Demystifying Machine Learning for Signal and Power Integrity Problems in Packaging
Madhavan Swaminathan, Hakki Mert Torun, Huan Yu, Jose A. Hejase, Dale Becker
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.
Topics & Concepts
IntuitionSignal integrityComputer sciencePower integrityCover (algebra)Focus (optics)Artificial intelligenceContext (archaeology)Machine learningData scienceEngineeringCognitive scienceTelecommunicationsPsychologyMechanical engineeringInterconnectionOpticsPhysicsPaleontologyBiologyLow-power high-performance VLSI designVLSI and FPGA Design TechniquesMaterial Properties and Processing