  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.
