Su Qinghua, Yuan Cadmus, Chiang Kuo-Ning
Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu City 30013, Taiwan.
Department of Mechanical and Computer-aided Engineering, Feng Chia University, Taichung 407102, Taiwan.
Materials (Basel). 2024 Aug 17;17(16):4091. doi: 10.3390/ma17164091.
There has always been high interest in predicting the solder joint fatigue life in advanced packaging with high accuracy and efficiency. Artificial Intelligence Plus (AI+) is becoming increasingly popular as computational facilities continue to develop. This study will introduce machine learning (a core component of AI). With machine learning, metamodels that approximate the attributes of systems or functions are created to predict the fatigue life of advanced packaging. However, the prediction ability is highly dependent on the size and distribution of the training data. Increasing the amount of training data is the most intuitive approach to improve prediction performance, but this implies a higher computational cost. In this research, the adaptive sampling methods are applied to build the machine learning model with a small dataset sampled from an existing database. The performance of the model will be visualized using predefined criteria. Moreover, ensemble learning can be used to improve the performance of AI models after they have been fully trained.
一直以来，人们都对高精度、高效率地预测先进封装中的焊点疲劳寿命有着浓厚的兴趣。随着计算设施的不断发展，人工智能增强版（AI+）越来越受欢迎。本研究将介绍机器学习（AI的核心组成部分）。通过机器学习，可以创建近似系统或功能属性的元模型，以预测先进封装的疲劳寿命。然而，预测能力高度依赖于训练数据的大小和分布。增加训练数据量是提高预测性能最直观的方法，但这意味着更高的计算成本。在本研究中，采用自适应采样方法，利用从现有数据库中采样得到的小数据集构建机器学习模型。将使用预定义的标准对模型的性能进行可视化展示。此外，在人工智能模型完全训练完成后，可以使用集成学习来提高其性能。
