A Hybrid Deep Learning-Based Framework for Chip Packaging Fault Diagnostics in X-Ray Images

人工智能 稳健性(进化) 计算机科学 规范化(社会学) 炸薯条 计算机视觉 分割 模板匹配 模式识别(心理学) 故障检测与隔离 图像分割 深度学习 图像(数学) 人类学 社会学 基因 电信 生物化学 执行机构 化学
作者
Jie Wang,Gaomin Li,Haoyu Bai,Guixin Yuan,Xuan Li,Bin Lin,Lijun Zhong,Xiaohu Zhang
出处
期刊:IEEE Transactions on Industrial Informatics [Institute of Electrical and Electronics Engineers]
卷期号:20 (9): 11181-11191 被引量:1
标识
DOI:10.1109/tii.2024.3397360
摘要

In the testing of chips, defect diagnostics in X-ray images of packaging chips is mainly performed by humans, which is time-consuming and inefficient. To overcome the abovementioned problems, a novel intelligent defect diagnostics system based on hybrid deep learning for chip X-ray images was proposed. The system consists of four successive stages: image segmentation and normalization, image reconstruction and defect detection, contour matching, and qualification diagnosis. The first stage is used to localize the external contours of the target chip and remove extraneous backgrounds through the improved UNet. Then, considering the variety of defects and the complexity of labeling, an unsupervised learning model is designed to reconstruct defect-free images to detect defects, which requires only normal samples for training. Third, the multicomponent template matching based on structural prior is used to localize the internal contours of the chip. In the final stage, the qualification is diagnosed based on the previous results through the Floyd–Warshall algorithm. The effectiveness and robustness of the proposed methods are verified by experiments on real-world inspection lines. The experimental results demonstrate that the developed system can successfully perform fault diagnostics tasks, achieving a judgment accuracy of 92.5%.
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