Statistical temperature coefficient distribution in analog RRAM array: impact on neuromorphic system and mitigation method

Emerging analog resistive random access memory (RRAM) based on HfO _x 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_α ). A compact model was presented in order to investigate the statistical distribution of T_α under different resistance states. Based on this model, the physical mechanism of thermal instability of cells with a positive T_α was elucidated. Furthermore, this model can also effectively evaluate the impact of conductance distribution of different levels under various temperatures in artificial neural networks. A current compensation scheme and hybird optimization method were proposed to reduce the impact of the distribution of T_α . The simulation results showed that recognition accuracy was improved from 79.8% to 91.3% for the application of Modified National Institute of Standards and Technology handwriting digits classification with a two-layer perceptron at 400 K after adopting the proposed optimization method.