<title>Extraction and evaluation of mura in liquid crystal displays</title>

Abstract

The visual performance ofliquid crystal displays (LCDs) has usually been evaluated by visual inspection during the manufacturing process. One ofthe visual problems hardest to recognize are regions oflow-contrast and non-uniform brightness called muras. The accurate and consistent detection ofthe muras is extremely difficult because there are various shapes and sizes ofmuras and the inspection results tend to depend on the operators. We conducted a study on the quantitative evaluation ofmuras based on visual analysis , intending to clarify the detection method and create an automated mura inspection process. We developed an algorithm and a hardware system based on a commercially available CCD camera and a PC with an image processor board. This system can successfully identify and evaluate muras. The algorithm was developed from research on visual analysis and human perception. We converted the front-of-screen (FOS) images from the LCDs into distributions ofluminance information, and the mura regions were distinguished from the background area using our novel algorithm. This approach also led to a weighting function for the categories ofmuras that appear in the panels . Our identification method can also distinguish between the muras caused by flaws in the LCD cells and the intentionally designed non-uniform luminance distribution ofthe backlight.

A novel dot‐pattern generation to improve luminance uniformity of LCD backlight

“…There are also several other issues to be solved in the field of optical simulation in LCDs. It was only very recently that the automated measurement of luminance "muras" was possible 12 in spite of its importance. By developing evaluation techniques of this kind, as well as theoretical design methodologies such as the DLDS method, it is necessary to promote optical design automation.…”

Mura‐type effect on human‐vision inspection

“…Experiments were modified based on SEMI and Mori's experiment. 11,12 In the current study, JNDs of the human eyes were measured by the psychophysics method of 2AFC (two alternatives, forced-choice) to prevent a large judging difference among subjects and using QUEST 7 to shorten the experimental time. The visual contrast threshold of each pattern would be obtained, depending on the subject's response to the changed pattern contrast.…”

“…(10)Depending on whether the subject's judgment was right or wrong, the program prompted an audio feedback: the signal for "correct" was a "ding," and that for "incorrect" was a "kwang." (11)The result of the judgment was recorded by the computer.…”

P‐71: Investigation of Optical Films Ripple Phenomenon in TFT‐LCD Module by Numerical Computing

“…The FOS quality is usually evaluated by experienced inspectors and inspection system during manufacturing process. Due to disadvantage of sensory analysis, quantitative evaluation algorithm technique is introduced to effectively extract and assess mura that achieves high-quality stabilization and control in manufacturing inspection process [1][2][3]. Ripple of optical film, in fact, is the wrinkling of thin elastic sheet.…”