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<p>Assessing the reliability of solder joints is a significant challenge in the electronics manufacturing industry, as numerous factors affect their integrity and performance. Traditionally, accelerated life tests (ALTs) are used for evaluating solder joint reliability, and survival analysis models such as Weibull and the Cox proportional hazards model (Cox-PHM) are widely used to develop life prediction models based on ALT data. The rise of machine learning (ML) models, including random survival forest, extreme gradient boosting (XGB), and survival support vector machines (SSVMs), offers promising data-driven alternatives, especially given their potential for higher predictive accuracy. However, their interpretability remains a concern for the electronics manufacturing community. In this study, we conducted systematic research to integrate multiple ML algorithms and Shapley’s additive explanation (SHAP) techniques to model solder joint reliability in thermal cycling tests from various impacting factors and to extract knowledge from the ML models for interpretability. The ML approaches demonstrate superior predictive performance compared to traditional survival analysis models. For instance, XGB achieves the highest c-index of 0.88 on the testing dataset, indicating strong discriminative power. Similarly, the KSSVM model yields the lowest test MAPE of 15.26%, reflecting excellent accuracy in predicting cycles to failure. The GB model also performs well, with a c-index of 0.88 and test MAPE of 15.31%, highlighting the reliability of boosting-based approaches. While traditional models like Cox-PHM and Weibull yield c-indices around 0.87 and 0.85, respectively, they fall short in prediction error, with MAPEs exceeding 20%. These findings confirm the advantages of advanced ML models in capturing complex patterns in reliability data. Furthermore, SHAP analysis enhances model transparency by revealing how critical features—such as component type, solder material, and aging duration—interact to drive failure predictions, offering insight beyond what conventional models can provide.</p>

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A Machine Learning Framework with Shapley’s Additive Explanations to Assess Solder Joint Reliability for Electronic Packaging

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月10日

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  • Qais Qasaimeh,
  • Haoran Li,
  • Saad Hamasha,
  • Jia Liu

摘要

Assessing the reliability of solder joints is a significant challenge in the electronics manufacturing industry, as numerous factors affect their integrity and performance. Traditionally, accelerated life tests (ALTs) are used for evaluating solder joint reliability, and survival analysis models such as Weibull and the Cox proportional hazards model (Cox-PHM) are widely used to develop life prediction models based on ALT data. The rise of machine learning (ML) models, including random survival forest, extreme gradient boosting (XGB), and survival support vector machines (SSVMs), offers promising data-driven alternatives, especially given their potential for higher predictive accuracy. However, their interpretability remains a concern for the electronics manufacturing community. In this study, we conducted systematic research to integrate multiple ML algorithms and Shapley’s additive explanation (SHAP) techniques to model solder joint reliability in thermal cycling tests from various impacting factors and to extract knowledge from the ML models for interpretability. The ML approaches demonstrate superior predictive performance compared to traditional survival analysis models. For instance, XGB achieves the highest c-index of 0.88 on the testing dataset, indicating strong discriminative power. Similarly, the KSSVM model yields the lowest test MAPE of 15.26%, reflecting excellent accuracy in predicting cycles to failure. The GB model also performs well, with a c-index of 0.88 and test MAPE of 15.31%, highlighting the reliability of boosting-based approaches. While traditional models like Cox-PHM and Weibull yield c-indices around 0.87 and 0.85, respectively, they fall short in prediction error, with MAPEs exceeding 20%. These findings confirm the advantages of advanced ML models in capturing complex patterns in reliability data. Furthermore, SHAP analysis enhances model transparency by revealing how critical features—such as component type, solder material, and aging duration—interact to drive failure predictions, offering insight beyond what conventional models can provide.