@inproceedings{ba165d0c64cf4012b50ac51b99ddada3,
title = "Machine learning for evaluating the impact of manufacturing process variations in high-speed interconnects",
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.",
keywords = "High-speed interconnect, Machine learning, Manufacturing process variations, Regression, Response surface",
author = "Geyik, {Cemil S.} and Zhichao Zhang and Kemal Aygun and Aberle, {James T.}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 22nd International Symposium on Quality Electronic Design, ISQED 2021 ; Conference date: 07-04-2021 Through 09-04-2021",
year = "2021",
month = apr,
day = "7",
doi = "10.1109/ISQED51717.2021.9424359",
language = "English (US)",
series = "Proceedings - International Symposium on Quality Electronic Design, ISQED",
publisher = "IEEE Computer Society",
pages = "160--163",
booktitle = "Proceedings of the 22nd International Symposium on Quality Electronic Design, ISQED 2021",
}