Predictive Modeling of IMC Growth in BGA Component Solder Joints Using Artificial Neural Networks Under Rework and Temperature Cycling Conditions

This paper presents a novel approach using artificial neural networks (ANN) to predict intermetallic compound (IMC) height growth in ball grid array (BGA) component solder joints under varying rework process and temperature cycling test (TCT) conditions. IMC growth is critical for solder joint reliability, directly affecting the performance and lifespan of electronic assemblies. Traditional methods of IMC analysis require extensive experimental testing, which is timeconsuming and resource intensive. By leveraging big data from various rework processes, TCT conditions, and solder joint configurations, this study employs ANN to model and predict IMC height growth, achieving a generalized R-Square of 0.8961 for training and 0.9740 for validation, demonstrating strong predictive performance. The actual versus predicted plots from the ANN analysis validate the model, showing close alignment between predicted and actual values, especially in the validation set, confirming the model's reliability. Additionally, scanning electron microscopy (SEM) inspection was performed on actual solder joint cross-sections to benchmark the ANN model's predictions against real-world samples. The SEM inspection revealed a high correlation between the predicted and measured IMC heights, further confirming the ANN model's accuracy in predicting both BGA and PCB side IMC growth. This approach underscores the benefits of leveraging artificial intelligence and big data to enhance the quality and reliability of solder joints. By providing highly accurate IMC growth predictions, the ANN model allows for optimized rework processes and TCT conditions, reducing the need for excessive experimental trials. This method not only improves solder joint reliability and long-term performance but also facilitates more efficient resource utilization in the electronics manufacturing process.