Ferrimagnetic materials with antiparallel exchange coupling, exhibit spin‐orbit‐torque‐induced dynamics, offering an emerging platform for realizing neuromorphic devices, such as artificial synapses. However, the state‐of‐the‐art artificial synapses based on ferrimagnet suffer from poor analog switching linearity, which serves as a bottleneck for achieving complex tasks with high accuracy in neuromorphic computing. Here, an artificial synapse is reported with high‐weight update linearity in a compensated ferrimagnetic crossbar device. In particular, the linear weight update of the synapses is enhanced by engineering the current density distribution. Using experimentally derived device parameters, handwritten digit recognition can be achieved with an accuracy of over 95% in a three‐layer fully connected artificial neural network. The work provides a universal method to improve the synaptic linearity, which also paves the way for applying the spin‐orbit device in neuromorphic computing.

中文翻译：

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铁磁神经形态器件中的线性权重更新突触反应


具有反平行交换耦合的亚铁磁性材料表现出自旋-轨道-扭矩诱导的动力学，为实现神经形态器件（如人工突触）提供了一个新兴平台。然而，基于铁磁体的最先进的人工突触的模拟开关线性度较差，这成为神经形态计算中高精度完成复杂任务的瓶颈。在这里，在补偿亚铁磁横杆装置中报告了具有高权重更新线性度的人工突触。特别是，通过设计电流密度分布来增强突触的线性权重更新。使用实验得出的设备参数，可以在三层全连接人工神经网络中以超过 95% 的准确率实现手写数字识别。这项工作提供了一种提高突触线性度的通用方法，这也为自旋轨道装置在神经形态计算中的应用铺平了道路。