Interpretable Machine Learning Models Can Outperform Statistical Models in Solder Joint Reliability

Weibull regression and the Cox proportional hazard model (Cox-PHM) are commonly employed for analyzing the solder joint reliability due to their simplicity and meaningful results. Recently, various machine learning (ML) models have been applied and shown similar or better performance than conventional approaches, yet they do not incur widespread adoption in reliability modeling because of their limited transparency and interpretability. In this research, we compare the predictive capabilities of Weibull regression and Cox-PHM with ML techniques (specifically, Random Survival Forests [RSF] and Gradient Boosting [GB]) using experimental data from an accelerated thermal cycling reliability test and calculating the concordance index (c-index) as an evaluation metric to assess the effectiveness of a model in predicting the sequence of failure times in the tests. The findings with our dataset showed that ML-based models have a similar performance with the traditional Weibull regression (c-index = 0.808), and GB can even outperform with a high c-index value of 0.848. The superior performance of ML models can be attributed to their capacity to capture nonlinearities and intricate relationships within the data. Additionally, we utilized feature importance scores to understand which factors/variables significantly influence the model's prediction. Such interpretable ML methods can provide clear insights into how models make predictions, thereby enhancing trust and the adoption of innovative ML techniques in electronics manufacturing.