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적응형 의사결정 트리와 최단 경로법을 이용한 기계 진단 및 보전 정책 수립
백준걸 한국경영과학회 2002 韓國經營科學會誌 Vol.27 No.2
CBM (Condition-Based Maintenance) has increasingly drawn attention in industry because of its many benefits. CBM problem is characterized as a state-dependent scheduling model that demands simultaneous maintenance actions, each for an attribute that influences on machine condition. This problem is very hard to solve within conventional Marlov decision process framework. In this paper, we present an intelligent machine maintenance scheduler, for which a new incremental decision tree learning method as evolutionary system identification model and shortest path problem as schedule generation model are developed. Although our approach does not guarantee an optimal scheduling policy in mathematical viewpoint, we verified through simulation based experiment that the intelligent scheduler is capable of providing good scheduling policy that can be used in practice.
Fault Detection of Cycle-Based Signals Using Wavelet Transform in FAB Processes
Kim, Jun-Seok,Lee, Jae-Hyun,Kim, Ji-Hyun,Baek, Jun-Geol,Kim, Sung-Shick 한국정밀공학회 2010 International Journal of Precision Engineering and Vol.11 No.2
This paper presents a wavelet multiresolution analysis based process fault detection algorithm to improve the accuracy of fault detection. Using Haar wavelet, coefficients that well reflect the process condition are selected and Hotelling's T2 control chart that uses the selected coefficients is constructed for assessing the process condition. To enhance the overall efficiency and accuracy of fault detection, the following two steps are suggested: First, a denoising method that is based on wavelet transform and soft-thresholding. Second, coefficient selection methods that use the difference in the variance. For performance evaluation, various types of abnormal process conditions are simulated and the proposed algorithm is compared with other methodologies. Also, We apply the proposed algorithm to the industrial data of the dry etching process, which is one of the FAB processes. Our method has a better fault-detection performance for various sections and various changes in mean than other methods.
Pattern Classification for Small-Sized Defects Using Multi-Head CNN in Semiconductor Manufacturing
Yunseon Byun,Jun-Geol Baek 한국정밀공학회 2021 International Journal of Precision Engineering and Vol.22 No.10
To improve the quality of semiconductor manufacturing, defects need to be detected and their root causes controlled. Because the root causes can vary depending on defect patterns, classifying the patterns accurately is important. Several recent studies have investigated automatic defect classification using a convolutional neural network (CNN) with wafer map images. CNNs are excellent tools for classifying images of different shapes and sizes. However, the detection of small-sized defects that have small clusters and linear patterns is difficult. Therefore, this study focuses on patterns that are difficult to detect. We propose three steps for pattern classification. First, modified median filtering is used to preserve the original shapes of patterns. Second, a rotated defects (RoD) transform is performed by applying the rotational properties of wafer maps. The RoD transform augments the defect proportion and improves the detection of small-sized defects. Third, a multi-head CNN is used to extract and combine the features from the original and transformed maps. The combined features are then used to classify the defect patterns. Overall classification performance of defects can be improved by accurately classifying small clusters and linear patterns. The proposed model was evaluated using WM-811K wafer maps, and small-sized defects were accurately classified. Such an accurate defect classification model will enable effective root cause analysis and quality improvement in semiconductor manufacturing.