摘要
The thermal cycle reliability and process-induced warpage related to high-power module packaging integrated with the type of insulated gate bipolar transistor modules that have direct bonded copper are critical factors remarkably influencing the longevity and performance of power electronics. Despite the robustness of traditional finite element analysis (FEA) methods, they have profound limitations, including high computational demands and the necessity for extensive experimental validation. This study presents an integrated approach that combines FEA with machine learning (ML) to improve predictive reliability. The ML model integrated support vector algorithms is employed to trained base on FEA-generated stress, strain, and warpage data to predict outcomes with better accuracy and mitigated computational overhead. Hyperparameter optimization through grid search improves the model's predictive capabilities. The proposed ML-FEA framework, validated through comparison with experimental results, effectively forecasts failure probabilities and process-induced warpage under diverse thermal cycling scenarios. This integrated approach improves predictive accuracy and offers a scalable solution for the reliability assessment and design optimization of high-power electronic modules. Accordingly, it provides a foundational reference for mitigating warpage and enhancing overall reliability. Due to predictive accuracy, R2 value is employed to check the ML prediction. In this study, R2 value for ML in training data is more than 0.95. In addition, for computational time decreasing, the time from 604,800 s to lower than 60 s is mitigated. © 2025 Elsevier Ltd