1D-based defect detection in patterned TFT-LCD panels using characteristic fractal dimension and correlations

Thin film transistor-liquid crystal display (TFT-LCD) is a major technology for flat panel display used in a wide range of electronic devices. As the TFT-LCD panel becomes dense, small defects can only be observed at an extremely high resolution. For fast imaging of a large-sized TFT-LCD panel at a high resolution, a one-dimensional (1D) line scan system is demanded. A TFT-LCD panel image at a fine resolution presents very complicated repetitive patterns, which increases the difficulty of the defect detection task. In this paper, we propose a 1D self-comparison defect detection scheme that directly works on the 1D line images of a TFT-LCD panel. The proposed method first uses the fractal transformation to enhance the periodicity and regularity of a 1D gray-level line image, and then divides the resulting fractal signal into small segments, each of the length of the repeated period. By calculating each divided segment’s normalized cross correlation with its neighboring segments and comparing the resulting correlation value with a predetermined threshold, the segments containing a defect can be effectively identified. Since the proposed method does not require a reference template, it is invariant to changes in illumination and image translation. Experimental results on a number of microdefects in patterned TFT-LCD panel surfaces show that the proposed method can well detect various ill-defined defects and is computationally very efficient.