High-Precision X-ray Inspection of Flip Chip Packaging Leveraging Deep Learning and Instance Segmentation for Manufacturing Process Optimization

This paper presents an innovative methodology for automated defect recognition in X-ray images by leveraging advanced Instance Segmentation techniques. The proposed approach accurately identifies and delineates defect regions, which are subsequently processed by a deep learning-based classification model to categorize defects based on their distinct morphological and textural characteristics. To enhance the model's robustness and mitigate false positives, a series of morphological operations, including Dilation, Erosion, and filtering, are applied to pre-process the data. Dilation is used to expand the boundaries of detected defect regions, ensuring that small gaps and holes within the defects are filled. Erosion, on the other hand, helps in removing small noise and separating closely located defect regions. Filtering techniques, such as Gaussian or median filtering, are employed to smooth the image and further reduce noise, thereby improving the quality of the input data for the segmentation and classification models. Furthermore, Image Augmentation techniques, such as rotation, scaling, translation, and flipping, are utilized to artificially expand the dataset. This augmentation not only increases the volume of training data but also introduces variability in the defect appearances, which helps in improving the model's generalization capabilities and feature diversity. By exposing the model to a wider range of defect scenarios, the accuracy and robustness of the defect recognition process are significant enhanced. The integrated approach combines the strengths of Instance Segmentation and classification models, along with robust pre-processing and augmentation strategies, to offer a substantial improvement in the efficiency and reliability of X-ray image-based defect analysis. This methodology has significant implications for quality control in industrial applications, where accurate and efficient defect detection is critical for maintaining product standards and reducing manufacturing cost.