Reliability Assessment of Solder Ball Joints Using Finite Element Analysis and Machine Learning techniques

This paper presents a comprehensive approach for reliability assessment and optimization of ball joints by combining finite element analysis (FEA) method with machine learning algorithms, in particular by exploiting the power of the XGBoost machine learning algorithm. FEA simulations were carried out using ANSYS to generate a dataset encompassing various solder joint geometrical parameters configurations. Key input parameters, including geometric dimensions, were identified by sensitivity analysis and used as features for training the XGBoost model. The trained model demonstrated solid performance. Feature importance analysis revealed critical factors influencing solder joint reliability and provided insights for optimization strategies. This research provides practical recommendations for optimizing solder joint and material design to improve reliability and performance in electronic packaging applications.