A Compact Model of Analog RRAM Considering Temperature Coefficient for Neural Network Evaluation

Developing analog RRAM model for studying the effects of nonideal characteristic on neural network is necessary. In this work, we developed an analog RRAM compact model considering the nonideal characteristic of temperature coefficient, I-V nonlinearity, variability, and programing nonlinearity, which is well consistent with data measured from RRAM array. Then we evaluated the accuracy of MNIST task using RRAM model based multilayer perceptron. Especially, the effects of temperature coefficient on the computing task is studied.