An automatic defect classification (ADC) system identifies and classifies wafer surface defects using scanning electron microscope images. By classifying defects, manufacturers can determine whether the wafer can be repaired and proceed to the next fabrication step. Current ADC systems have high defect detection performance. However, the classification power is poor. In most work sites, defect classification is performed manually using the naked eye, which is unreliable. This paper proposes an ADC method based on deep learning that automatically classifies various types of wafer surface damage. In contrast to conventional ADC methods, which apply a series of image recognition and machine learning techniques to find features for defect classification, the proposed model adopts a single convolutional neural network (CNN) model that can extract effective features for defect classification without using additional feature extraction algorithms. Moreover, the proposed method can identify defect classes not seen during training by comparing the CNN features of the unseen classes with those of the trained classes. Experiments with real datasets verified that the proposed ADC method achieves high defect classification performance.
Bibliographical noteFunding Information:
This work was supported in part by NRF through the Ministry of Science, ICT and Future Planning, South Korea, under Grant National Research Foundation-2016R1A2B4008337, and in part by the Graduate School of Yonsei University Research Scholarship Grants in 2018.
Manuscript received January 5, 2019; revised February 15, 2019; accepted February 24, 2019. Date of publication March 4, 2019; date of current version May 3, 2019. This work was supported in part by NRF through the Ministry of Science, ICT and Future Planning, South Korea, under Grant National Research Foundation-2016R1A2B4008337, and in part by the Graduate School of Yonsei University Research Scholarship Grants in 2018. (Corresponding author: Chang Ouk Kim.) S. Cheon, H. Lee, and C. O. Kim are with the Department of Industrial Engineering, Yonsei University, Seoul 03722, South Korea (e-mail: firstname.lastname@example.org).
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All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Industrial and Manufacturing Engineering
- Electrical and Electronic Engineering