Efficient and adaptive Bayesian data-driven design of reliable solder joints for micro-electronic devices

Abstract

Solder joint reliability related to failures due to thermomechanical loading is a critically important yet physically complex engineering problem. As a result, simulated behavior is oftentimes computationally expensive. In an increasingly data-driven world, it is popular to use efficient data-driven design schemes. Among the family of efficient optimization methods, Bayesian optimization with Gaussian process regression is a key representative. The authors argue that additional computational savings can be obtained from exploiting thorough surrogate modeling and selecting a design candidate based on multiple acquisition functions. This is feasible due to the relatively low computational cost, compared to the expensive simulation objective. This paper presents a novel heuristic framework for performing Bayesian optimization with adaptive hyperparameters across multiple optimization iterations. A comparative study shows the ability of adaptive Bayesian optimization to save on expensive objective evaluations with respect to the worst-performing regular Bayesian optimization scheme. As an engineering use case, the solder joint reliability problem is tackled by minimizing the accumulated non-linear creep strain under a cyclic thermal load. Results show that adaptive Bayesian optimization can at least match the performance of regular Bayesian optimization in terms of raw objective performance, but achieves this with half of the computational expense budget. This practical result underlines the methodological potential of the novel adaptive Bayesian data-driven methodology to achieve more efficient results and significantly cut optimization-related expenses. Lastly, to promote the reproducibility of the results, the data-driven implementations are made available on an open-source basis.