Browse > Article

Semi-Supervised Learning for Fault Detection and Classification of Plasma Etch Equipment  

Lee, Yong Ho (Department of Electronics Engineering, Myongji University)
Choi, Jeong Eun (Department of Electronics Engineering, Myongji University)
Hong, Sang Jeen (Department of Electronics Engineering, Myongji University)
Publication Information
Journal of the Semiconductor & Display Technology / v.19, no.4, 2020 , pp. 121-125 More about this Journal
Abstract
With miniaturization of semiconductor, the manufacturing process become more complex, and undetected small changes in the state of the equipment have unexpectedly changed the process results. Fault detection classification (FDC) system that conducts more active data analysis is feasible to achieve more precise manufacturing process control with advanced machine learning method. However, applying machine learning, especially in supervised learning criteria, requires an arduous data labeling process for the construction of machine learning data. In this paper, we propose a semi-supervised learning to minimize the data labeling work for the data preprocessing. We employed equipment status variable identification (SVID) data and optical emission spectroscopy data (OES) in silicon etch with SF6/O2/Ar gas mixture, and the result shows as high as 95.2% of labeling accuracy with the suggested semi-supervised learning algorithm.
Keywords
Machine Learning; Semi-supervised Learning; Labelling; Fault Detection and Classification; Optical Emission Spectroscopy;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 P. S. Peercy, "The drive to miniaturization," Nature., Vol. 406, No. 6799, pp. 1023-1026, 2000.   DOI
2 Q. P. He and J. Wang, "Fault Detection Using the k-Nearest Neighbor Rule for Semiconductor Manufacturing Processes," IEEE Trans. Semi. Manufac., Vol. 20, No. 4, pp. 345-354, 2007.   DOI
3 Kim, Dongil, et al. "Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing," Expert Systems with Applications., Vol. 39, No. 4, pp. 4075-4083, 2012.   DOI
4 D. H. Kim, J. E. Choi, T. M. Ha and S. J. Hong, "Modeling with Thin Film Thickness using Machine Learning," Journal of the Semiconductor & Display Technology, Vol. 18, No. 2, pp. 48-52, 2019.
5 S. J. Hong, G. S. May and D. C. Park, "Neural network modeling of reactive ion etching using optical emission spectroscopy data," IEEE Transactions on Semiconductor Manufacturing, Vol. 16, No. 4, pp. 598-608, 2003.   DOI
6 K. Nakata, R. Orihara, Y. Mizuoka and K. Takagi, "A comprehensive big-data-based monitoring system for yield enhancement in semiconductor manufacturing," IEEE Transactions on Semiconductor Manufacturing, Vol. 30, No. 4, pp. 339-344, 2017.   DOI
7 S. J. Hong, W. Y. Lim, T. Cheong and G. S. May, "Fault Detection and Classification in Plasma Etch Equipment for Semiconductor Manufacturing $e$-Diagnostics," IEEE Trans. Semi. Manufac., Vol. 25, No. 1, pp. 83-93, 2012.   DOI
8 K. B. Lee, S. Cheon and C. O. Kim, "A convolutional neural network for fault classification and diagnosis in semiconductor manufacturing processes," IEEE Transactions on Semiconductor Manufacturing, Vol. 30, No. 2, pp. 135-142, 2017.   DOI
9 Chou, Paul B., et al. "Automatic defect classification for semiconductor manufacturing," Machine Vision and Applications., Vol. 9, No. 4, pp. 201-214, 1997.   DOI
10 P. K. Mallapragada, R. Jin, A. K. Jain and Y. Liu, "SemiBoost: Boosting for Semi-Supervised Learning," IEEE Trans. Pattern Analysis and Machine Intelligence., Vol. 31, No. 11, pp. 2000-2014, 2009.   DOI
11 T. Sarmiento, S. J. Hong and G. S. May, "Fault detection in reactive ion etching systems using one-class support vector machines," IEEE/SEMI Conference and Workshop on Advanced Semi. Manufac., pp. 139-142, 2005.
12 Xiaojin, Zhu, and Ghahramani Zoubin, "Learning from labeled and unlabeled data with label propagation," Synthesis lectures on artificial intelligence and machine learning., Vol. 3, No. 1, pp. 1-130, 2009.
13 J. H. Han and S. S. Hong, "Semiconductor Process Inspection Using Mask R-CNN," Journal of the Semiconductor & Display Technology, Vol. 19, No. 3, pp. 12-18, 2020.