臺灣博碩士論文加值系統

半導體製造產業通常是世界各國投以重大關注的對象，也是最主要的產業經濟指標。根據摩爾定律的推論，晶圓尺寸愈大，將愈有助於降低積體電路的製造成本，所以在晶圓的尺寸大小將從300mm 增加至450mm。在晶圓大小、重量與吞吐量增加的狀況下，人體已無法負荷多次數的搬運，所以半導體製造必需依靠自動化搬運系統。在顧客至上的時代裡，對於企業要保持競爭力必須要滿足客戶需求減少產品達交時間等，要對Hot lots提供產能馬上服務是一項很大的挑戰。本研究提出一種Conveyor派工法則(A conveyor dispatching rules for product priority ,CDP)來降低Hot lots 的平均變動時間。本研究以模擬軟體構建一個虛擬晶圓廠進行實驗與研究，並以Nearest Job First (NJF)做為比較的對象。根據實驗數據證明，Hot lots的總平均變動時間大約將低51%(從439.271秒降低為217.334秒)，結果說明了CDP派工法則是很有效率的，在每一種的負載率條件下都能有效減少Hot lots的運輸時間進而提高生產力。

The semiconductor manufacturing is one of the most important industries nowadays. According to Moore's Law, the bigger the size of wafer is, the lower manufacturing cost may cost. ; therefore the wafer size will increase from 300mm to 450mm. While the size, weight, and throughput of wafer increase, workers cannot afford the great amount of wafer handling; therefore semiconductor industries usually rely on automatic handling systems. In order to satisfy customers’ demands, it is important to provide on-time delivery service and reduce the cycle time. It is a big challenge to provide no-wait transport service for high priority product. Therefore, this study proposes a conveyor dispatching rule(A conveyor dispatching rules for product priority ,CDP) to reduce the handling delay of Hot lots. A simulation of wafer foundry is developed to conduct the research, comparing NJF rule with CDP. The result indicates that the total average delivery variable time of hot lots can be reduced by 51% under CDP rule. This study proves that CDP dispatching rule can efficiently reduce the cycle time of Hot lots in every load configuration and improve productivity.

中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
圖目錄 v
表目錄 vi
一、 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 5
1.3 研究範圍與假設 6
1.4 研究流程 7
1.5 論文架構 8
二.文獻回顧 9
2.1 自動物料搬運系統佈置型態之相關文獻 9
2.2 自動物料搬運系統佈置分析之相關文獻 10
2.3 派工法則之相關文獻 12
2.4 Conveyor之相關文獻 14
三、研究方法 17
3.1 CDP派工法則 17
3.2 搜尋演算法 23
3.2.1 循序搜尋演算法 23
3.2.2 循序搜尋演算法程式碼撰寫 25
四、模擬模型與結果數據分析 27
4.1 模擬實驗模型與設計與建立 27
4.1.1 系統模擬工具 29
4.1.2 系統模擬環境 30
4.1.3 模型實驗方式 32
4.1.4 控制變數 33
4.1.5 績效衡量指標 34
4.1.6 模擬模型程式設計 36
4.1.7 物件模組設定型態 37
4.2數據結果分析 40
4.2.1 實驗結果 41
4.3總結 46
五、結論 48

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