臺灣博碩士論文加值系統

半導體產業為現今台灣最重要且資金稠密度最高的產業之一。隨著科技的發展與製程技術的改善，晶圓尺寸日益增大；而其製作出之元件/晶片的尺寸則日趨輕薄短小。針對此高精密度的製程，如何即時有效瞭解機台的健康狀況及其對應之製程表現，進而提升製程良率、增進產出並降低成本，實為一極具挑戰性的重要議題。

此外，由於半導體製程之步驟繁複且歷時甚久，產品的品質特徵值在各製程加工中難以即時取得。如何仰賴生產線之機台製程資料，定義機台各子系統的健康指標並且有效偵測出製程之異常表現，為現今半導體業製程監控工作之重大挑戰。本研究提出一系統性的半導體製程品質管制監控系統，依據生產線之機台製程資料，可有效並快速的偵測異常製程並且提供機台的即時健康情況之分析。

Semiconductor industry is one of the most important and capital-intensive industries in Taiwan. As the trend of extended wafer size and narrowed feature size of chips and dies, the powerful and suitable control scheme for such highly accurate manufacturing becomes the essential issue to improve the yield, enhance the throughput and decrease the cost.

Due to the rare output observation known as metrology compared with the huge sensor data set collected on-line over the whole process, it is the crucial challenge to detect the abnormal performance of process and analyze the health conditions of equipment effectively within the shortest possible time. In this research, the novel fault detection and classification, FDC, system is provided to perform the fast and precise diagnosis for process/equipment healthy performance determination.

ABSTRACT I
CHINESE ABSTRACT II
ACKNOWLEDGEMENT III
CONTENTS IV
FIGURE AND TABLE CAPTIONS V
CHAPTER 1.　INTRODUCTION 1
1.1. BACKGROUND 1
1.2. MOTIVATION 2
1.3. ORGANIZATION 3
CHAPTER 2.　LITERATURE REVIEW 5
2.1. STATISTICAL PROCESS CONTROL, SPC 5
2.2. FAULT DETECTION AND CLASSIFICATION, FDC 6
2.3. HIDDEN EXTRAPOLATION 8
2.4. WESTERN ELECTRIC RULES, WERS 8
CHAPTER 3.　CONCEPTS OF NEW FDC FOR SEMICONDUCTOR INDUSTRY 10
3.1. DATA DISCRIMINATION 11
3.2. HIDDEN EXTRAPOLATION IN FDC FOR IC PROCESS 12
3.3. SCORING MECHANISM 14
3.4. FAULTY WAFER/EQUIPMENT DIAGNOSIS 15
CHAPTER 4.　ADJUSTMENTS OF WERS (WESTERN ELECTRIC RULES) FOR THE AUTO-CORRELATED DATA 17
4.1. SIMULATION EXPERIMENT AND RESULTS OF MODIFIED WERS 17
4.2. THE FURTHER DISCUSSION AND SUMMARY ON SIMULATION RESULTS 21
CHAPTER 5.　ILLUSTRATED EXAMPLES AND CONCLUSIONS 24
5.1. TWO ILLUSTRATED EXAMPLES 24
5.2. CONCLUSIONS AND FUTURE RESEARCH 30
REFERENCE 31

[1]. Alwan, L. C. and Roberts, H. V. (1988), “Time-series Modeling for Statistical Process Control,” Journal of Business and Economic Statistics, 6, 87-95.
[2]. Alwan, A. J. and Alwan, L.C. (1994), “Monitoring Autocorrelated Processes Using Multivariate Quality Control Charts,” Proceedings of the Decision Sciences Institute Annual Meeting, 3, 2106-2108.
[3]. Apley, D. W. and Tsung F. (2002), “The Autoregressive T2 Chart for Monitoring Univariate Autocorrelated Processes,” Journal of Quality Technology, 34, 80-96.
[4]. Chen, A., Guo, R. S., Yang, A. and Tseng, C. L. (1998), “An Integrated Approach to Semiconductor Equipment Monitoring,” Journal of The Chinese Society of Mechanical Engineering, a special issue on Advanced Automation Technology and Production Research, 19, 581-591.
[5]. Goodlin, B. E., Boning, D. S., Sawin, H. H. and Wise, B. M. (2003), “Simultaneous Fault Detection and Classification for Semiconductor Manufacturing Tools,” Journal of the Electrochemical Society, 150, 778-784.
[6]. Ingolfsson, A. and Sachs, E. (1993), “Stability and Sensitivity of an EWMA Controller,” Journal of Quality Technology, 25, 271-287.
[7]. Johnson, R. A. and Wichern, D. W. (1990), Applied Multivariate Statistical Analysis, 4th Edition, NJ: Prentice Hall.
[8]. Kalgonda, A. A. and Kulkarni, S. R. (2004), “Multivariate Quality Control Chart for Autocorrelated Processes,” Journal of Applied Statistics, 31, 317-327.
[9]. Kramer, H. G. and Schmid, W. (1997), “EWMA Charts for Multivariate Time Series,” Sequential Analysis, 16, 131-154.
[10]. Krieger, C. A., Champ, C. W. and Alwan, L. C. (1992), “Monitoring an Autocorrelated Process,” Pittsburgh Conference on Modeling and Simulation, Pittsburgh, PA., USA.
[11]. Lu, C. W. and Reynolds, M. R. Jr. (1991), “Control Charts for Monitoring the Mean and Variance of Autocorrelated Process,” Journal of Quality Technology, 31, 259-273.
[12]. Montgomery, D.C. and Mastrangelo, C. M. (1991), “Some Statistical Process Control Methods for Autocorrelated Data,” Journal of Quality Technology, 23, 179-204.
[13]. Montgomery, D.C. (2005), Introduction to Statistical Quality Control, 5th Edition, New York: John Wiley & Sons.
[14]. Neter J, Kutner, M. H., Nachtsheim, C. J. and Wassweman W. (1990), Applied Linear Regression Models, 3rd Edition, Chicago, IL: Irwin.
[15]. Pan, X. (2005), “Notes on Shift Effects for T2-type Charts on Multivariate ARMA Residuals,” Computers & Industrial Engineering, 49, 381-392.
[16]. Qin, S. J., Cherry, G., Good, R., Wang, J. and Harrison, C. A. (2006), “Semiconductor Manufacturing Process Control and Monitoring: A Fab-wide Framework,” J. Process Control, 16, 179-191.
[17]. Reinsel, G. C. (1993), Elements of Multivariate Time Series Analysis, 1st Edition, New York: Springer-Verlag.
[18]. Rousseeuw, P. J. and Leory, A. M. (1987), Robust Regression and Outlier Detection, 1st Edition, New York: John Wiley & Sons.
[19]. Shewhart, W. A. (1980), Economic Control of Quality of Manufactured Product, Milwaukee, Wis.: American Society for Quality Control. Originally published: New York: Van Nostrand, 1931.
[20]. Vasilopoulos, A. V. and Stamboulis, A. P. (1978), “Modification of Control Chart Limits in the Presence of Data Correlation,” Journal of Quality Technology, 10, 20-30.
[21]. Weisberg, S. (2005), Applied Linear Regression, 3rd Edition, NJ: Wiley-Interscience.
[22]. Western Electric Company (1956), Statistical Quality Control Handbook, Delmar Printing Company, Charlotte, NC.
[23]. Wu, G. X. (1999), Real-time Equipment Health Evaluation and Dynamic Preventive Maintenance Policy, dissertation, NTU.
[24]. 行政院國家科學委員會自然科學發展處學門與資源規劃報告:統計科學,九十四年十月修訂