Emerging analog resistive random access memory (RRAM) based on HfOx is an attractive device for non-von Neumann neuromorphic computing systems. The differences in temperature dependent conductance drift among cells hamper computing accuracy, characterized by the statistical distribution of temperature coefficient(T{\alpha}). A compact model was presented in order to investigate the statistical distribution of T{\alpha} under different resistance states. Based on this model, the physical mechanism of thermal instability of cells with a positive T{\alpha} was elucidated. Furthermore, this model can also effectively evaluate the impact of conductance distribution of different levels under various temperatures in artificial neural networks (ANN). An approach incorporating the optimized conductance range selection and the current compensation scheme was proposed to reduce the impacts of the distribution of T{\alpha}. The simulation results showed that recognition accuracy was improved from 79.8% to 89.6% for the application of MNIST handwriting digits classification with a two-layer perceptron at 400K after adopting the proposed optimization method.

中文翻译：

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模拟RRAM阵列中的统计温度系数分布：对神经形态系统和缓解方法的影响

基于HfOx的新兴模拟电阻式随机存取存储器（RRAM）对于非冯·诺依曼神经形态计算系统来说是一种有吸引力的设备。单元之间温度相关的电导漂移的差异妨碍了计算精度，其特征在于温度系数（T {\ alpha}）的统计分布。为了研究不同电阻状态下T {\ alpha}的统计分布，提出了一个紧凑模型。基于此模型，阐明了具有正T {\ alpha}的细胞的热不稳定性的物理机制。此外，该模型还可以有效地评估人工神经网络（ANN）在不同温度下不同水平电导分布的影响。提出了一种结合优化电导范围选择和电流补偿方案的方法，以减少T {\ alpha}分布的影响。仿真结果表明，采用提出的优化方法后，采用两层感知器在400K下进行MNIST手写数字分类，识别准确率从79.8％提高到89.6％。