@inproceedings{13610d12bd0642f4b1bd50e451767a76,
title = "Realistic Stripline Corner Modeling Using Surrogate Model and Topographic Fitting",
abstract = "This paper demonstrates a method to extract impedance-attenuation corners of a stripline with user-prescribed confidence levels. This is done using a sparse-grid-based surrogate model to quickly generate vast Monte Carlo datasets from which the impedance-attenuation distribution is calculated. Ellipses are fit to this distribution as equi-density contours to enclose a proportion of the solution data. Appropriate corners can be read off these ellipses and applied to broadband simulation. The results are compared against three measured test coupons, showing capability to analyze a PCIe Gen. 5 link. Realistic modeling of geometries and material variations is emphasized.",
keywords = "Monte Carlo, corner model, sparse grid",
author = "Andrew Page and Matteo Cocchini and Zhaoqing Chen and Xu Chen",
note = "This material is based upon work supported by the National Science Foundation under Grant No. CNS 16-24811 - Center for Advanced Electronics through Machine Learning (CAEML) and its industry members. 978-1-6654-5075-1/22/$31.00 {\textcopyright}2022 IEEE; 31st IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2022 ; Conference date: 09-10-2022 Through 12-10-2022",
year = "2022",
doi = "10.1109/EPEPS53828.2022.9947141",
language = "English (US)",
series = "EPEPS 2022 - IEEE 31st Conference on Electrical Performance of Electronic Packaging and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "EPEPS 2022 - IEEE 31st Conference on Electrical Performance of Electronic Packaging and Systems",
address = "United States",
}