Demystifying Machine Learning for Signal and Power Integrity Problems in Packaging
| Autor: | Wiren D. Becker, Jose A. Hejase, Hakki Mert Torun, Madhavan Swaminathan, Huan Yu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Computer science business.industry 020206 networking & telecommunications Power integrity 02 engineering and technology Bayesian inference Machine learning computer.software_genre Industrial and Manufacturing Engineering 020202 computer hardware & architecture Electronic Optical and Magnetic Materials Software 0202 electrical engineering electronic engineering information engineering Artificial intelligence Electrical and Electronic Engineering business computer Intuition |
| Zdroj: | IEEE Transactions on Components, Packaging and Manufacturing Technology. 10:1276-1295 |
| ISSN: | 2156-3985 2156-3950 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|