Combining polynomial regression with unsupervised machine learning on wafer-level packaging reliability prediction

The benefits of wafer-level packaging include better thermal dissipation, lower latency and reduced space consumption. Accelerated thermal cycling test (ATCT) is a regulation that determines whether a product is ready for mass production, but it takes a long time and is costly to perform. The design-on-simulation approach can reduce the number of ATCT experiments and shorten the design cycle. However, the simulation method must be verified before it can be treated as an experiment; if the simulation consistently matches experiments at close range, it can also be treated as an experiment. In addition, the verified simulation method can be used to develop a machine learning ( ML) database and obtain an artificial intelligence model for long-term reliability prediction. Due to its effectiveness in solving nonlinear problems with relatively short computation times, polynomial regression ( PR) is used in this study as ML model. Results show combining PR with an unsupervised learning algorithm, K-means, can produce more accurate predictions.