Knowledge augmented broad learning system for computer vision based mixed-type defect detection in semiconductor manufacturing |
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| Affiliation: | 1. Shanghai Engineering Research Center of Industrial Big Data and Intelligent System, Institute of Artificial Intelligence, Donghua University, Shanghai, China;3. College of Mechanical Engineering, Donghua University, Shanghai, China;4. School of Computer sciences, China University of Geosciences, Wuhan, China;5. College of Information Science and Technology, Donghua University, Shanghai, China;1. School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China;2. College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;1. School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao, Shandong China;2. School of Mechanical Engineering, Shandong University, Jinan, Shandong, China;3. School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China;1. School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China;2. School of Mechanical-electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;3. School of Mechanical Engineering, Shandong University, Jinan 250061, China;4. Institute of Systems Engineering, Macau University of Science and Technology, Macau 999078, China;5. Institute of Transport Management, Ministry of Transport, Beijing 101601, China |
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| Abstract: | 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. |
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| Keywords: | Semiconductor wafer fabrication Wafer map Mixed-type defects |
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