Demystifying Machine Learning for Signal and Power Integrity Problems in Packaging

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

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


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 languageEnglish (US)
Article number9149655
Pages (from-to)1276-1295
Number of pages20
JournalIEEE Transactions on Components, Packaging and Manufacturing Technology
Issue number8
StatePublished - 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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