Machine learning (ML) is widely used for building data-driven models that are highly useful for optimization. In this study, a finite element model-based adaptive ML method is presented for chip package reliability prediction and design optimization. This ML method employs a validated multi-scale finite element model for training data generation. An adaptive sampling scheme is developed to optimize the training process with a steepest descent algorithm. The developed method was used to optimize ultra low-k chip package design. The effects of ten key design parameters on chip packaging reliability were considered. Multiple ML algorithms were evaluated for model development. It is shown that the adaptive sampling method performs much better than existing sequential sampling methods and that the finite element-based ML model can be used to achieve improved prediction accuracy for chip package design optimization.

中文翻译：

---

基于有限元分析的自适应机器学习方法用于超低k芯片封装设计


机器学习 (ML) 广泛用于构建对优化非常有用的数据驱动模型。在本研究中，提出了一种基于有限元模型的自适应机器学习方法，用于芯片封装可靠性预测和设计优化。该机器学习方法采用经过验证的多尺度有限元模型来生成训练数据。开发了一种自适应采样方案，以使用最速下降算法来优化训练过程。所开发的方法用于优化超低 k 芯片封装设计。考虑了十个关键设计参数对芯片封装可靠性的影响。评估了多种机器学习算法以进行模型开发。结果表明，自适应采样方法比现有的顺序采样方法表现得更好，并且基于有限元的 ML 模型可用于提高芯片封装设计优化的预测精度。