本論文提出一個適用於全自動虛擬量測系統的自動化資料品質評估架構，此架構包括了製程資料品質評估指標及量測資料品質評估指標。製程資料品質評估指標為將收集到的製程資料，利用主成份分析擷取出資料特徵，再以歐氏距離整合成一綜合指標。其次，量測資料品質評估指標為採用自適應共振理論2與標準變異數方法計算而得。同時，必須自動訂定製程及量測資料品質評估指標的門檻值，若資料品質評估指標值大於其門檻值，代表資料為異常。應用此架構於半導體與TFT-LCD廠的全自動虛擬量測系統上，能夠線上即時地自動評估製程資料與量測資料的好壞，避免異常資料被重新訓練或調校至虛擬量測模型內，以便保持虛擬量測的準確性。在量測資料品質評估方法中，以自適應共振理論2為基礎，依據製程資料的相似性，分出不同的群組，而後計算對應的量測資料的品質統計量。
然而，實務應用上，傳統適應共振理論2仍有無法精確地分類製程資料之情況發生，使得群組內的製程資料並不相似，但卻被分在同一群裡；致造成該群對應的量測資料會有二群(或以上)的現象，如此可能會造成量測資料品質評估指標偵測能力失效。有鑑上述的問題，本論文亦提出進階適應共振理論2，以便藉強化相似度比對和自動尋優相關參數等功能，來改善原量測資料品質評估指標的偵測能力。
本論文以實際在TFT-LCD廠運作的CVD、黃光、與彩色濾光片生產機台為例，來驗證本自動化資料品質評估架構的效益。由驗證結果綜合本論文提出的資料品質評估架構確實有效且可行。

This dissertation proposes the schemes of automatic process and metrology data-quality evaluations for the automatic virtual metrology (AVM) system. Firstly, principal component analysis (PCA) is applied to extract data features of all the collected equipment process data; then Euclidean distance (ED) is utilized to unify all the principal components into a single index denoted process data quality index (DQIX) for evaluating the quality of process data. Secondly, adaptive resonance theory 2 (ART2) and normalized variability (NV) are applied to define the metrology data quality index (DQIy) for appraising the quality of metrology data. The thresholds of both DQIX and DQIy are also defined and can be adaptively calculated. The DQIX and DQIy data quality evaluation schemes are well-suited for the AVM systems of the semiconductor and TFT-LCD industries to on-line, real-time, and automatically evaluate the quality of all the collected process and metrology data. As such, abnormal data will not be adopted for VM model training or tuning and VM conjecture accuracy can be maintained.
The DQIy algorithm is based on ART2. ART2 divides data into different patterns according to the similarity of process data, and then calculates the corresponding DQIy value and its threshold, DQIyT, for evaluation and judgment. However, in practical applications, the classical ART2 technique still could not cluster process data very precisely. Since some samples with dissimilar process parameters might be sorted into the same cluster, two or more groups could be found in the corresponding metrology-data cluster. This phenomenon may cause invalid DQIy detection. To solve the problem above, the advanced ART2 scheme is also proposed in this dissertation to enhance the accuracy of the DQIy algorithm.
The actual CVD, photo, and color-filter production tools of a TFT-LCD factory were adopted as illustrative examples to verify the practicality of the proposed schemes. Experimental results show that the performance of the schemes is indeed effective and feasible.

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