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

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

doi:10.1109/EPEPS47316.2019.193199

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 proceeding › Conference 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 language	English (US)
Title of host publication	2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728145853
DOIs	https://doi.org/10.1109/EPEPS47316.2019.193199
State	Published - Oct 2019
Event	28th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2019 - Montreal, Canada Duration: Oct 6 2019 → Oct 9 2019

Publication series

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

Conference

Conference	28th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2019
Country/Territory	Canada
City	Montreal
Period	10/6/19 → 10/9/19

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

Online availability

10.1109/EPEPS47316.2019.193199

Library availability

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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 Article 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

Comparison of Machine Learning Techniques for Predictive Modeling of High-Speed Links. / Ma, Hanzhi; Li, Er Ping; Cangellaris, Andreas C. et al.
2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 9073018 (2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ma, H, Li, EP, Cangellaris, AC & 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., 28th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2019, Montreal, Canada, 10/6/19. https://doi.org/10.1109/EPEPS47316.2019.193199

Ma H, Li EP, Cangellaris AC , Chen X. 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. Institute of Electrical and Electronics Engineers Inc. 2019. 9073018. (2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2019). doi: 10.1109/EPEPS47316.2019.193199

Ma, Hanzhi ; Li, Er Ping ; Cangellaris, Andreas C. et al. / Comparison of Machine Learning Techniques for Predictive Modeling of High-Speed Links. 2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2019).

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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.",

author = "Hanzhi Ma and Li, {Er Ping} and Cangellaris, {Andreas C.} and Xu Chen",

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AB - 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.

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

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