Machine Learning for high speed channel optimization
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2019-11-12 v1 Machine Learning
Signal Processing
Machine Learning
Abstract
Design of printed circuit board (PCB) stack-up requires the consideration of characteristic impedance, insertion loss and crosstalk. As there are many parameters in a PCB stack-up design, the optimization of these parameters needs to be efficient and accurate. A less optimal stack-up would lead to expensive PCB material choices in high speed designs. In this paper, an efficient global optimization method using parallel and intelligent Bayesian optimization is proposed for the stripline design.
Cite
@article{arxiv.1911.04317,
title = {Machine Learning for high speed channel optimization},
author = {Jiayi He and Aravind Sampath Kumar and Arun Chada and Bhyrav Mutnury and James Drewniak},
journal= {arXiv preprint arXiv:1911.04317},
year = {2019}
}
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3 Pages