臺灣博碩士論文加值系統

批次控制在工業界實行多年，一直受到廣泛的使用，藉由整合統計製程管制及工程製程管制，使製程的變異能夠有效地減少。del Castillo (2002) 利用多變量指數加權移動平均控制器隨時對製程干擾的不良影響進行補償性的調整。
模糊控制器不同於傳統的多變量指數加權移動平均控制器，它是利用模糊集合理論的概念，建構歸屬函數，設計模糊邏輯控制器，達到優於傳統多變量指數加權移動平均控制器的控制效能。吾人以Min-max-gravity方法為基礎，以及利用Gupta, Kiszka and Trojan (1986)的多變量多進多出模糊系統，設計出模糊控制器。討論在多變量白噪音及IMA(1,1)的製程干擾模型影響下，透過這個模糊控制器來替代單一多變量指數加權移動平均控制器及雙重多變量指數加權移動平均控制器做回饋控制，其模擬結果發現相較於單一多變量指數加權移動平均控制器及雙重多變量指數加權移動平均控制器，模糊控制器能夠獲得較小且穩定的MSE統計量。

Run-to-Run control has been extensively applied in semiconductor industry. It has been recognized that these two techniques, statistical process control and engineering process control, can be integrated in prefect harmony to produce more effective tools for process variation reduction. del Castillo (2002) used multivariate EWMA controller to adjust and maintain the dynamic process output on target.
Multivariate fuzzy control is inherently different from conventional multivariate EWMA control. It is designed by fuzzy logic and set concept. As the proposed fuzzy control can structure an appropriate membership function, thus it can be shown more effective than conventional multivariate EWMA control. In this study, fuzzy logic is used to design the multivariate fuzzy controller for MIMO Run-to-Run applications based on min-max-gravity method under Gupta, Kiszka and Trojan (1986)’s MIMO fuzzy system. Under multivariate white noise and IMA (1,1) disturbances, in some specific circumstances the multivariate fuzzy controller can generate stabler control outputs with smaller MSEs than single-MEWMA and double-MEWMA controller.

中文摘要 i
Abstract ii
Acknowledgements iv
Table of Contents v
List of Tables vii
List of Figures ix
Chapter 1. Introduction 1
1.1 Background 1
1.2 Motivation 3
1.3 Problem Statement and Research Goals 5
1.4 Organization 6
Chapter 2. Literature Review 8
2.1 SPC Literature Review 8
2.2 EPC Literature Review 13
2.2.1 Minimum MSE Controller 15
2.2.2 EWMA Controller 16
2.2.3 PID Controller 18
2.2.4 Self-Tuning controller 19
2.3 Run-to-Run Control 20
2.4 Fuzzy Control 22
Chapter 3. Multivariate Fuzzy Control for Noise Disturbance Model and
Deterministic Trend Disturbance Model 31
3.1 MIMO Process Feedback Control 31
3.2 Multivariate Fuzzy Controller 35
3.2.1 Fuzzification 37
3.2.2 Knowledge Base 38
3.2.3 Fuzzy Inference 39
3.2.4 Defuzzification 40
Chapter 4. Simulations and Results Analysis 42
4.1 MIMO Fuzzy Controller for Linear Process Model 42
4.2 Fuzzy Set and Membership Matrix 46
4.3 Results Analysis 51
4.3.1 White Noise Series Without Deterministic Trend Model 53
4.3.2 IMA (1,1) Series Without Deterministic Trend Model 57
4.3.3 White Noise Series With Deterministic Trend Model 63
4.3.4 IMA (1,1) Series With Deterministic Trend Model 67
Chapter 5. Conclusions and Future Research 73
5.1 Conclusions 73
5.2 Future Research 73
Bibliography 75

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