A Compact Model of Analog RRAM With Device and Array Nonideal Effects for Neuromorphic Systems
Yan Liao, Bin Gao, Feng Xu, Peng Yao, Junren Chen, Wenqiang Zhang, Jianshi Tang, Huaqiang Wu, He Qian
Abstract
The parallelism and analog computing features of neuromorphic systems bring great challenges in developing a compact model of analog resistive random access memory (RRAM). In this article, we develop a physics-based compact model for analog RRAM devices and crossbar array. Nonideal effects of analog RRAM device, such as variability, I-V nonlinearity, programming nonlinearity and asymmetry, and tuning voltage sensitivity, are modeled and verified with the statistical data measured from RRAM array. Modeling of parallel-vector-matrix-multiplication and weight update process on RRAM crossbars with interconnect resistance enables fast and accurate estimation of the training accuracy. Benchmarks of neural networks under different hardware conditions validate the functionality of the proposed model. This model can provide valuable design guidelines for a practical neuromorphic system with high performance and computing accuracy.