Machine learning for evaluating the impact of manufacturing process variations in high-speed interconnects

Cemil S. Geyik, Zhichao Zhang, Kemal Aygun, James T. Aberle

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

2 Scopus citations

Abstract

This paper presents a machine learning based modeling methodology to analyze the impact of high-volume manufacturing process variations on electrical performance of high-speed interconnects, that overcomes the limitations of traditional approaches. The proposed methodology outperforms the response surface based modeling for high-speed interconnects and is capable of handling highly nonlinear relationships. Machine learning is demonstrated to be a promising approach to explore design spaces efficiently and accurately even when modeling data is limited due to expensive computational cost.

Original languageEnglish (US)
Title of host publicationProceedings of the 22nd International Symposium on Quality Electronic Design, ISQED 2021
PublisherIEEE Computer Society
Pages160-163
Number of pages4
ISBN (Electronic)9781728176413
DOIs
StatePublished - Apr 7 2021
Event22nd International Symposium on Quality Electronic Design, ISQED 2021 - Santa Clara, United States
Duration: Apr 7 2021Apr 9 2021

Publication series

NameProceedings - International Symposium on Quality Electronic Design, ISQED
Volume2021-April
ISSN (Print)1948-3287
ISSN (Electronic)1948-3295

Conference

Conference22nd International Symposium on Quality Electronic Design, ISQED 2021
Country/TerritoryUnited States
CitySanta Clara
Period4/7/214/9/21

Keywords

  • High-speed interconnect
  • Machine learning
  • Manufacturing process variations
  • Regression
  • Response surface

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

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