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.
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.
Research Areas
