High-speed channel modeling with deep neural network for signal integrity analysis

Tianjian Lu, Ken Wu, Zhiping Yang, Ju Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-3
Number of pages3
ISBN (Electronic)9781467364836
DOIs
StatePublished - Apr 2 2018
Externally publishedYes
Event26th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2017 - San Jose, United States
Duration: Oct 15 2017Oct 18 2017

Publication series

Name2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2017
Volume2018-January

Conference

Conference26th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2017
Country/TerritoryUnited States
CitySan Jose
Period10/15/1710/18/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

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