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.

中文翻译：

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包装中信号和电源完整性问题的神秘机器学习

在本文中，我们重点介绍了神经网络和贝叶斯学习的基础知识，重点是包装中出现的信号和电源完整性问题。我们不仅关注数学公式，还解释了重要的概念及其背后的直觉，从而揭开了使用机器学习解决这些问题的神秘性。我们还将分享该领域的一些最新进展以及包装方面的未来研究方向。还提供了有关所讨论的某些方法的开源可下载软件的链接。