Deep Learning-Enhanced Electronic Packaging Defect Detection via Fused Thermal Simulation and Infrared Thermography

Advancements in semiconductor packaging toward higher integration and interconnect density have increased the risk of structural defects—such as missing solder balls, pad delamination, and bridging—that can disrupt thermal conduction paths, leading to localized overheating and potential chip failure. To address the limitations of traditional non-destructive testing methods in detecting micron-scale defects, this study introduces a multimodal detection approach combining finite-element thermal simulation, infrared thermography, and the YOLO11 deep learning network. A comprehensive 3D finite-element model of a ball grid array (BGA) package was developed to analyze the impact of typical defects on both steady-state and transient thermal distributions, providing a solid physical foundation for modeling defect-induced thermal characteristics. An infrared thermal imaging platform was established to capture real thermal images, which were then compared with simulation results to verify physical consistency. An integrated dataset of simulated and infrared images was constructed to enhance the robustness of the detection model. Leveraging the YOLO11 network’s capabilities in end-to-end training, dataset small-object detection, and rapid inference, the system achieved accurate and rapid localization of defect regions. Experimental results show a mean average precision (mAP) of 99.5% at an intersection over union (IoU) threshold of 0.5 and an inference speed of 556 frames per second on the simulation dataset. Training with the hybrid dataset improved detection accuracy on real images from 41.7% to 91.7%, significantly outperforming models trained on a single data source. Furthermore, the maximum temperature discrepancy between simulation and experimental measurements was less than 5%, validating the reliability of the proposed method. This research offers a high-precision, real-time solution for semiconductor packaging defect detection, with substantial potential for industrial application.