Inspection of defect on LCD panel using polynomial approximation

This paper proposes a surface fitting algorithm for inspection of flat panel display(FPD) based on multilevel B-spline approximation(MBA). FPD devices have non-uniform background, and shape and intensity of defects are very various. MBA is useful algorithm for surface fitting but it is sensitive to number of control lattices. As level increases, details of original image can be reconstructed. To detect defect, defective area must not be well-reconstructed. In this paper, we propose a level decision method of MBA for FPD. After process of each level, we decide whether stop to increase level of MBA or not with simple distance measure. Experimental result shows that our proposed method makes to detect defects easy.

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“…Some previous work shows satisfiable result [6] [7]. Adaptive multilevel threshold [6] is very useful and powerful.…”

Section: Introductionmentioning

confidence: 89%

“…This block is the reason of errors and long processing time. Another approach is surface fitting method based on polynomial approximation introduced in [7]. In this case, it is simple and easy, but orthogonality between vertical and horizontal polynomial is not proved.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Surface Fitting for Inspection of FPD Devices Using Multilevel B-spline Approximation

Lee

Kim

Chung

et al. 2005

TENCON 2005 - 2005 IEEE Region 10 Conference

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“…(2) is small enough to eliminate the noise, whereas that in Eq. (7) has to be large enough to accurately reconstruct the background. Here, r and s are chosen following [s, r] = floor([M, N]/6).…”

Section: Low-pass Filtering Proceduresmentioning

confidence: 99%

“…5 Based on the sensitivity of the human eye to mura, the level for each mura candidate is quantified using the concept of just-noticeable difference (JND), which is used to identify real muras by grading as either pass or fail. Two methodologies for the detection of mura defects have recently been proposed: background image reconstruction [5][6][7][8][9][10][11][12][13][14][15][16] and image segmentation technique. [16][17][18] The reference image can be obtained by background image reconstruction of the DUT source image or by using the representation of basis images.…”

Section: Introduction To Thin-film-transistor-liquid-crystal Display mentioning

confidence: 99%

A new algorithm on the automatic TFT‐LCD mura defects inspection based on an effective background reconstruction

et al. 2017

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In this study, an automatic detection method for mura defects is developed based on an accurate reconstruction of the background and precise evaluation of the mura index level. To achieve this, an effective background reconstruction method is first developed to represent the brightness intensity of the display panel. As a result, any nonuniform brightness of the background can be removed effectively. Furthermore, the associated mura level is quantified based on the sensitivity of the human eye in order to alternatively grade the liquid‐crystal display panels. The main focus of this study is on the reconstruction of the background from the display under test image. The proposed method takes full advantage of the following three existing methods: low‐pass filtering, discrete cosine transform, and polynomial surface fitting. By applying the method to several case studies, we have shown that it is more effective compared with other existing methods in detecting various types of mura defects.

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“…However, the target of inspection is limited to contact-hole patterns, and the technique is not designed to address the recognition of defect areas containing a wide variety of defects. In other fields, such as thin film transistor (TFT) display panel manufacturing, similar inspection techniques have also been proposed [13,14]. However, optical microscope images are used for inspection, because the defects that occur in the fabrication of TFT devices are measured in micrometers, and techniques involving the use of SEM images have not been proposed.…”

Section: Introductionmentioning

confidence: 99%

Automatic recognition of defect areas on a semiconductor wafer using multiple scanning electron microscope images

Nakagaki

Honda

Nakamae

2009

Meas. Sci. Technol.

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A technique for high-precision and automatic recognition of defect areas on a semiconductor wafer using scanning electron microscope (SEM) images is proposed. The proposed technique inputs multiple SEM images formed by selectively detecting secondary electrons and backscattered electrons emitted from the specimen by irradiating with primary electrons, and defect areas are then automatically recognized by comparison with reference images. The number of detected secondary electrons and backscattered electrons is highly dependent on the surface roughness of the defect areas, namely the height and depth of defects; therefore, a surface-roughness analysis from input images is conducted and the result is used to determine the mixing proportion for multiple difference images. The proposed technique aims to obtain high recognition accuracy for process wafers that contain various kinds of defects with a wide variety of height and depth. The technique provides effective pre-processing for automating the classification of defects, and is expected to contribute to improvements to the efficacy of process monitoring and yield management in the fabrication of semiconductor devices. Experimental results with two process wafers (involving 200 defect samples, each of which belongs to one of the nine defect classes) have confirmed that the proposed technique is capable of automatic recognition of defect areas with an accuracy of 98.9%.

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Inspection of defect on LCD panel using polynomial approximation

Cited by 12 publications

References 5 publications

Adaptive Surface Fitting for Inspection of FPD Devices Using Multilevel B-spline Approximation

Adaptive Surface Fitting for Inspection of FPD Devices Using Multilevel B-spline Approximation

A new algorithm on the automatic TFT‐LCD mura defects inspection based on an effective background reconstruction

Automatic recognition of defect areas on a semiconductor wafer using multiple scanning electron microscope images

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