## 4 Conclusions  
This paper presents an integrated defect detection framework for semiconductor packaging, combining finite-element thermal simulation, infrared thermal imaging, and YOLO11-based deep learning. By modeling typical defects--including missing solder balls, missing pads, and solder ball bridging--the study systematically analyzed their influence on steady-state and transient thermal distributions. The simulation results demonstrated strong consistency with experimental infrared measurements, validating the physical accuracy of the simulation model.  
A hybrid dataset composed of simulated and experimentally captured thermal images was constructed to train the YOLO11 network. The proposed model achieved a high detection accuracy of 99.5% mAP50 on the simulation dataset and maintained a real-time inference speed of 556 FPS. More importantly, integrating real thermal images into the training process improved the model's defect recognition rate on experimental data from 41.7% to 91.7%, effectively addressing domain adaptation challenges. These results confirm that the proposed multimodal framework offers a practical, high-precision, and real-time solution for industrial semiconductor packaging defect detection.  
Although the hybrid dataset significantly improves the model performance, it still has the following limitations: First, the number of experimental image samples is limited, and the defect types are unevenly distributed, which may affect the model's ability to distinguish between multiple types of targets. Second, the resolution of infrared imaging is limited, and the boundaries of some defects are blurred, affecting the accuracy of target positioning. In future work, efforts will focus on enlarging the experimental dataset, integrating advanced imaging technologies, refining the model for edge deployment, and extending the method to support a wider range of packaging structures and defect types. These advancements aim to promote the development of intelligent, automated, and scalable defect detection solutions for semiconductor manufacturing.  
Conceptualization, H.H.; methodology, Z.P. and H.H.; investigation, Z.P. and H.H.; writing--original draft preparation, Z.P. and H.H.; writing--review and editing, Z.P. and H.H.; supervision, H.H.; project administration, H.H.; funding acquisition, H.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Natural Science Foundation of Hunan Province (2025 JJ50276), Changsha Natural Science Foundation (kq2502121), and Shenzhen Science and Technology Plan Project (CJGJZD202020517141804010).  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model Dataset**} & **Number of** & **Recognized** & **Unrecognized** & **Recognition** \\  & **Test Images** & **Defects** & **Defects** & **Rate** \\ \hline Simulation Dataset & 12 & 5 & 7 & 41.7\% \\ Hybrid Datasets & 12 & 11 & 1 & 91.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of defect detection performance on real infrared images using models trained with different datasets, showing the number of successfully identified defects and corresponding recognition rates.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy issue.  
**Acknowledgments:** We would like to thank [PERSON] for useful discussion.  
**Conflicts of Interest:** The authors declare no conflicts of interest.