Study On The Methods Of Machine Vision Inspection For The Mura Defect Of TFT-LCD Process

With the development of LCD toward large area, thin thickness and high resolution, the size of glass substrate increases rapidly and the thickness decreases gradually, which makes a remarkable occurrence probability of Mura defect. Due to high resolution, the traditional manual inspection method hardly satisfies the quality and efficiency of LCD manufactures, thus a fast and objective automatic machine vision inspection way that in accordance with the human criterion is very important to LCD insdustry.This thesis makes studies of Mura defect inspection technique based on the key problem of vision inspection for TFT-LCD. Mura is local lightness variation with low contrast, blurry contour and complicated image background, so it is hard to be inspected with traditional thresholding or edge detection methods. This thesis is to study the automatic vision inspection way for Mura defect which in accordance with the human criterion, focusing on textural background suppression, uneven brightness adjustment, defect segmentation and quantification. The main contents and achievement are as follows:Firstly, considering the respective textural background of Mura defect, a background suppression method based on the real Gabor wavelet filter is proposed and the design principles of filters are studied. Designed according to the texture characteristics, the real Gabor wavelet filter can eliminate the background and enhance Mura defect, acting as an excellent blob detector. Comparing with usual background suppressin method, Gabor wavelet filter is similar with the vision characteristic of human eyes and can obtain good accordance with manual inspection results. And it's robustness to adapt to the backgroud uneveness, image rotation and distortion.Secondly, through analyzing the reasons of uneven brightness, all uneven factors are treated as the multiplicative illuminance. The homomorphic filtering method is used to transform the multiplicative uneveness into additive one, and then the independent component analysis method is applied to the estimation and separation of mixed signals. Thus the uneven brightness signals are eliminated or lightened and the target defective singals are preserved. The simulation results show that the proposed method can realize blind separation of uneven brightness and moire fringe.Thridly, the segmentation method of Mura defet is studied after background suppression and brightness adjustment. By comparing the edge-based and region-based active contour models, a modified Chan-Vese active contour model together with the level set method is proposed to automatically segment the Mura defect, aiming at the characteristics of low contrast, blurry contour and background uneveness of Mura. The modified model can exactly trace the blurry contours and eliminate the influences of uneven background luminance. At the same time, the parameters of Mura contrast and area from segmentation are directly used in the defect quantification based on SEMI standard.Finally, the image samples of LCD with Mura defects are captured based on the regulations of SEMI standard, as well as, the background suppression,brightness adujstment and Mura segmentation method proposed above are verified. For real Gabor wavelet filter, the influences of parameters on the background suppression effects are compared and the design principles are proved through experiments. Results show that this method can eliminate the textural background of LCD rapidly and reliably. Further, for the modified Chan-Vese active contours model together with the level set method, it is appropriate and excellent to the Mura segmentaion. The unconditional stable AOS scheme can increase the time step and save the solution time of level set function. Then, with the parameters of Mura contrast and area from segmentation, the quantifiction experiments are carried out conveniently. In addition, the proposed method can realize good brightness adjustment of LCD images and the defect segmentation becomes easier and more exactly after adjustment. Finally, the automatic inspection process of Mura defect is set up. Inspection experiments show that for 50 samples with Mura, 48 samples are inspeced accurately.