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研究生:張鈞程
研究生(外文):Chang, Chun-Cheng
論文名稱:分群方法與批次控制於高度混和生產半導體製程之應用
論文名稱(外文):Grouping Method and Run-to-run Control for High-mix Semiconductor Manufacturing Process
指導教授:鄭西顯鄭西顯引用關係
指導教授(外文):Jang, Shi-Sheng
學位類別:博士
校院名稱:國立清華大學
系所名稱:化學工程學系
學門:工程學門
學類:化學工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:英文
論文頁數:87
中文關鍵詞:批次控制高度混和製程EWMA控制器
外文關鍵詞:Run-to-run controlHigh-mixEWMA
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In mixed run processes, typical in semiconductor manufacturing and other automated assembly-line type process, products with different recipe will be produced on the same tool. Product based run-to-run control can be applied to improve the process capability. The effect of product-based controller on low frequency products is, however, minimal, due to inability to track tool variations. In first work, we propose a group and product based EWMA control scheme which combines adaptive k-means cluster method and run-to-run EWMA control to improve the performance of low frequency products in the mixed run process. Similar products could be classified into the same group adaptively and controlled by a group EWMA controller. The group controller is updated by both low frequency products and similar high frequency products; so that low frequency products can be improved by shared information from similar large frequency products. However, the high frequency products are controlled by individual product-based EWMA to avoid interference of the low frequency products. The advantages of proposed control scheme are demonstrated by benchmark simulation and reversed engineered industrial applications.
The performance of EWMA RtR controllers is affected by the values of the selected tuning parameter. In practice, the tuning parameter usually remains unchanged, resulting in sub-optimal performance. In second work, we propose an adaptive-tuning method for a G&P EWMA controller to improve the control performance. The G&P EWMA controller is developed for mixed run processes. We show that the optimum tuning parameters for the next run of this G&P EWMA controller are obtained online using a window of historical input–output data. The performance improvement due to the proposed method is demonstrated by a simulation example and an industrial application.
Run-to-Run control algorithms for high-mix semiconductor processes typically require that the initial product state estimates have sufficient accuracy for satisfactory control. In third work, we use historical process data and apply single observation just-in-time adaptive disturbance estimation (JADE) to find the initial product state estimates. Single observation JADE with random selection, high frequency sampling and exclusion of the earliest data from the average is shown to provide satisfactory initial product state estimates. The effect of initial state estimate accuracy is demonstrated by several simulation and industrial data examples. We also provide a method to estimate relative confidence between individual product state estimates, information that may be used to determine assignment of process error between the tool and product state.

In mixed run processes, typical in semiconductor manufacturing and other automated assembly-line type process, products with different recipe will be produced on the same tool. Product based run-to-run control can be applied to improve the process capability. The effect of product-based controller on low frequency products is, however, minimal, due to inability to track tool variations. In first work, we propose a group and product based EWMA control scheme which combines adaptive k-means cluster method and run-to-run EWMA control to improve the performance of low frequency products in the mixed run process. Similar products could be classified into the same group adaptively and controlled by a group EWMA controller. The group controller is updated by both low frequency products and similar high frequency products; so that low frequency products can be improved by shared information from similar large frequency products. However, the high frequency products are controlled by individual product-based EWMA to avoid interference of the low frequency products. The advantages of proposed control scheme are demonstrated by benchmark simulation and reversed engineered industrial applications.
The performance of EWMA RtR controllers is affected by the values of the selected tuning parameter. In practice, the tuning parameter usually remains unchanged, resulting in sub-optimal performance. In second work, we propose an adaptive-tuning method for a G&P EWMA controller to improve the control performance. The G&P EWMA controller is developed for mixed run processes. We show that the optimum tuning parameters for the next run of this G&P EWMA controller are obtained online using a window of historical input–output data. The performance improvement due to the proposed method is demonstrated by a simulation example and an industrial application.
Run-to-Run control algorithms for high-mix semiconductor processes typically require that the initial product state estimates have sufficient accuracy for satisfactory control. In third work, we use historical process data and apply single observation just-in-time adaptive disturbance estimation (JADE) to find the initial product state estimates. Single observation JADE with random selection, high frequency sampling and exclusion of the earliest data from the average is shown to provide satisfactory initial product state estimates. The effect of initial state estimate accuracy is demonstrated by several simulation and industrial data examples. We also provide a method to estimate relative confidence between individual product state estimates, information that may be used to determine assignment of process error between the tool and product state.

Abstract I
Table of Contents III
List of Figures V
List of Tables VIII
Chapter 1. Introduction 1
1.1. High-mix Semiconductor Manufacturing Process 1
1.2. Run to Run Control 3
1.2.1. Overview 3
1.2.2. EWMA algorithm 4
1.2.3. The extension of EWMA algorithm 8
1.2.4. Thread and non-thread control algorithm 9
1.3. Research Objectives 11
1.4. Structure of the Dissertation 13
Chapter 2. A Group and Product Based EWMA Algorithm 14
2.1. Introduction 14
2.2. Theory 14
2.2.1. Simple Product-Based EWMA (P-EWMA) 14
2.2.2. Group- and Product-Based EWMA control (G&P-EWMA) 15
2.2.3. Adaptive K-means Cluster Algorithm 19
2.2.4. Performance Criterion 20
2.3. Simulation Studies 21
2.3.1. Performance of G&P-EWMA Algorithm 21
2.3.2. Information Exchange between P-EWMA and G-EWMA 25
2.3.3. Segregation of High Frequency Products 27
2.4. Industrial Data 29
2.5. Conclusions 35
Chapter 3. Auto-tuning Scheme for G&P-EWMA Controller 36
3.1. Introduction 36
3.2. Theory 36
3.2.1. Tuning an EWMA Controller 36
3.2.2. Performance Criteria 39
3.3. Results and Discussions 41
3.3.1. Simulates Cases 41
3.3.2. Industrial Case 47
3.4. Conclusions 52
Chapter 4. The Effect of Initial State Estimates on Just-in-Time Adaptive Disturbance Estimation 53
4.1. Introduction 53
4.2. Theory 53
4.2.1. The High-mix Initial State Estimation Method 53
4.2.2. Performance Criteria 58
4.3. Simulation Examples 59
4.3.1. Effect of Weights 59
4.3.2. Step and IMA Tool Disturbance 63
4.3.3. Model Gain Mismatch 66
4.3.4. Relative Confidence in Estimated States 68
4.4. Industrial Data Analysis 73
4.5. Conclusions 78
Chapter 5. Conclusion 80
Bibliography 81
Appendix 86
Publication list 88

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