用獨立成份分析法於薄膜電晶體液晶顯示面板之製程監控與表面瑕疵檢測

Independent Component Analysis approaches for process variation monitoring and Mura defect inspection in TFT-LCD manufacturing

曾彥馨(Yen-Hsin Tseng)

指導教授 ： 蔡篤銘

元智大學/工程學院/工業工程與管理學系/博士(2013年)

https://doi.org/10.6838/YZU.2013.00106

摘要

顯示器（Display）是資訊時代人們訊息傳遞與溝通之重要界面，平面顯示器（Flat-panel displays）具有小至輕薄可攜性與大至應用於公眾公佈欄領域之特性，更加帶給人們許多生活上之便利。本研究針對薄膜電晶體液晶顯示面板(TFT-LCD)提出以獨立成分分析為基(ICA-based)之方法，分別應用於一維時間序列訊號的製程監控與二維影像之Mura瑕疵檢測。透過對關鍵製程參數的監測能夠於製程中即時有效的提升良率與避免材料的浪費。本研究導入獨立成分分析於TFT面板之製程參數變異偵測，偵測對象選定為總斜度變異量，總斜度變異量是一個關鍵監控參數，目的為觀測對組工程中的變異量，該變異量會形成如對組位移造成顯示畫面不均勻的斑或漏光之現象，透過總斜度變異量的監控可以有效且快速回饋製程變異，避免大量不良品的產生。本研究透過ICA分別出製程參數資料之獨立成分，針對獨立成分資訊即可分辨出製程變異。相較於目前TFT-LCD的製程變異偵測採用之傳統的統計管制圖，可獲得良好之變異偵測結果。經實驗與真實資驗證本研究採用之ICA方法對TFT面板變異量監控具有良好效果。

針對Mura影像以機器視覺方法進行瑕疵檢測。Mura瑕疵在顯示器面板上呈現光源不均現象，並且與周圍背景具低對比度相似不易偵測特徵。本研究提出一個以獨立成分分析為基(ICA-based)之Mura瑕疵檢測方法，分為二階段：訓練階段透過無瑕疵樣本建立基影像與特徵向量，基影像間同時具有統計獨立與空間不重複之特性，其中目標式結合了統計獨立的最大負熵與空間不重複性的最小相關係數，無瑕疵樣本則為基影像與特徵向量之線性組合；測試階段針對測試之影像利用訓練之特徵向量與測試影像之特徵向量距離辨別是否為瑕疵影像。實驗結果顯示訓練之基影像可以充分代表LCD 無瑕疵影像具有源不均的之組成，亦使得特徵向量可有效的於測試階段將瑕疵與無瑕疵影像分辨出來。本論文所提出之方法具有良好之計算效率，十分適於即時之線上檢測。

關鍵字

瑕疵偵測 ； 表面檢測 ； 統計製程管制 ； 獨立成份分析 ； 薄膜電晶體液晶顯示器 ； 粒子群聚演算法

並列摘要

In this dissertation, two ICA-based approaches have been proposed for process monitoring of 1-D time-series data and mura detection of 2-D images in TFT-LCD manufacturing. For 1-D signal process monitoring and control, independent components (ICs) are used as source signals for statistical process control. To improve the yield of Liquid Crystal Display (LCD) panels, process control becomes a critical task in LCD manufacturing. In this study, a control chart based on Independent Component Analysis (ICA) is proposed to monitor TFT-LCD process variation. The proposed method can be effectively used in the monitoring of LCD critical process parameter, called Total Pitch (TP). TP is a parameter that is used to control alignment errors in TFT-LCD process. TP variations will cause serious defects like mura (brightness unevenness of a panel) and small bright points on the display area of LCD panels. Since the collected data could be a mixture of noise and different source signals, ICA is first applied to separate mixed data into independent components. The X-bar and R control charts are then used to monitor the separated source signals. Experimental results on real measured data of TP in the TFT-LCD process show that the proposed method can reliably detect process variations.

For mura inspection in 2-D images, a machine vision approach is proposed for detecting local irregular brightness in low-contrast surface images and, especially, with focus on Mura defects in LCD panels. A Mura defect embedded in a low-contrast surface image shows no distinct intensity from its surrounding region, and the sensed image may also present uneven illumination on the surface. All these make the Mura defect detection in low-contrast surface images extremely difficult. A set of basis images derived from defect-free surface images are used to represent the general appearance of a clear surface. Each LCD image is then constructed as a linear combination of the basis images, and the coefficients of the combination form the feature vector for discriminating Mura defects from clear surfaces. In order to find minimum number of basis images for efficient and effective representation, the basis images are designed such that they are both statistically independent and spatially exclusive. An ICA-based model that finds both the maximum negentropy for statistical independence and minimum spatial correlation for spatial redundancy is proposed to extract the representative basis images. Experimental results have shown that the proposed method can effectively detect various Mura defects in low-contrast LCD panel images. It is also computationally very fast for real-time, on-line inspection.

並列關鍵字

Defect detection ； Surface inspection ； Statistical process control ； TFT-LCD ； Independent component analysis ； Particle swarm optimization

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