Laguerre–Volterra Feed-Forward Neural Network for Modeling PAM-4 High-Speed Links

Abstract

In this article, we present a PAM-4 IBIS-AMI model derived from machine learning for time-domain simulation. More specifically, we report a Laguerre–Volterra-expanded feed-forward neural network (LVFFN) approach with one hidden layer and ten neurons to model the 28-Gb/s PAM-4 high-speed link buffer. The proposed LVFFN model reduces the model size and improves the computational efficiency dramatically compared with the Volterra series model and other transitional artificial neural network models. The LVFFN model is implemented in IBIS-AMI, an industrial standard, and is simulated in existing software platforms for eye-diagram analysis. This work has two innovations: 1) we propose a method that dramatically reduces the neural network model complexity through a Laguerre–Volterra expansion when modeling weakly nonlinear systems with memory and 2) we implement an LVFFN model into IBIS-AMI to enhance the model interoperability and transportability.