Research on Integrated Circuit Package Inspection Based on Machine Vision

This research aims to enhance the inspection of integrated circuit (IC) packages, specifically focusing on the detection of defects such as residual glue and scratches within the plastic packaging of IC chips. The research employs machine vision technology, encompassing processes such as image segmentation, feature extraction, and classification, to effectively detect defects in IC chip plastic packaging. The findings indicate that machine vision analysis significantly improves the accuracy and reliability of defect detection, enhancing production efficiency and product quality. Through deep learning techniques, machine vision analysis demonstrates superior performance in handling various defect types, making it more adaptable and robust compared to traditional gray detection methods. In conclusion, machine vision technology, particularly through deep learning, offers a more effective and efficient approach to IC package inspection, contributing to improved production processes and product quality.