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研究生:施明德
研究生(外文):Mingdr Shr
論文名稱:半導體製造之智慧型排程系統
論文名稱(外文):An Intelligent Scheduling System for Semiconductor Manufacturing
指導教授:劉立頌
指導教授(外文):Alan Liu
學位類別:博士
校院名稱:國立中正大學
系所名稱:電機工程所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
畢業學年度:94
語文別:英文
論文頁數:119
中文關鍵詞:半導體製造智慧型系統排程
外文關鍵詞:Resource Schedule and Execution MatrixDedicated Machine ConstraintSemiconductor Manufacturing
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論文的研究發展目標是開發半導體之智慧型排程系統以解決半導體工廠生產排程所遇到的新問題—專屬微影黃光機台限制,及其所衍生的工廠中微影黃光機台的負載不平衡的不正常現象。過去的研究曾使用排隊網路模型(Queuing Network Model)以及人工智慧(Artificial Intelligence)的技術或其他相關理論從事半導體排程的研究,然而如今因為產品需要或基於品質的考量,愈來愈多的限制加入於現今的半導體生產工廠,加上近來產品的少量多樣化競爭,使得半導體管理面臨愈來愈困難的生產效率與準時交貨的挑戰。先前的半導體工廠生產管理系統面對無法預期的事件意外和時時變動的外在環境,僅是採取手動調整排程結果或離線修改排程程式的方式,如此的方法當然無法滿足半導體工廠的生產作業的需求,並且需要依靠大量的經驗知識與專家人力。
經過研究分析,並使用近來方便的知識表示及推理工具,我們整理發現要解決目前半導體工廠生產管理所面對的問題,有幾項重要的特徵,我們必須掌握及處理,所以在論文中我們提出多維陣列資料表示架構(Multi-dimensional Matrix Representation Framework)來輔助開發半導體智慧型排程系統。利用多維陣列資料表示架構,可以容易並清楚描述系統的重要特徵、要素及它們之間的重要關係。而透過一些簡單的行列運算,我們可以經過預設的條件規則,整理出重要訊息以供排程系統中決策時使用。
論文中我們描述了我們所開發的兩個智慧型排程系統—平衡負載排程系統(Load Balancing Scheduling System)及多重智慧型代理人排程系統(Multiagent Scheduling System)來解決半導體工廠之專屬微影黃光機台限制,及其所衍生的工廠中微影黃光機台的負載不平衡的不正常問題。資源排程及執行陣列(Resource Schedule and Execution Matrix)是論文所提之多維陣列資料表示架構的一個實作,我們將此資源排程及執行陣列應用於我們所開發的兩個智慧型排程系統。實驗經過模擬的結果,我們所提之兩個智慧型排程系統的表現優於傳統的排程系統。
The development of scheduling strategies for the new dedicated photolithography machine constraint of the semiconductor manufacturing systems is the main objective of this research. The scheduling of the complicated semiconductor manufacturing systems has been studied for more than a decade and there is still much effort we need to be involved. Previous research work has applied the Queuing Network system models or many Artificial Intelligence techniques to develop and practice the scheduling system in this topic. However, the more constraints for quality issues or new technologies have been set on the semiconductor manufacturing systems as well as the more competitive Integrated Circuit manufacture business has derived the urgent demands of efficiency production and the accuracy ability of make-to-order production.
The previous semiconductor manufacturing scheduling systems have come to the situation that they need to adjust the scheduling algorithm often to fit the dynamic changing environment or even need to modify the scheduling results manually. The worst, we can not have a systematic method to acquire the experience or knowledge of the special situation. Moreover, we still have not the efficiency techniques for the translation from problem model to computation framework today. The effort also depends on the specific domain knowledge and human expert.
By investigating current powerful modeling techniques including Queue Network systems and Constraint Satisfaction Problem (CSP) models, we capture the most important features of the semiconductor manufacturing systems. The research result inspires us to propose a naive data structure and manipulation method—the Multi-dimensional Matrix Representation (MMR) framework to help the design and implementation of the intelligent scheduling systems for this new dedicated photolithography constraint in the semiconductor manufacturing systems.
The proposed MMR framework is to work with the complex scheduling problems in the semiconductor manufacturing systems. The modern manufacturing systems are usually facing the unpredictable dynamics and changing environment which is unknown at design time but also at run time. Sometimes, these systems need to make decisions under uncertain or incomplete information. The MMR framework can provide the approximate current situation of the manufacturing system by some factors such as the load, utilization, and the level of queue buffer of one machine as well as the progress status of the wafers. Applying the predefined heuristic rules to these factors, we accomplish the scheduling task by adapting to the changing environment.
We realize that a suitable knowledge representation method for software systems can not only guide us to better understand the specification of the software systems, but also our human’s point of view to the problem and solution. Knowledge representation is to represent the natural world we are live in, the activities of the real world, and what we are thinking of the world. Developing software systems to tackle complex problems and to operate in the dynamics and changing environment will need much human’s mind effort. We practice the proposed MMR framework by using the Resource Schedule and Execute Matrix (RSEM), a two-dimension matrix as a simple knowledge representation method to deal with the dedicated photolithography machine constraint in semiconductor manufacturing. The RSEM can represent this new important constraint as well as easily setup with different factors and scheduling rules for the constraint.
In this dissertation, we present the detail architecture and manipulation method of the proposed MMR framework and describe the constructing procedure of the RSEM. The RSEM is the practice of the proposed MMR framework to tackle the dedicated photolithography machine machines. We also briefly illustrate an example of the semiconductor manufacturing system to demonstrate the proposed MMR framework and present the Load Balancing scheduling and Multiagent Scheduling System which are two developed intelligent scheduling system. The simulations set with difference wafer lots and photolithography machines have shown the case study we have investigated in a real semiconductor manufacturing system.
CHAPTER 1 INTRODUCTION 1
1.1 MOTIVATION 1
1.1.1 Semiconductor Manufacturing System 2
1.1.2 Dedicated Photolithography Machine Constraint 4
1.1.3 Load Balancing among Photolithography Machines 5
1.2 MULTI-DIMENSIONAL MATRIX REPRESENTATION (MMR) FRAMEWORK 6
1.2.1 Modeling Example 8
1.2.2 Resource Schedule and Execute Matrix (RSEM) 12
1.3 DISSERTATION ORGANIZATION 13
CHAPTER 2 RELATED WORK 14
2.1 SEMICONDUCTOR MANUFACTURING SCHEDULING SYSTEMS 14
2.1.1 Queuing Network Model for Scheduling 14
2.1.2 Artificial Intelligence Scheduling 15
2.1.3 Other Scheduling Application and Approach 17
2.2 SCHEDULING SYSTEM INVESTIGATION 24
2.2.1 Scheduling for Uncertainty 24
2.2.2 System Decomposed Approach: MMR vs. CSP 25
2.2.3 Discussion 26
CHAPTER 3 SEMICONDUCTOR MANUFACTURING SCHEDULING SYSTEMS 28
3.1 INTRODUCTION TO SCHEDULING 28
3.2 SCHEDULING OF SEMICONDUCTOR MANUFACTURING 31
3.2.1 Sequencing Rules 32
3.2.2 Special Sequencing Rules 33
3.2.3 Work Release Policies 33
3.3 DIFFICULTIES OF SCHEDULING 35
3.4 LEAST SLACK (LS) SCHEDULING POLICIES 37
3.4.1 Fluctuation Smoothing for Variance of Lateness (FSVL) 39
3.4.2 Fluctuation Smoothing Policy for Variance of Cycle-Time (FSVCT) 40
3.4.3 Fluctuation Smoothing Policy for Mean Cycle-Time (FSMCT) 40
3.5 CONSTRAINT SATISFACTION PROGRAM MODELING 42
3.5.1 Distributed Multi-Event Scheduling (DiMES) 44
3.5.2 Dedicated Machine Constraint Modeling 46
3.6 SUMMARY 50
3.7 KNOWLEDGE ACQUISITION FOR INTELLIGENT SCHEDULING SYSTEM 56
3.7.1 Load Factor of Photolithography Machines 56
3.7.2 Definition of Load Factor 57
3.7.3 Implementation of Load Factor 59
CHAPTER 4 MULTI-DIMENSIONAL MATRIX REPRESENTATION FRAMEWORK 60
4.1 BASIC IDEA OF MMR FRAMEWORK 60
4.1.1 Story of Scheduling 60
4.1.2 New Story of Scheduling 60
4.1.3 MMR framework for New Challenges 61
4.2 ADVANTAGE OF MMR FRAMEWORK 62
4.2.1 Analysis Tool 62
4.2.2 Dynamic Environment Representation 62
4.3 MMR FRAMEWORK 63
4.4 FORMAL DEFINITION OF THE MMR FRAMEWORK 64
4.4.1 Multi-dimension Matrix Λ 64
4.4.2 Set of Factors Generated from Multi-dimension Matrix Λ 65
4.4.3 Set of Predefined Rules 66
4.5 RESOURCE SCHEDULE AND EXECUTION MATRIX 66
4.5.1 Tasks of Constructing RSEM 66
4.5.2 Procedure of RSEM 68
4.5.3 Algorithms Associated to RSEM 69
4.6 DISCUSSION 72
CHAPTER 5 INTELLIGENT SCHEDULING SYSTEMS 74
5.1 LOAD BALANCING SCHEDULING APPROACH 74
5.1.1 Task Generation 74
5.1.2 Resource Calculation 75
5.1.3 Resource Allocation 77
5.2 MULTIAGENT SCHEDULING SYSTEM APPROACH 78
5.2.1 Background 78
5.2.2 Agent 79
5.2.3 Prototype System 82
CHAPTER 6 SIMULATIONS 86
6.1 LOAD BALANCING (LB) VS. LEAST SLACK (LS) 86
6.1.1 Different Photolithography Machines 89
6.1.2 Different Photolithography Layers 90
6.2 MULTIAGENT SCHEDULING SYSTEM (MSS) VS. LEAST SLACK (LS) 91
6.3 DIFFERENT PHOTOLITHOGRAPHY PROCESS TIME FOR DIFFERENT PHOTOLITHOGRAPHY STAGES 94
6.4 THRASHING PHENOMENON 97
CHAPTER 7 CONCLUSION AND FUTURE WORK 99
REFERENCE 101
APPENDICES 108
A. QUEUING NETWORK MODEL: REENTRANT LINES 108
B. SIMULATION PROGRAM CODES 112
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