XSCAN: Explainable solder joint defect probability prediction through solder paste printing status with imbalanced data

This research addresses challenges in Surface Mount Technology (SMT) related to solder joint quality prediction, focusing on the initial solder paste printing stage. Recognizing that over 50% of defects originate at the printing stage, this research delves into establishing a direct correlation between printing quality and joint quality. Traditional approaches have limitations in accurately predicting defects due to isolated treatment of printing quality indicators, scarce explainability of prediction models, and lack of joint defect data. This research introduces a novel framework, XSCAN, aimed at predicting the probabilities of solder joint defects from the states of the printed solder paste. This is accomplished by using a generative adversarial network (GAN) to synthesize additional defect data and segment the feature space of printing indicators using customized decision trees to minimize defect probability prediction error. Specifically, XSCAN optimizes generative model structures using decision tree prediction results focused on defects, generating valuable defect information to help feature space partition. Also, pruning rules are designed to handle imbalanced data and improve defect prediction. They enhance explainability by defining safe and high-risk zones for solder paste quality. XSCAN outperforms all other baselines when tested on real-world datasets of chip resistors. It achieves the lowest prediction error and provides different warning levels for potential joint defects. XSCAN takes a proactive approach to improve manufacturing quality while addressing data imbalance and model explainability challenges. It provides practical insights to enhance SMT processes and reduce waste and rework costs.