High-Speed Channel Modeling with Deep Neural Network for Signal Integrity Analysis
Abstract
In this work, deep neural networks (DNNs) are
trained and used to model high-speed channels for signal integrity
analysis. The DNN models predict eye-diagram metrics by taking
advantage of the large amount of simulation results made
available in a previous design or at an earlier design stage. The
proposed DNN models characterize high-speed channels through
extrapolation with saved coefficients, which requires no complex
simulations and can be achieved in a highly efficient manner. It
is demonstrated through numerical examples that the proposed
DNN models achieve good accuracy in predicting eye-diagram
metrics from input design parameters. In the DNN models, no
assumptions are made on the distributions of and the interactions
among individual design parameters.