PAM-4 Behavioral Modeling using Machine Learning via Laguerre-Volterra Expansion

In this paper, we demonstrate a PAM-4 IBIS-AMI model obtained from machine learning for time domain simulation in commercial simulators. More specifically, we report a Laguerre-Volterra-expanded Feed-Forward neural Network (LVFFN) with only one hidden layer and 10 neurons to model the 28Gb/s PAM-4 high speed link buffer. the LVFFN model reduces the model size and improves the computational efficiency dramatically compared to the Volterra series model and other transitional artificial neural network models. The LVFFN PAM-4 model can be implemented in IBIS-AMI, an industrial standard for high speed link modeling. Eye-diagram analysis is conducted on LVFFN PAM-4 IBIS-AMI model in commercial simulators. Our results show that our model is faster to simulate 1 million bits and is easier to implement compared to the standard PAM-4 IBIS-AMI model.