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

Statistical Temperature Coefficient Distribution in Analog RRAM Array: Impact on Neuromorphic System and Mitigation Method