One long-standing goal in the emerging neuromorphic field is to create a reliable neural network hardware implementation that has low energy consumption, while providing massively parallel computation. Although diverse oxide-based devices have made significant progress as artificial synaptic and neuronal components, these devices still need further optimization regarding linearity, symmetry, and stability. Here, we present a proof-of-concept experiment for integrated neuromorphic computing networks by utilizing spintronics-based synapse (spin-S) and neuron (spin-N) devices, along with linear and symmetric weight responses for spin-S using a stripe domain and activation functions for spin-N. An integrated neural network of electrically connected spin-S and spin-N successfully proves the integration function for a simple pattern classification task. We simulate a spin-N network using the extracted device characteristics and demonstrate a high classification accuracy (over 93%) for the spin-S and spin-N optimization without the assistance of additional software or circuits required in previous reports. These experimental studies provide a new path toward establishing more compact and efficient neural network systems with optimized multifunctional spintronic devices.

新兴的神经形态领域的一个长期目标是创建一种可靠的神经网络硬件实现，该实现具有低能耗，同时提供大规模并行计算。尽管各种基于氧化物的设备已作为人造突触和神经元组件取得了重大进展，但这些设备仍需要在线性，对称性和稳定性方面进行进一步优化。在这里，我们通过利用基于自旋电子学的突触（spin-S）和神经元（spin-N）设备，以及使用条带对spin-S进行线性和对称权重响应，提出了集成神经形态计算网络的概念验证实验自旋N的域和激活函数。电连接的spin-S和spin-N的集成神经网络成功证明了用于简单模式分类任务的集成功能。我们使用提取的设备特性模拟了Spin-N网络，并证明了Spin-S和spin-N优化的高分类精度（超过93％），而无需先前报告中所需的其他软件或电路的帮助。这些实验研究为通过优化的多功能自旋电子设备建立更紧凑和有效的神经网络系统提供了一条新途径。