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Special Graduate Schools
Graduate School of Science and Technology
Department of Industrial Engineering
4. Theses(Ph.D)
Fault Detection with Index Regression Correction and Variational Smoothing for Semiconductor Equipment Sensor Signal
- Author(s)
- 하태경
- Alternative Author(s)
- Ha Tae Kyung
- Advisor
- 신현정
- Department
- 일반대학원 산업공학과
- Publisher
- The Graduate School, Ajou University
- Publication Year
- 2022-02
- Language
- eng
- Alternative Abstract
- Fault detection is an important method in semiconductor manufacturing for monitoring equipment condition and examine the potential causes of the fault. The vacuum leakage is considered one of major faults in semiconductor processing. Unnecessary O2, N2 mixture, major components of atmosphere, creates unexpected process results hence drops yield. Currently available vacuum leak detection systems in vacuum industry are based on helium mass spectrometers. It is used for detecting the vacuum leakage at sole isolation condition where chamber is fully pumped, but unable to be used at in-situ detection while the process is ongoing in the chamber. In this study, a chamber vacuum leak detection method named Index Regression & Correction (IRC) and variational smoothing autoencoder has been presented, utilizing common data which gathered during normal chamber operation. This method was developed by analyzing a simple list of data, such as temperature of the chamber wall and the position of auto pressure control (APC) to detect any change of leakages in the vacuum chamber. First approach is index regression & correction method. We defined vacuum leak modeling based on the engineer's experience. Based on this modeling, index regression was proposed. A prediction method was developed to reduce the error by applying the prediction update loop idea of the Kalman filter. These two loops each other and make predictions by reflecting the previous prediction error. The update term has an error value between the measured value and the predicted value. The best condition result of each parameter was extracted and used from the final results sequence. The best gain of the IRC method was a=0.5, b=0.6. As a result of the accuracy comparison evaluation, the excellent performance was confirmed with an accuracy of 0.70 and an F1 score of 0.78. Second approach is variational smoothing autoencoder. The weakest point of data smoothing is the loss of information. To improve this problem, a variational smoothing method was developed. The length of the process log data is slightly different for each process due to the limit of command processing according to the sequence of the computer. To improve this problem, we partition the time series data and extract the segment information. The extracted segment information is strongly related. So, the autoencoder model was applied to train well on highly relevant data. The proposed method, variational smoothing autoencoder model, showed the best performance, AUC by 0.84 and accuracy by 0.76. The IRC method and variational smoothing autoencoder were effective in classifying abnormalities by predicting time series data of semiconductor facility sensors.
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