光電產業在自動化的潮流下，使得製造程序日漸複雜和精密，製程技術也不斷的在精進，要提高產品品質並降低製造成本，良好的製程管制將是重要的課題。展望未來，TFT-LCD不論是在電視、電腦、監視器等市場將進行全面性的取代，在此龐大的產業規模下，獲利與否將是在競爭市場上生存的關鍵因素。

　　本研究使用倒傳遞類神經網路對TFT-LCD產業之濺鍍製程建構一多重輸入單一輸出之製程控制系統，但由於現場資料收集之方式不良，造成研究上的限制，經過了本文考慮製程為一動態系統而提出pattern觀念之資料前處理後，仍無法架構出良好之預測模式。工程製程管制之觀念無法落實，退而採用傳統之管制圖與多變量之馬氏距離及區別分析來診斷膜厚品質，最後分析顯示，考慮多變量之方法比傳統管制法更有效地診斷產品品質。雖無法建構出有良好品質診斷能力之工程製程管制系統，但本研究在資料處理及觀念上所提之方法仍是有相當的貢獻。

　With the trend of automation the manufacturing process of optical electrical industry is more complicated and the technology keeping progressing. In order to improve the product’s quality and to reduce the production’s cost, it is very important to have a control system. In the future, the TFT-LCD will replace the products including the TV, computer, monitor, etc. Under the huge scale of industry, it will be the key factor of surviving on the competitive market to make a profit.

　In this research, applying Artificial Neural Network for a sputter process constructs a multiple-input single-output process quality control system. Due to the way the raw data are collected in the sputter process is bad, causing many constraints in this study. This research proposes a preprocessed method with the concept of pattern for this dynamic system. After preprocessing, the prediction model wasn’t still created, so this research uses the Control Chart, Mahalanobis Distance and Fisher Discriminant Analysis separately to diagnose the thick quality. These application studies demonstrated that, in comparison to conventional control chart, the multivariate analysis is more accurate and efficient. Although the prediction model with good performance isn’t constructed, the method and concept of data process in this research have fairly good contributions.

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