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Knowledge augmented broad learning system for computer vision based mixed-type defect detection in semiconductor manufacturing
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AI
DOI：10.1016/j.rcim.2022.102513.png)
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摘要
En 中文
Defect detection is a critical measurement process for intelligent manufacturing systems to provide insights for product quality improvement. For complex products such as integrated circuit wafers, several types of defects are usually coupled in a piece of wafer to form a mixed-type defect, which poses a challenge to current defect detection methods. This paper proposed a knowledge augmented broad learning system with a knowledge module and broad selective sampling module, which provides a multichannel selective sampling network to decouple the mixed-type defects. In this model, each channel is equipped with a pre-trained deformable convolution model to extract the feature of a fixed single-type defect. The knowledge module is designed to activate the candidate network channel by pre-detection of wafer maps. The experiment results indicated that the proposed model outperforms conventional models and other deep learning models, which demonstrated that the knowledge augmented broad selective sampling mechanism is effective for mixed-type defect detection.
Keyword:
Semiconductor wafer fabrication
Wafer map
Surface defects
Mixed -type defects
Pattern recognition
Broad learning
Deformable convolution
期刊
R
IF:
11.4
论文数:
3.3K
被引数:
1.3W
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