晶圓廠為維護產品品質與掌握機台健康程度，在線上生產階段會利用缺陷掃描的檢測設備對線上生產的貨批進行抽樣檢測，以及早發現不良品，並作及時的處理，並透過缺陷分析監控機台狀況。缺陷檢測雖然能夠用以及早發現機台的異常情況並減少發生錯誤所造成的品質損失，但檢測需要耗費成本、過多的檢測量也會提高晶圓生產週期時間，抽樣策略的制定將影響整座晶圓廠的生產力。本研究目的為建立半導體製造設備健康監控之動態抽樣決策架構，包含透過貝氏決策分析與數學規劃模式以決定最佳機台抽樣週期時間之配置，並發展線上檢驗貨批資訊價值層級架構以評選貨批檢測資訊價值，提昇貨批掃描之成本效益。為檢驗提出方法的效度，本研究以台灣新竹科學園區的某半導體製造廠商作為實證對象，討論在不同水準檢測貨批數限制之下，最小化成本損失的變化趨勢，供個案公司在動態調整檢測量水準時參考，同時在選取模式中，利用個案公司歷史資料進行離線實驗以評估效益，並實際導入線上使用。實證結果已降低個案公司每日檢測貨批數、減輕檢測站負荷，達到減少檢測站作業人員數之效益。

To avoid potential quality loss, inline defect inspection is used to monitor equipment health via sampling a processing lot every fixed period in semiconductor manufacturing. However, inspection needs cost and prolongs cycle time. Optimizing inspection sampling strategy is critical to enhance fab productivity and maintain competitive advantage of semiconductor companies. This study aims to construct a dynamic defect sampling decision framework for equipment health monitoring in semiconductor manufacturing. In particular, we focus on two sub-problems to enhance the effectiveness and efficiency of defect sampling. First, this study optimizes the sampling period allocation for each equipment using Bayesian decision analysis and mathematical programming model. Second, this study develops a scan lot evaluation hierarchy from the information value perspective to enhance cost-effectiveness. The empirical study was conducted in a leading semiconductor company in Taiwan. This study discussed the total expected quality loss in different scenarios and provided risk evaluation of scan lot reduction. In addition, this study offline simulated the selection mechanism based on historical data to evaluate performance, and implemented inline process. The result showed that the practical value for scan lot reduction and thus reduced workforce loading.

簡禎富（2005），決策分析與管理，雙葉書廊，台北。
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