Demystifying Machine Learning for Signal and Power Integrity Problems in Packaging
In this paper, 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 u...
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| Veröffentlicht in: | IEEE transactions on components, packaging, and manufacturing technology (2011) packaging, and manufacturing technology (2011), 2020-08, Vol.10 (8), p.1-1 |
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| container_title | IEEE transactions on components, packaging, and manufacturing technology (2011) |
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| creator | Swaminathan, Madhavan Torun, Hakki Mert Yu, Huan Hejase, Jose Ale Becker, Wiren Dale |
| description | In this paper, 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. |
| doi_str_mv | 10.1109/TCPMT.2020.3011910 |
| format | Article |
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| subjects | Artificial neural networks Bayesian Learning behavioral modeling Computational modeling design optimization Integrity Machine learning Neural Networks Neurons Optimization Packaging signal and power integrity Source code surrogate modeling Training |
| title | Demystifying Machine Learning for Signal and Power Integrity Problems in Packaging |
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