Fast Multiple Edge Response Method Based on the Design of Experiment and Machine Learning

Abstract

The multiple edge response (MER) method can accurately capture the nonlinear characteristics in signal integrity simulations. However, transient simulation is time-consuming to obtain all the edge responses for high-order MER. A fast method based on the design of experiment (DoE) and machine learning is proposed in this letter to obtain all the edge responses for a severe nonlinear link. The DoE is used to generate a few training sets for an artificial neural network (ANN). The ANN uses the generated training sets to build a precise model. The developed ANN model predicts the remaining edge responses. The proposed method takes 9.8 min to obtain all the responses, which is 92 min for transient simulations for the ninth-order MER. The results show that the eye height and width error between the proposed method and measurement is less than 5%.