LOG-filter-based inspection of cluster Mura and vertical-band Mura on liquid crystal displays

Abstract

In this paper, we propose the use of Laplacian of Gaussian (LOG) filters in the detection of Cluster Mura defects and Vertical-Band Mura defects on Liquid Crystal Displays. To detect Cluster Mura defects, 2-D LOG filters with their shapes matching the shapes of Cluster Mura are adopted. The optimal threshold for the detection of Cluster Mura is determined based on the SEMU formula. On the other hand, to detect Vertical-Band Mura, a 1-D LOG filter is used over the projected 1-D intensity profile to check if the variation tendency of the intensity profile is changed. The portions with inconsistently intensity variation are regarded as the portions where V-Band Muras occur. The simulation results demonstrate the LOG filters are very useful in the detection of Mura defects.

50.2: Mura Detection Automation in LCD Panels by Thresholding Fused Normalized Gradient and Second Derivative Responses

“…....... (1) Such that G(x,y) is the gradient response of image intensity matrix I(x,y), and is 2D convolution operator. k's are the Sobel kernels used to obtain the gradient in four directions (i.e.…”

“…Video Electronics Standards Association (VESA) [5] and Semiconductor Equipment and Material International (SEMI) [6] have spent a lot of efforts on setting standards for classifying and quantizing defects, respectively. Several Mura detection algorithms have been proposed in the literature, Chen et al [1] proposed a detection algorithm based on Laplacian of Gaussian (LoG) filter for cluster Muras. Song et al [2] utilized morphological operation tools in image processing to improve the delectability, and mainly designed to detect blob-Mura defects.…”

“…In fact, many researchers in the field have paid an immense attention to automate Mura detection and inspection [1]- [4]. Video Electronics Standards Association (VESA) [5] and Semiconductor Equipment and Material International (SEMI) [6] have spent a lot of efforts on setting standards for classifying and quantizing defects, respectively.…”

Automatic mura detection based on thresholding the fused normalized first and second derivatives in four directions

“…In fact, many researchers in the field have paid immense attention to automate the mura detection and inspection. [1][2][3][4] The Video Electronics Standards Association (VESA) 5 and Semiconductor Equipment and Material International (SEMI) 6 have spent much effort on setting standards for classifying and quantizing defects, respectively. Several mura-detection algorithms have been proposed in the literature.…”

“…Several mura-detection algorithms have been proposed in the literature. Chen et al 1 proposed a detection algorithm based on the Laplacian of the Gaussian (LoG) filter for cluster muras. Song et al 2 utilized morphological operational tools in image processing to improve the detectability, and it was mainly designed to detect blob-mura defects.…”