circuit architecture is built, which implements an image edge recognition application. Finally, based on a simulation model, an image edge recognition hardware circuit based on our CoPt-SOT devices is implemented, which is capable of performing image edge recognition with an accuracy of 96.33%. This scheme provides technical support and development prospects for SOT devices in neural network hardware applications.

中文翻译：

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用于可编程图像边缘识别电路的无场多态自旋轨道扭矩器件


自旋轨道扭矩 （SOT） 器件在神经形态计算平台上的应用集中在硬件电路架构的开发上。然而，器件间的可变性、器件与外围电路的集成模式以及合适的应用场景仍不清楚，限制了 SOT 器件在神经形态计算中的发展。为了解决这个问题，本文首先提出了一种针对 SOT 器件电阻值差异的电路补偿方案，在电路层面解决了这个可变性问题。此外，基于 SOT 器件的多态电阻特性与卷积算法之间的对应关系，开发了与电路的协同方案。为此，构建了多通道 SOT 卷积核电路架构，实现了图像边缘识别应用。最后，基于仿真模型，实现了基于我司 CoPt-SOT 器件的图像边缘识别硬件电路，能够以 96.33% 的准确率进行图像边缘识别。该方案为 SOT 器件在神经网络硬件应用中提供了技术支持和发展前景。