Wafer defect maps have different generation mechanisms according to the defect pattern, and automatic classification of wafer maps is therefore critical to reveal the root cause of the defects. In this paper, we examine the open set recognition problem, in which not only must wafer maps be classified using major defect patterns that are already known but also unknown defect patterns must also be detected. Our model is an ensemble model of a one-versus-one method that uses a convolutional neural network as the base classifier for wafer map classification. The proposed model calculates a weighted mean score for each defect pattern and determines the presence or absence of a pattern based on this score. The weight is calculated based on the proximity of data groups in the feature space and can be considered a support level at which a new wafer map belongs to a specific defect pattern. An untrained wafer map input into the model has a low support level and thus does not belong to any known defect pattern. An experiment was conducted using work-site failure bit count maps.
Bibliographical noteFunding Information:
Manuscript received June 16, 2020; revised July 22, 2020; accepted July 23, 2020. Date of publication July 27, 2020; date of current version October 29, 2020. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) under Grant NRF-2019R1A2B5B01070358. (Corresponding author: Chang Ouk Kim.) The authors are with the Department of Industrial Engineering, Yonsei University, Seoul 03722, South Korea (e-mail: firstname.lastname@example.org). Digital Object Identifier 10.1109/TSM.2020.3012183
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Industrial and Manufacturing Engineering
- Electrical and Electronic Engineering