半導體晶圓製造是一個複雜且高製程能力標準的產業，如何提升機台使用率已達到客戶交期是一個重要的議題。故在本研究中，將提出一個整數規劃模型與啟發式演算法解決半導體製造業黃光區的負荷平衡的問題。考慮機台能力、機台綁機與光照限制，本研究模型之目標式為最小化機台間負荷差異。機台能力代表每個產品必須在某些特定規格的機台下加工，機台綁機限制則是要確保晶圓批的品質，如果第一層關鍵層在某特定機台加工，則之後回流的每層關鍵層皆須在此機台加工。本研究進行兩方法的比較、最佳參數的實驗設計、經實驗驗證本研究方法之成效，顯示基因演算法可在合理之求解時間內獲得近似最佳解。最後，針對機台能力彈性與光罩數量彈性分析，找出與適合的彈性百分比可供未來研究與半導體業參考。

關鍵字: 晶圓廠、機台綁機限制、黃光區、基因演算法、整數規劃

Wafer fabrication is a complicate and high process capability manufacturing, how to utilize the machine capacity efficiency in order to meet the customer deadline is a very important issue.in this paper, we proposed a integer programming model and heuristic algorithm approach to solve the loading balance problem for photolithography area in semiconductor manufacturing industry. Taking process capability, machine dedication and reticle constraint into account, we want to minimize the difference of each machine’ loading. Process capability is means that each product needs to be processed on the machines that meet the process specification. Machine dedication means that if the first critical layer of this wafer assigned to certain machine, than the following critical layers of this wafer need to be processed on this certain machine in order to ensure the wafer quality. This research compared the result of two methods and found the best parameter setting of genetic algorithm, the computational performance of genetic algorithm shows that we can find the near optimal solution in the reasonable time. Finally, this research analyzed the machine capability and reticle flexibility that found the best percentage which can be a reference for the future research and real semiconductor industry.
Key words: Wafer fabrication, Machine dedication, Photolithography area, Genetic algorithm、Integer programming

摘要 I
ABSTRACT II
致謝 III
CONTENTS IV
LIST OF TABLES V
LIST OF FIGURES VI
Chapter 1 INTRODUCTION 1
1.1 Background 1
1.2 Research motivation 3
1.3 Research objective and scope 4
1.4 Structure of thesis 5
Chapter 2 LITERATURE REVIEW 6
2.1 Characteristics of photolithography area 6
2.2 Dispatching and scheduling rule of photolithography process 12
2.3 Genetic Algorithm 17
2.4 Summary of literature review 25
Chapter 3 PROBLEM RESEARCH AND STRATEGY 26
3.1 Problem Definition and analysis 26
3.2 Problem formulation 29
3.3 A demonstrative example 33
Chapter 4 SOLUTION METHOD 36
4.1 Traditional genetic algorithm 36
4.2 Algorithm framework 37
4.3 Algorithm steps analysis 38
Chapter 5 COMPUTATIONAL DISCUSSION 44
5.1 Lingo scenario analysis 44
5.2 Large scale problem description 45
5.3 Optimal parameter setting 47
5.4 Machine capability flexibility analysis 50
5.5 Reticle available quantity flexibility analysis 51
Chapter 6 CONCLUSIONS AND SUGGESTIONS 57
6.1 Conclusions 57
6.2 Suggestions for the future research 58
REFERENCE 59

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