TY - JOUR
T1 - Demystifying Machine Learning for Signal and Power Integrity Problems in Packaging
AU - Swaminathan, Madhavan
AU - Torun, Hakki Mert
AU - Yu, Huan
AU - Hejase, Jose Ale
AU - Becker, Wiren Dale
N1 - Funding Information:
Manuscript received June 14, 2020; accepted July 15, 2020. Date of publication July 27, 2020; date of current version August 14, 2020. This work was supported in part by the NSF under Grant No. CNS 16-24731 - Center for Advanced Electronics through Machine Learning (CAEML). Recommended for publication by Associate Editor D. G. Kam upon evaluation of reviewers’ comments. (Corresponding author: Madhavan Swaminathan.) Madhavan Swaminathan, Hakki Mert Torun, and Huan Yu are with the 3D Systems Packaging Research Center (PRC), School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA (e-mail: madhavan.swaminathan@ece.gatech.edu; htorun3@ gatech.edu; huanyugt@gmail.com).
Publisher Copyright:
© 2011-2012 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - In this article, we cover the fundamentals of neural networks and Bayesian learning with a focus on signal and power integrity problems arising in packaging. Rather than only focus on mathematical formulations, we explain the important concepts and the intuition behind them, thereby demystifying the use of machine learning for these problems. We also share some of the recent developments in this area along with future research directions in the context of packaging. Links to open-source downloadable software for some of the methods discussed are also provided.
AB - In this article, we cover the fundamentals of neural networks and Bayesian learning with a focus on signal and power integrity problems arising in packaging. Rather than only focus on mathematical formulations, we explain the important concepts and the intuition behind them, thereby demystifying the use of machine learning for these problems. We also share some of the recent developments in this area along with future research directions in the context of packaging. Links to open-source downloadable software for some of the methods discussed are also provided.
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U2 - 10.1109/TCPMT.2020.3011910
DO - 10.1109/TCPMT.2020.3011910
M3 - Article
AN - SCOPUS:85089873374
SN - 2156-3950
VL - 10
SP - 1276
EP - 1295
JO - IEEE Transactions on Components, Packaging and Manufacturing Technology
JF - IEEE Transactions on Components, Packaging and Manufacturing Technology
IS - 8
M1 - 9149655
ER -