半導體製造為我國優勢產業中佔有重要的一環。在半導體製程中，以黃光區製程最為繁雜重要，佔了將近整個製程40%~50%的時間，為半導體製程之瓶頸，必須將製程中光罩、機台與晶圓做妥善分配。希望藉由有效的製程規劃，達到減少晶圓Cycle Time(C/T)之目標。
現有黃光區製程技術已經將光阻塗佈、曝光及顯影三者串聯於同一生產平面，而如何再精進減少C/T，除了增加平行機台增加製程速度外，即是用運用晶圓轉機的方法，使每台機台在其的機台負荷能力下，達到最佳的負載量。也就是從黃光區產線平衡規劃的角度檢視黃光區製程:運用晶圓轉機追求產線平衡，進而改善黃光區的人工派工模式。
本研究蒐集科學園區某半導體製造廠的資料來建立晶圓轉機模型，運用柔性運算來提供較佳的轉機派工規劃，經由驗證可知柔性運算能顯著改善人工派工之缺點。其中，經驗證在單目標與雙目標的模型中，又分別以SSO及MM-SSO有較佳的改善幅度。

Semiconductor Process plays an important role in competitive industries in Taiwan. Photolithography is the most complicated and key part in IC process flow, taking up about 40%~50% above all process. It is necessary to allocate mask, machine and wafer in the process flow appropriately. In order to reach efficiency and increase rate of equipment utilization, reducing cycle time(C/T) of wafer on each machine during photolithography is expected.
It's capable of install coater, exposure and developer in series on the same machine to shorten C/T. However, there are still many methods to keep cutting down C/T .Except for adding parallel machine, running the existing parallel machine efficiently is also required. In other words, it makes every machine reach the best in the loading and reduce C/T on the restrictions of mask by transferring goods. It does works, and wafers are produced on time as expected.
This research collected information of an IC manufactory in Science Park to set up a model of photolithography considering all the constraints on process. We found out a better wafer transfer dispatching solution through SSO algorithms and MM-SSO, and establishing a complete arithmetic logic applied to photolithography dispatching system. After testing and debugging, the logical operation can be applied to the follow-up study and demonstration in pratice. It's able to downsize and save time about 70% by algorithms automatic dispatching.

摘要 I
Abstract II
致謝 III
目錄 IV
表格目錄 VII
圖目錄 VIII
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 論文架構與研究流程 4
第二章 文獻探討 6
2.1 半導體黃光區製程 6
2.2 平行機台生產排程 9
2.3 柔性運算 11
2.3.1 遺傳演算法 11
2.3.2 簡化群體演算法 13
2.4 多目標規劃 14
2.4.1 柏拉圖最適解 14
2.4.2 大中取小法 15
2.4.3 非被支配解排序基因演算法 17
2.5 文獻回顧小結 18
第三章 問題研究 20
3.1 問題架構 20
3.2 求解流程 22
3.2.1 問題假設 22
3.2.2 符號說明 23
3.2.3 資料分類 23
3.2.4 演算法模型建立 25
第四章 研究方法 26
4.1 西北角法 26
4.2 遺傳演算法 27
4.3 簡化群體演算法 30
4.4 非被支配解排序基因演算法 32
4.5 派工結果的連結 34
第五章 實例驗證 35
5.1 單目標小規模問題 35
5.1.1 五台機台對三種光罩 35
5.1.2 四台機台對五種光罩 48
5.1.3 五台機台對三種光罩(機台3當機) 52
5.2 單目標大規模問題 55
5.2.1 二十台機台對十種光罩(h=12) 55
5.2.2 二十台機台對十種光罩(h=3) 58
5.2.3 二十台機台對十種光罩(兩版光罩h1=3,h2=2) 60
5.3 雙目標問題 62
5.3.1 五台機台對三種光罩(考慮雙目標) 62
5.3.2 二十台機台對十種光罩(考慮雙目標，h=12) 66
5.3.3 二十台機台對十種光罩(考慮雙目標，h=3) 70
5.3.4 二十台機台對十種光罩(考慮雙目標，h1=3,h2=2) 74
5.3.5 雙目標問題小結 78
第六章 結論 79
6.1 研究結論 79
6.2 未來研究建議 81
參考文獻 82

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