A Small Database with an Adaptive Data Selection Method for Solder Joint Fatigue Life Prediction in Advanced Packaging

• Published in: Materials Vol. 17; no. 16; p. 4091
• Main Authors: Su, Qinghua, Yuan, Cadmus, Chiang, Kuo-Ning
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 17.08.2024; MDPI
• Subjects: Adaptive sampling; advanced packaging; Artificial intelligence; Computing costs; Data analysis; Datasets; Design; Ensemble learning; Experiments; Fatigue; Fatigue life; Fatigue testing machines; Finite element analysis; Life prediction; Machine learning; Materials; Metamodels; Methods; Neural networks; Optimization algorithms; Packaging; Performance enhancement; Sampling methods; Sampling techniques; Shear strain; Simulation; small data; Soldered joints; Solders; Thermal cycling; adaptive sampling; ensemble learning; AI-assisted design of simulation (AI-DoS); small data; life prediction; advanced packaging; machine learning
• ISSN: 1996-1944; 1996-1944
• 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.