Comparison of Machine Learning Techniques for Predictive Modeling of High-Speed Links

Hanzhi Ma, Er Ping Li, Andreas C. Cangellaris, Xu Chen

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

Abstract

We compare three different machine learning techniques for constructing predictive model for eye opening based on channel length and interconnect cross-sectional geometry. Surrogate model is constructed using sparse grids, support vector regression, and artificial neural networks. Models for training data are generated using quasi-TEM modeling of the interconnect, and eye opening training data is obtained from statistical high-speed link simulation using IBIS-AMI transmitter and receiver models. Numerical results illustrate that all three methods offer reasonable predictions of eye height, eye width and eye width at 10-12 bit error rate.

Original languageEnglish (US)
Title of host publication2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728145853
DOIs
StatePublished - Oct 2019
Event28th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2019 - Montreal, Canada
Duration: Oct 6 2019Oct 9 2019

Publication series

Name2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2019

Conference

Conference28th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2019
CountryCanada
CityMontreal
Period10/6/1910/9/19

ASJC Scopus subject areas

  • Hardware and Architecture
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Electronic, Optical and Magnetic Materials

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  • Cite this

    Ma, H., Li, E. P., Cangellaris, A. C., & Chen, X. (2019). Comparison of Machine Learning Techniques for Predictive Modeling of High-Speed Links. In 2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2019 [9073018] (2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EPEPS47316.2019.193199