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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/48123

    Title: 液晶面板MURA現象之研究;The Rearch of Liquid Crystal Panel MURA
    Authors: 楊欽富;Ching-fu Yang
    Contributors: 光電科學研究所碩士在職專班
    Keywords: 液晶;面板;缺陷;色彩;干涉條紋;類神經;Liquid crystal;panel;TFT LCD;defect;color;neural network;inteference fringe
    Date: 2011-07-20
    Issue Date: 2012-01-05 14:34:33 (UTC+8)
    Abstract: 近幾年來,薄膜電晶體液晶顯示器(Thin Film Transistor Liquid Crystal Display)的市場需求越來越高。該產品也被大量的生產,生產者亦嚴格的要求其品質以及仔細的檢測各種缺陷。在製造業當中,精準的篩選出缺陷,可說是基本要求;傳統上,工廠僱用作業員,利用人類視覺檢測方法來篩檢TFT液晶顯示器的缺陷,但因參雜了人的主觀想法,準確度較難以掌握,並且會消耗大量的人力資源。在液晶面板產業中,為了節省成本,必須開發出自動缺陷檢測的方法。 在此我們研究自動檢驗缺陷的方法。利用影像攝影機觀察面板上的干涉條紋,抓取干涉條紋成為影像檔案,並使用圖像處理,以提高干涉條紋的對比度,如此,識別速度以及準確度才能提升;接著利用類神經網路方法,反覆學習面板條紋走向與缺陷的關係,並確定缺陷分類。由於缺陷的種類複雜,本文將重點放在MURA的缺陷檢測和分類。在剛開始作類神經網路學習時候,大約有3%的誤判發生;若是繼續用類神經網路反覆訓練這些樣本後,去測試其他影像,將可以達到零誤判的水平(100%正確)。該類神經網路缺陷辨識系統得到零誤判水準後,我們發現檢查單一面板的時間可以小於 1秒。將異常面板挑出後,產能可以移轉給正常面板,避免虛工,還可節省液晶,即時發現異常,回饋可能的原因給製程工程師,作為提升良率的參考依據。因此本方法可以用來預先檢驗出面板可能的MURA缺陷。 In recent years the thin-film transistor liquid crystal display (TFT LCD) has a high demand in the market, the high necessity of the product quality control and the requirements of the various defect detections are more stringent. The high defect detection rate is the basic requirement of the quality control process. The use of the conventional human visual inspection methods to find the defects of the TFT LCD is not accurate, and will consume a large amount of resources. The automatic defect inspection method is necessary to this industry. In addition to find the defects, the types of the defects should be recognized as well. Here, we propose a method based on the optical interference patterns sensing method to find the interference fringes, and use the image processing to enhance the contrast of the interference fringes, and the recognition rate for the later process could be increased. The neural network method is used to learn the defects and to identify the types of the defects. The paper is to focus on the mura defect inspection and the classification. In the beginning, before the learning process, about 3% of misjudges were happened, if the threshold of the certainty factor is set to be 90%. After neural network retrain these samples, the result of the rest of the images, got a non misjudge level (100% correct). The neural network system gets a non-misjudge level after retraining. The elapse time for the inspection of one panel is less than 1 sec. The inspection procedure is processed before the injection of the liquid crystal (LC) into the lattice panel. The defect panels could be sorted out, so that the later process and the waste of the materials could be avoided, it is called the pretest process of the TFT LCD product.
    Appears in Collections:[光電科學研究所碩士在職專班 ] 博碩士論文

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