在半導體產業中，線上製程通常包含了300個以上的製造步驟與檢測站，使得整體的生產流程時間相當長，線上各檢測站抽檢時更包含了重工、報廢等回流問題使得整體流程之抽樣策略決策比起一般製造業更具挑戰性。當提升各站抽樣頻率時，雖然可以降低不良品加工浪費所造成的成本，但相對增加了生產週期時間使產出降低進而導致成本上升，如何權衡抽樣風險與生產週期時間達最佳抽樣策略為本研究探討的主題。在本研究中，我們考慮了線上各製程與各檢測站作業時間之隨機性，發展出一個數學模型來描述此問題。基於此數學模型，本研究建立模擬模型並透過隨機最佳化演算法與多重起始解求解進行比較，透過權衡因抽樣策略所造成之不良品後續加工成本及生產週期時間增加所造成之成本，達到最小化總成本。數值實驗與實證研究充分的證明了本研究所提出之最佳化半導體線上抽樣策略於實務上的可行性。

In semiconductor manufacturing, the process is one of the most complicated manufacturing processes in the world. The process usually consists of more than 300 manufacturing steps that have complex interaction with each other. Frequent sampling can lead to redundant tests and increased cost, while infrequent sampling makes the quality of final products doubtable. There is a nontrivial tradeoff. How to decide on the optimal sampling policy is a critical, but challenging problem, in semiconductor manufacturing. In this thesis, we study the sampling problem in semiconductor industry and develop a mathematical model to characterize it. We use the simulation optimization technique to solve this model, and compare with multiple start schemes. We also derive the optimal sampling policy that can achieve minimum cost. A numerical experiment and an empirical study are conducted to verify the viability of the proposed model.

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