MACHINE LEARNING OPTIMIZATION OF UNDERFILL FLOW TIME IN FLIP-CHIP ENCAPSULATION OF BGA ASSEMBLIES

Authors

  • R. K. Apalowo

    Department of Mechanical Engineering, Federal University of Technology, Akure, Ondo State, Nigeria

  • J. A. Kehinde

  • E. O. Oyeleke

  • O. J. Owoloja

  • S. O. Famuyiwa

DOI:
https://doi.org/10.51459/futajeet.2026.20.1.530
Keywords:
Flip-chip BGA packaging, Underfill encapsulation, Capillary flow dynamics, Machine learning optimization, FVM modeling
Abstract

Predicting and minimizing underfill flow time remains challenging due to complex nonlinear interactions among material properties, solder bump geometry, and dispensing strategy. This study presents a hybrid computational framework integrating computational fluid dynamics (CFD) simulations with machine learning–based predictive modeling to analyze and optimize underfill flow behavior. A total of 120 simulations were performed using ANSYS Fluent, covering six dispensing configurations, four solder bump geometries (spherical, cylindrical, hourglass, and concave), and five underfill materials with varying viscosity, surface tension, and density. To eliminate volumetric bias, an inlet-area-balanced dispensing strategy was implemented to maintain constant total injection volume across configurations. Taguchi-based parametric analysis revealed that underfill material viscosity is the dominant factor governing flow time, followed by dispensing configuration, while bump geometry shows comparatively minor influence. Bayesian optimization using Scikit-Optimize and Optuna identified the optimal configuration as hourglass-shaped solder bumps, U-33.3 three-inlet dispensing, and a low-viscosity methanol-based underfill. Among the evaluated machine learning models, Random Forest achieved the highest predictive accuracy (RMSE = 1.19), demonstrating strong capability in modeling nonlinear process interactions and enabling data-driven optimization of encapsulation processes. The CFD–ML framework provides a computationally efficient tool for encapsulation process optimization, enabling accelerated virtual prototyping and data-driven design in microelectronic packaging.

References

Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019). Optuna: A Next-generation Hyperparameter Optimization Framework. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2623–2631. https://doi.org/10.1145/3292500.3330701

Apalowo, R. K., Abas, A., Bin Salim, M. R., Famuyiwa, S. O., & Kok, C. S. (2026). Heat dissipation performance of solid-state drives under convective cooling: analyzing the impact of heat sink fin spacing. Microelectronics International, 1–16. https://doi.org/10.1108/MI-03-2025-0044

Apalowo, R. K., Abas, M. A., Bachok, Z., Sharif, M. F. M., Che Ani, F., Ramli, M. R., & Mukhtar, M. A. F. bin M. (2024). Deformation and crack growth in multilayered ceramic capacitor during thermal reflow process: numerical and experimental investigation. Microelectronics International, 41(3), 162–171. https://doi.org/10.1108/MI-03-2023-0025/FULL/XML

Apalowo, R. K., Abas, M. A., Mukhtar, M. A. F. M., & Ramli, M. R. (2025). Investigation of the Impacts of Solder Alloy Composition and Temperature Profile on Fatigue Life of Ball Grid Array Solder Joints Under Accelerated Thermal Cycling. Journal of Electronic Packaging, 147(1). https://doi.org/10.1115/1.4065805

Apalowo, R. K., Kareem, A., Ikudehinbu, E., Somefun, D., Ukpoweh, G., Kehinde, E., & Oyeleke, E. (2026). Data-Driven Optimization of Nanoparticle-Reinforced Underfill Encapsulation in Ball Grid Array (BGA) Assemblies. Journal of Metastable and Nanocrystalline Materials, 43, 17–28. https://doi.org/10.4028/p-isii0y

Apalowo, R. K., Muhamed Mukhtar, M. A. F., Abas, M. A., & Che Ani, F. (2024). Numerical modeling and optimization of fillet height of reinforced SAC305 solder joint in an ultra-fine capacitor assembly. Soldering & Surface Mount Technology, 36(5), 309–322. https://doi.org/10.1108/SSMT-04-2024-0020

Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. The Journal of Machine Learning Research. https://doi.org/10.5555/2188385.2188395

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324

Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-August-2016, 785–794. https://doi.org/10.1145/2939672.2939785

Hassan, R. U., Khalil, S. M., Khan, S. A., Moon, J., Cho, D. H., & Byun, D. (2023). Electric field and viscous fluid polarity effects on capillary-driven flow dynamics between parallel plates. Heliyon, 9(6). https://doi.org/10.1016/j.heliyon.2023.e16395

Hung, H.-H., Cheng, Y.-C., Hwang, S.-J., Chen, D.-L., Chang, H.-J., Huang, B.-Y., Huang, H.-H., Wang, C.-C., & Hung, C.-P. (2024). Analysis of flip-chip ball grid array underfill flow process. The International Journal of Advanced Manufacturing Technology 2024 134:9, 134(9), 4851–4870. https://doi.org/10.1007/S00170-024-14304-1

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415

Khor, C. Y., Abdul Mujeebu, M., Abdullah, M. Z., & Che Ani, F. (2010). Finite volume based CFD simulation of pressurized flip-chip underfill encapsulation process. Microelectronics Reliability, 50(1), 98–105. https://doi.org/10.1016/j.microrel.2009.08.007

Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539

Lee, S., Yim, M. J., Master, R. N., Wong, C. P., & Baldwin, D. F. (2008). Void formation study of flip chip in package using no-flow underfill. IEEE Transactions on Electronics Packaging Manufacturing, 31(4), 297–305. https://doi.org/10.1109/TEPM.2008.2002951

Ng, F. C., Abas, A., & Abdullah, M. Z. (2018). Effect of solder bump shapes on underfill flow in flip-chip encapsulation using analytical, numerical and PIV experimental approaches. Microelectronics Reliability, 81, 41–63. https://doi.org/10.1016/J.MICROREL.2017.12.025

Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117. https://doi.org/10.1016/j.neunet.2014.09.003

Stencel, L. C., Strogies, J., Müller, B., Knofe, R., Borwieck, C., & Heimann, M. (2023). Capillary Underfill Flow Simulation as a Design Tool for Flow-Optimized Encapsulation in Heterogenous Integration. Micromachines 2023, Vol. 14, Page 1885, 14(10), 1885. https://doi.org/10.3390/MI14101885

Wan, J. W., Zhang, W. J., & Bergstrom, D. J. (2005). Influence of transient flow and solder bump resistance on underfill process. Microelectronics Journal, 36(8), 687–693. https://doi.org/10.1016/j.mejo.2005.05.022

Wang, H., Su, Y., Wang, W., Sheng, G., Li, H., & Zafar, A. (2019). Enhanced water flow and apparent viscosity model considering wettability and shape effects. Fuel, 253, 1351–1360. https://doi.org/10.1016/j.fuel.2019.05.098

Zhou, S., & Sun, Y. (2012). Multiscale, multiphysics model of underfill flow for flip-chip packages. IEEE Transactions on Components, Packaging and Manufacturing Technology, 2(6), 893–902. https://doi.org/10.1109/TCPMT.2012.2184762

Downloads
Published
2026-03-31
Issue
Vol. 20 No. 1 (2026): FUTA Journal of Engineering and Engineering Technology
Section
Articles
License

Copyright (c) 2026 FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright

With the submission of a manuscript, the corresponding author confirms that the manuscript is not under consideration by another journal. With the acceptance of a manuscript, the Journal reserves the exclusive right of publication and dissemination of the information contained in the article. The veracity of the paper and all the claims therein is solely the opinion of the authors not the journal.

How to Cite

MACHINE LEARNING OPTIMIZATION OF UNDERFILL FLOW TIME IN FLIP-CHIP ENCAPSULATION OF BGA ASSEMBLIES. (2026). FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY, 20(1), 8-20. https://doi.org/10.51459/futajeet.2026.20.1.530

Similar Articles

31-40 of 84

You may also start an advanced similarity search for this article.