Prediction of the void formation in no-flow underfill process using machine learning-based algorithm

This paper investigated the variation variables on the void formation in the no-flow underfill process. A total of 96 distinct no-flow underfill cases were numerically simulated to determine the void formations for different combinations of underfill parameters. The numerical model has been well validated with the experiment, for which the discrepancies are less than 4 %. It was found that the void formation rate increases with the chip placement speed however it decreases with the increase in bump pitch. The highest chip placement speed of 14 mm/s produces 4-6 % meanwhile, the low chip placement speed (2-5 mm/s) produces around 2-3.5 % of void formation. A supervised machine learning (ML) approach is implemented using the validated numerical results to build a prediction-based method for no-flow underfill process. Three ML methods were trained and tested. Neural networks regression was recommended compared to other methods since it has the highest R-squared value and lowest error: 0.95159 and 0.15885, respectively. Chip placement speed obtained the highest score of 1.791641 for permutation feature in ML, which indicates the most significant variable affecting the package's void formation. The obtained result will aid the industry to choose the most favourable no-flow underfill process parameters regarding the void formation issue, thus increasing the reliability of the package.