Generic Memory Modeling with Recurrent Neural Network

In this work, a methodology for developing a memory compact model is described using recurrent neural network (RNN). Compared to traditional modeling approaches, it is flexible and able to develop an accurate model based on physical data before the material physics is fully understood. A simple ReRAM-type memory model is demonstrated using Nonlinear Autoregressive with External Input (NARX) machine learning approach. To enable the neural network to capture the resistive switching characteristics of ReRAM, the output at a range of cycling voltages was captured and used as training data. The trained model is used for DC prediction under different voltage amplitudes, and the prediction results are consistent with the physical data, with MSE values below $10^{-9}$. The accuracy of the RNN-assisted ReRAM model during the read, write and erase operations is also evaluated to show the validity of the approach. The calibration between the prediction results of the model and experimental data further proves the feasibility of the approach.