지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 JOURNAL OF SEMICONDUCTOR TECHNOLOGY AND SCIENCE Journal of Semiconductor Technology and Science Vol.14 No.2
- 발행연도
- 2014.4
- 수록면
- 252 - 261 (10page)
이용수
초록· 키워드
오류제보하기
A Bayesian network (BN) based fault diagnosis framework for semiconductor etching equipment is presented. Suggested framework contains data preprocessing, data synchronization, time series modeling, and BN inference, and the established BNs show the cause and effect relationship in the equipment module level. Statistically significant state variable identification (SVID) data of etch equipment are preselected using principal component analysis (PCA) and derivative dynamic time warping (DDTW) is employed for data synchronization. Elman’s recurrent neural networks (ERNNs) for individual SVID parameters are constructed, and the predicted errors of ERNNs are then used for assigning prior conditional probability in BN inference of the fault diagnosis. For the demonstration of the proposed methodology, 300 mm etch equipment model is reconstructed in subsystem levels, and several fault diagnosis scenarios are considered. BNs for the equipment fault diagnosis consists of three layers of nodes, such as root cause (RC), module (M), and data parameter (DP), and the constructed BN illustrates how the observed fault is related with possible root causes. Four out of five different types of fault scenarios are successfully diagnosed with the proposed inference methodology.
목차
Abstract
I. INTRODUCTION
II. THEORETICAL BACKGROUND
III. DIAGNOSTIC METHODOLOGY
IV. IMPLEMENTATION
V. CONCLUSION
REFERENCES
참고문헌 (15)
참고문헌 신청
1. [학술대회논문] - S. J. Hong / 2012 / Fault Detection and Classification in Plasma Etch Equipment for Semiconductor Manufacturing e-Diagnostics / IEEE Trans. Semi. Manufac. 16 (1) : 83 ~ 93
2. [학술지(정기간행물)] - B. E. Goodlin / 2003 / Simultaneous Fault Detection and Classification for Semiconductor Manufacturing Tools / J. Electrochem. Soc. 150 (12) : G778 ~ G784
3. [학술대회논문] - A. M. Ison / 1997 / Fault Diagnosis of Plasma Etch Equipment / Proc. Int. Symp. Semi. Manufac., San Francisco, CA : B49 ~ B52
4. [학술대회논문] - L. J. Barrios / 1998 / Autoassociative Neural Networks for Fault Diagnosis in Semiconductor Manufacturing / Proc. 11th Int. Conf. on Ind. Eng. App. of Artificial Intelligence and Expert Systems, Castellón, Spain : 582 ~ 592
5. [학술지(정기간행물)] - S. Baluja / 2011 / Artificial Neural Network Based Detection and Diagnosis of Plasma-Etch Faults / J. Intel. Sys. 7 (1-2) : 57 ~ 82
2. [학술지(정기간행물)] - B. E. Goodlin / 2003 / Simultaneous Fault Detection and Classification for Semiconductor Manufacturing Tools / J. Electrochem. Soc. 150 (12) : G778 ~ G784
3. [학술대회논문] - A. M. Ison / 1997 / Fault Diagnosis of Plasma Etch Equipment / Proc. Int. Symp. Semi. Manufac., San Francisco, CA : B49 ~ B52
4. [학술대회논문] - L. J. Barrios / 1998 / Autoassociative Neural Networks for Fault Diagnosis in Semiconductor Manufacturing / Proc. 11th Int. Conf. on Ind. Eng. App. of Artificial Intelligence and Expert Systems, Castellón, Spain : 582 ~ 592
5. [학술지(정기간행물)] - S. Baluja / 2011 / Artificial Neural Network Based Detection and Diagnosis of Plasma-Etch Faults / J. Intel. Sys. 7 (1-2) : 57 ~ 82
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UCI(KEPA) : I410-ECN-0101-2015-560-001440653