A machine learning approach for evaluating drop impact reliability of solder joints in BGA packaging

The failure of solder joints of Ball Grid Array (BGA)Package under drop impact is influenced by multiple parameters, highlighting the need for optimization during the early design stages of electronic systems. In this paper, ensemble methods were developed to predict failure in solder joints by estimating the dynamic responses of printed circuit board assembly (PCBA) during drop impact conditions. Finite element (FE) simulations were carried out by varying PCB thickness, PCB modulus, solder ball diameter, and solder ball material to obtain the dynamic responses of the PCBA during impact loading, which served as the dataset for the predictive model. Also drop test experiments were conducted according to the JESD22-B111A standard to validate the FEM results. XGBoost regression achieved the best performance with an R² of 0.96 and the lowest error, with feature importance analysis identifying solder ball material (score: 0.447) as the most influential factor and PCB modulus (score: 0.065) as the least.The predictive model developed in this work offers a robust tool for evaluating mechanical performance and optimizing design parameters in PCBA structures under dynamic mechanical stresses.