To achieve high classification accuracy in the neuromorphic computing, the good linearity of the weight updating in memristors is critical. However, it is very challenging to realize a good linearity behavior in filamentary memristors. In this work, we develop a unidirectional depression pulse scheme to optimize the linearity in TaO_x-based memristors. Devices with the conventional identical pulse scheme perform high nonlinearity because of the abrupt SET process, while the proposed scheme offers an optimized linearity and uses two devices to define the synaptic weight. Moreover, the classification accuracy of MNIST handwritten digits by the neural network based on the TaO_x memristors is enhanced from 45% to 94% with this pulse scheme.

为了在神经形态计算中实现高分类精度，忆阻器中权重更新的良好线性至关重要。然而，在丝状忆阻器中实现良好的线性行为非常具有挑战性。在这项工作中，我们开发了一种单向抑制脉冲方案来优化基于TaO _x的忆阻器的线性度。由于突然的 SET 过程，具有传统相同脉冲方案的设备执行高度非线性，而所提出的方案提供优化的线性度并使用两个设备来定义突触权重。此外，使用这种脉冲方案，基于 TaO _x忆阻器的神经网络对 MNIST 手写数字的分类精度从 45% 提高到 94%。