In neuromorphic computing, artificial synapses provide a multi-weight (MW) conductance state that is set based on inputs from neurons, analogous to the brain. Herein, artificial synapses based on magnetic materials that use a magnetic tunnel junction (MTJ) and a magnetic domain wall (DW) are explored. By fabricating lithographic notches in a DW track underneath a single MTJ, 3–5 stable resistance states that can be repeatably controlled electrically using spin-orbit torque are achieved. The effect of geometry on the synapse behavior is explored, showing that a trapezoidal device has asymmetric weight updates with high controllability, while a rectangular device has higher stochasticity, but with stable resistance levels. The device data is input into neuromorphic computing simulators to show the usefulness of application-specific synaptic functions. Implementing an artificial neural network (NN) applied to streamed Fashion-MNIST data, the trapezoidal magnetic synapse can be used as a metaplastic function for efficient online learning. Implementing a convolutional NN for CIFAR-100 image recognition, the rectangular magnetic synapse achieves near-ideal inference accuracy, due to the stability of its resistance levels. This work shows MW magnetic synapses are a feasible technology for neuromorphic computing and provides design guidelines for emerging artificial synapse technologies.

中文翻译：

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用于神经形态计算的形状相关多权重磁性人工突触

在神经形态计算中，人工突触提供多权重 (MW) 电导状态，该状态基于神经元的输入设置，类似于大脑。在此，探索了基于使用磁隧道结 (MTJ) 和磁畴壁 (DW) 的磁性材料的人工突触。通过在单个 MTJ 下方的 DW 轨道中制造光刻凹口，可以实现 3-5 个稳定的电阻状态，这些状态可以使用自旋轨道扭矩进行电气重复控制。探讨了几何形状对突触行为的影响，表明梯形装置具有高度可控性的不对称权重更新，而矩形装置具有更高的随机性，但具有稳定的阻力水平。设备数据被输入到神经形态计算模拟器中，以显示特定于应用程序的突触功能的有用性。实施应用于流式 Fashion-MNIST 数据的人工神经网络 (NN)，梯形磁突触可用作高效在线学习的化生函数。实现用于 CIFAR-100 图像识别的卷积神经网络，由于其电阻水平的稳定性，矩形磁突触实现了近乎理想的推理精度。这项工作表明 MW 磁突触是一种可行的神经形态计算技术，并为新兴的人工突触技术提供了设计指南。梯形磁突触可用作高效在线学习的化生功能。实现用于 CIFAR-100 图像识别的卷积神经网络，由于其电阻水平的稳定性，矩形磁突触实现了近乎理想的推理精度。这项工作表明 MW 磁突触是一种可行的神经形态计算技术，并为新兴的人工突触技术提供了设计指南。梯形磁突触可用作高效在线学习的化生功能。实现用于 CIFAR-100 图像识别的卷积神经网络，由于其电阻水平的稳定性，矩形磁突触实现了近乎理想的推理精度。这项工作表明 MW 磁突触是一种可行的神经形态计算技术，并为新兴的人工突触技术提供了设计指南。