半導體生產製造為典型的批次製程，由於此類製程所生產的產品通常需要極長的製造時間，因此，其最終品質特徵值無法即時取得，故無法使用傳統的線上管制系統。此時如何利用量測機台的製程剖面資料，來執行製程的錯誤偵測與分類 (fault detection and classification, FDC)，是目前製程管制的重要研究課題。 Lee, et al. (2011) 提供一個簡單且有效的統計模型來分析製程剖面資料，將製程剖面資料拆解成機台設定目標值、位移量度 (level-shift parameter) 及殘差，其中以位移量度表示批次之間平移或飄移量，且以殘差值來建構健康指標 (health index) 監控製程是否穩定或有異常現象發生。然而此方法未考慮到製程資料點之間存在的自我相關性，同時位移量度亦無法有效地描述製程中的開關事件 (on-off action effect)，此時若以傳統的管制圖進行監控，極易產生錯誤警訊 (false alarm)，為克服此困難，本文使用季節性時間序列模型與干預模式來分析製程剖面資料，它不但可以消除資料點之間的自我相關性，並且透過干預模式可以有效地描述製程中晶圓切換的 on-off action effect。最後，本文亦提出三種不同類型的健康指標來探討並監控晶圓之間、批次之間及機台穩定度的變化，它可有效地改善Lee, et al. (2011) 以殘差作為偵測製程是否異常的單一考量因素，進而能有效地提升製程的監控能力。

In semiconductor manufacturing, due to the reason that the manufactured products usually require very long process time, the final output of the quality characteristics cannot be collected immediately. Hence, the traditional online statistical process control (SPC) techniques cannot be implemented directly. Under this situation, how to use the profile data of manufacturing equipment to perform fault detection and classification (FDC), is an important research topic. Recently Lee, et al. (2011) introduced a simple FDC procedure to analyze the profile data, which consists of the target value, level-shift parameter and residuals. The level-shift parameters indicate the amount of drift between batches. A health indicator (health index) is also constructed to address the healthy condition of the manufacturing of equipment. However, this method does not take the autocorrelation of data points into consideration. In addition, the level-shift parameter is not capable of describing the process on-off effect effectively. To overcome those difficulties, this thesis proposed a seasonal time series model together with intervention analysis to analyze the profile data. The proposed method can eliminate the autocorrelation between the data points. Furthermore, this article also constructs three different types of health index to address the wafer-to-wafer, batch-to-batch variations and the residuals performance. It provides engineers a useful way to identify the potential faulty of the manufacturing equipment.

摘要 i
Abstract ii
第一章 緒論 1
1.1 前言 1
1.2 批次製程資料與同步化 2
1.3 研究動機與目的 3
1.4 研究架構 4
第二章 文獻探討與問題描述 5
2.1 製程資料介紹 5
2.2 剖面資料模型與健康指標 8
2.3 問題描述 10
第三章 批次剖面資料統計分析與錯誤偵測 14
3.1 時間序列與干預模式 14
3.1.1 季節性時間序列 14
3.1.2 干預模式與建模 18
3.1.3 實例分析與比較 20
3.2 健康指標建構與分析 27
3.2.1 批次間變異之健康指標 27
3.2.2 開關動作之健康指標 28
3.2.3 機台穩定度之健康指標 28
3.2.4 實例分析比較 32
第四章 結論與後續研究 33
參考文獻 35

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