半導體製造業包括複雜而繁瑣的製程，同時製程中存在大量而彼此非獨立的量測參數，因此，製程管制與監控被應用於半導體廠以確保良率及在大量資本投資下的獲利狀況。
本研究發展一半導體製造之異常偵測與分類架構，藉由建立統計與資料挖礦模式於異常偵測與分類之系統中，監控半導體製程之異常事件，並對潛藏的異常事件進行分類。經由此架構，複雜而彼此相關的變數關係可以被化簡，同時簡化後的模型，可以用來偵測，群聚並分類異常事件；最後排除造成異常事件的原因，進行改善，讓製程回到正常狀況。透過多變量統計及資料挖礦方法由歷史製程資料萃取與挖掘出的資訊及知識，可以成為異常事件診斷的重要線索。
同時，透過一半導體廠的實證研究進行驗證與說明，結果顯示此架構的實用性與有效性；此研究發展的模式可以被建構於實際的製程或機台監控之異常偵測與分類系統。藉由持續不斷的製程監控與萃取自歷史資料的資訊，可以指認出關鍵的製程變數或量測項目，並分類與診斷異常事件，減少異常事件之發生頻率，降低發生製程偏移之次數。另一方面，經由掌握系統中的數個關鍵變數將使得製程監控的行為變得更容易

Semiconductor fabrication involves highly complex and lengthy processes in which a large number of variables are interrelated. Process control and monitoring are necessary in a semiconductor fab to ensure the yield and thus the profitability of huge investments.
This study aims to develop a framework for Fault Detection and Classification (FDC) and statistical models to be embedded in the FDC system to monitor the semiconductor fabrication process in which multi-faults may exist. The objective of the proposed framework is to structure the process operation from a large number of correlated variables, to detect faults, diagnose them by clustering and classifying the abnormal events, eliminate the cause of the faults, and then improve the performance of the process. The information and knowledge are discovered and extracted by the multivariate statistical methods and data mining approaches form the historical process data, and can be an aid of fault diagnosis and recovery. Simple rules can be generated to classify and predict the wafers.
Also, an empirical study is conducted in an advanced 300mm DRAM fab for validation. The results showed the practical viability of this approach. The developed model can be embedded in a Fault Detection System for process or equipment monitoring. As a result of routine monitoring process and the extracted information from process data, critical variables of the process are identified, and the faults can be removed, process excursion is decreased. Moreover, the monitoring process are simplified by reducing the number of variables in the system, fewer key variables are monitored by the FDC instead of all the variables in the system.

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