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High-Speed Channel Modeling with Deep Neural Network for Signal Integrity Analysis

Ju Sun
Ken Wu
Zhiping Yang
IEEE Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS) - EPEPS 2017.


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.

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