A fuzzy neural network approach for quantitative evaluation of mura in TFT-LCD

Mura is a typical region defect of TFT-LCD, which appears as low contrast, non-uniform brightness regions, typically larger than a single pixel. It is caused by a variety of physical factors such as non-uniformly distributed liquid crystal material and foreign particles within the liquid crystal. As compared to point defect and line defect, mura is relatively difficult to be identified due to its low contrast and no particular pattern of shape. Though automatic inspection of mura was discussed in many literatures, there is no an inspection method could be used to practical application because the defect models proposed were not consistent with the real ones. Since mura is of strong complexity and vagueness, so it is difficult to establish the accurate mathematical model of mura. Therefore, a fuzzy neural network approach for quantitative evaluation of mura in TFT-LCD is proposed in this paper. Experimental results show that a fuzzy neural network is very useful in solving such complex recognition problems as mura evaluation

[1]  Toru Yoshizawa,et al.  Evaluation and discrimination method of 'mura' in liquid crystal displays by just noticeable difference observation , 2002, International Symposium on Optomechatronic Technologies.

[2]  Yakov Frayman,et al.  Data Mining Using Dynamically Constructed Recurrent Fuzzy Neural Networks , 1998, PAKDD.

[3]  Ryoji Yoshitake,et al.  Extraction and evaluation of mura in liquid crystal displays , 2001, Optics + Photonics.

[4]  Lipo Wang,et al.  Gene selection and cancer classification using a fuzzy neural network , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..

[5]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[6]  Yu-Ching Lin,et al.  Systems identification using type-2 fuzzy neural network (type-2 FNN) systems , 2003, Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No.03EX694).

[7]  Zhu-Zhi Yuan,et al.  Predicting nonlinear network traffic using fuzzy neural network , 2003, Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint.

[8]  William K. Pratt,et al.  Automatic blemish detection in liquid crystal flat panel displays , 1998, Electronic Imaging.