Demystifying Machine Learning for Signal and Power Integrity Problems in Packaging

Madhavan Swaminathan; Hakki Mert Torun; Huan Yu; Jose Ale Hejase; Wiren Dale Becker

doi:10.1109/TCPMT.2020.3011910

Demystifying Machine Learning for Signal and Power Integrity Problems in Packaging

Madhavan Swaminathan, Hakki Mert Torun, Huan Yu, Jose Ale Hejase, Wiren Dale Becker

Electrical Engineering

Research output: Contribution to journal › Article › peer-review

71 Scopus citations

Abstract

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.

Original language	English (US)
Article number	9149655
Pages (from-to)	1276-1295
Number of pages	20
Journal	IEEE Transactions on Components, Packaging and Manufacturing Technology
Volume	10
Issue number	8
DOIs	https://doi.org/10.1109/TCPMT.2020.3011910
State	Published - Aug 2020

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Industrial and Manufacturing Engineering
Electrical and Electronic Engineering

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10.1109/TCPMT.2020.3011910

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Demystifying Machine Learning for Signal and Power Integrity Problems in Packaging

Abstract

All Science Journal Classification (ASJC) codes

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