在半導體製造過程中，都會蒐集晶圓通過機台而自動化產生的參數資料或是以人工方式做紀錄的判斷資料來進行產品的監控或是故障分析，然而有時候工程師卻無法快速且有效從大量資料中找出事故發生原因。故本研究係建構半導體製程資料挖礦架構，針對製程資料進行分析，達到故障排除與良率提升的目的，分為問題定義與架構、資料準備、建立挖礦模式、到最後結果解釋與評估等四個階段，並具體說明每階段之內容。實證研究中對於兩種不同類型的製程資料，發展具體的資料挖礦架構，在實證一中，針對晶圓圖資料，利用類神經網路和空間統計檢定的方法，建構晶圓圖分類流程，藉由分群後的共同故障特徵圖形，幫助工程師快速追查問題發生的原因；在實證二中，針對製程及其相關機台時間資料，結合K-W統計檢定和決策樹分析方法，發展半導體WAT測試參數之事故診斷資料挖礦方法，快速找出發生問題的機台與時間，提供工程師解決問題的參考依據，最後兩個結果證實可幫助工程師縮短事故問題的時間與範圍。

During the process of wafers in semiconductor manufacturing, many kinds of data will be collected automatically or manually to monitor the product. It is important to fix the problem of the manufacture process as soon as possible. However, the engineers sometimes can not find out the root-cause from the mass manufacturing data timely and rapidly. This research focused on constructing a conceptual framework for data mining of the semiconductor manufacturing. In this framework, there are four phases: problem definition and problem structuring, data preparation, modeling, and evaluation/ interpretation. Two empirical studies with different type of manufacturing data were applied to the framework. In the first empirical study, we focused on the wafer bin map data and constructed the clustering process by the neural network and the spatial statistic test. It can help engineers to find out the root-cause of problems through the cluster groups of common fail bin patterns. In the second empirical study, we developed the data mining methods of diagnosing defects of the WAT parameters and the related process information. In the use of decision tree and K-W test, we can extract the valuable information or knowledge for the domain engineers to identify the trouble process step. The final results of study showed the domain engineers could improve the effectiveness of trouble shooting.

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