Data-Driven Optimization of Nanoparticle-Reinforced Underfill Encapsulation in Ball Grid Array (BGA) Assemblies

Abstract:

This study investigated the influence of nanoparticle material type and weight percentage on the flow behaviour of underfill encapsulation in Ball Grid Array (BGA) assemblies. As BGA packages are increasingly used in high-density and high-performance electronic devices, ensuring reliable solder joint encapsulation becomes critical. While nanoparticle-reinforced underfills enhance thermal and mechanical performance, they also introduce complexities in flow behaviour due to changes in viscosity and particle–fluid interactions. To address this, a multiphase numerical model was developed using the Finite Volume Method (FVM) and the Discrete Phase Model (DPM) in ANSYS Fluent to simulate the transient flow of underfill resin reinforced with Al₂O₃, SiO₂, and TiO₂ nanoparticles at varying weight percentages (5%, 10%, 15%, and 20%). The simulation captured the progression of fluid fill at intervals (25%, 50%, 75%, 95%) and measured total flow time. Results revealed Al₂O₃-based underfill consistently achieved faster flow, with the shortest 95% fill time recorded at 69.84 seconds for a 17.16% weight load concentration, while SiO₂-based underfill had the slowest flow, with times exceeding 74 seconds at 20% loading. These differences were attributed to variations in nanoparticle density and dispersion behaviour. A Random Forest regression model trained on simulation data further confirmed that nanoparticle type and concentration were the most significant predictors of flow time. These findings demonstrate that optimal nanoparticle selection can balance mechanical reinforcement with manufacturability. The results offer practical insights for electronics manufacturers aiming to improve process throughput and reliability in advanced packaging by selecting suitable nanoparticle-enhanced underfill formulations.Keywords: Underfill encapsulation, Nanoparticle reinforcement, Finite Volume Method, Discrete Phase Model, Artificial Neural Network.

• Periodical: Journal of Metastable and Nanocrystalline Materials (Volume 43)
• Pages: 17-28
• DOI: https://doi.org/10.4028/p-iSIi0Y
• Online since: January 2026
• Authors: Rilwan Kayode Apalowo, Alfred Kareem*, Ebenezer Ikudehinbu, David Somefun, Gift Ukpoweh, Emmanuel Kehinde, Elijah Oyeleke
• Keywords: Artificial Neural Network, Discrete Phase Model, Finite Volume Method, Nanoparticle Reinforcement, Underfill Encapsulation

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