KR20140071016A - Panel Inspection Method - Google Patents

Panel Inspection Method Download PDF

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Publication number
KR20140071016A
KR20140071016A KR1020120138831A KR20120138831A KR20140071016A KR 20140071016 A KR20140071016 A KR 20140071016A KR 1020120138831 A KR1020120138831 A KR 1020120138831A KR 20120138831 A KR20120138831 A KR 20120138831A KR 20140071016 A KR20140071016 A KR 20140071016A
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KR
South Korea
Prior art keywords
value
panel
image
gradation value
modulation
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KR1020120138831A
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Korean (ko)
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KR101946581B1 (en
Inventor
김태호
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엘지디스플레이 주식회사
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Priority to KR1020120138831A priority Critical patent/KR101946581B1/en
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Publication of KR101946581B1 publication Critical patent/KR101946581B1/en

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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/84Systems specially adapted for particular applications
    • G01N21/88Investigating the presence of flaws or contamination
    • GPHYSICS
    • G02OPTICS
    • G02FDEVICES OR ARRANGEMENTS, THE OPTICAL OPERATION OF WHICH IS MODIFIED BY CHANGING THE OPTICAL PROPERTIES OF THE MEDIUM OF THE DEVICES OR ARRANGEMENTS FOR THE CONTROL OF THE INTENSITY, COLOUR, PHASE, POLARISATION OR DIRECTION OF LIGHT, e.g. SWITCHING, GATING, MODULATING OR DEMODULATING; TECHNIQUES OR PROCEDURES FOR THE OPERATION THEREOF; FREQUENCY-CHANGING; NON-LINEAR OPTICS; OPTICAL LOGIC ELEMENTS; OPTICAL ANALOGUE/DIGITAL CONVERTERS
    • G02F1/00Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating, or modulating; Non-linear optics
    • G02F1/01Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating, or modulating; Non-linear optics for the control of the intensity, phase, polarisation or colour 
    • G02F1/13Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating, or modulating; Non-linear optics for the control of the intensity, phase, polarisation or colour  based on liquid crystals, e.g. single liquid crystal display cells
    • G02F1/1306Details
    • G02F1/1309Repairing; Testing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection

Abstract

The present invention relates to a panel inspection method for calculating modulation gradation values of various image patterns displayed on a panel to be inspected and using difference values of the modulation gradation values and a Gaussian blending of image patterns of the panel to be inspected and image patterns of the normal panel (Gaussian Mixture Model) is provided to determine whether a panel is defective or not.

Description

{Panel Inspection Method}

Panel inspection method.

In recent years, many display devices having various excellent image qualities have been developed. It is important to check the presence or absence of defects in the panel displaying the screen in the display device for an excellent image quality. The image quality of the panel is inspected during the manufacturing process of the panel to produce the panel that exhibits defect-free image quality. Particularly, a foreign substance is contained in the filter included in the panel during the manufacturing process, and a screen including the panel may be displayed due to the foreign substance. In addition, a misalignment of the component may occur due to the impact of the panel during the manufacturing process, and thus a screen including the unevenness may appear on the panel. In addition to these reasons, marks may be formed on the panel due to the pressing of the film in the panel. Conventionally, the inspector changed the pattern in order to detect the panel in which the stain was visually observed, and visually checked the panel for abnormality. However, because the characteristics of the stain defect are not constant, the inspection standard is ambiguous and there is a problem that the quality of the panel is not constant due to individual differences among the inspectors.

The present invention provides a method of calculating a modulation tone value of each of various patterns of a panel to be inspected and analyzing a difference value of each of the modulation tone values to inspect the panel.

The present invention provides a method of inspecting a panel using a result of comparing a Gaussian Mixture Model of an image pattern of a panel to be inspected and an image pattern of a normal panel.

The method includes displaying first through third image patterns on a panel to be inspected; Photographing the first to third image patterns displayed on the panel to be inspected, respectively, to implement first to third photographed images; The second gradation value of the second photographed image, and the third gradation value of the third photographed image by the first to third set gradation values, Calculating a first modulation gradation value, a second modulation gradation value of the second captured image, and a third modulation gradation value of the third captured image; A difference value between the modulation gradation value of at least one of the second modulation gradation value of the second captured image and the third modulation gradation value of the third captured image and the first modulation gradation value of the first captured image is Calculating; And analyzing the difference value to check whether the panel to be inspected is defective or not.

The present invention relates to a panel inspection method for calculating various pattern modulation tone values displayed on a panel to be inspected, a panel inspection method using difference values of the modulation tone values, a Gaussian blending model of image patterns of the panel to be inspected, Mixture Model) is provided to determine whether there is a defect in the panel with high reliability.

1 is a block diagram of a panel inspection method according to a first embodiment of the present invention;
2A shows an image expressed by a first modulation gray level value using a 127 gray level image pattern.
FIG. 2B shows an image expressed by a second modulation gray value using a white image pattern. FIG.
FIG. 2C is a diagram for calculating a difference value between the first modulation gradation value and the second modulation gradation value and modulating the level of the difference value. FIG.
FIG. 3A is a diagram showing tone values of respective pixels of an image represented by a first modulation gray level value using a 127 gray level image pattern; FIG.
FIG. 2B is a graph illustrating tone values of respective pixels of an image represented by a second modulation tone value using a white image pattern. FIG.
FIG. 2C is a diagram showing a gray level value of each pixel of an image obtained by calculating a difference value between the first modulation gray level value and the second modulation gray level value and modulating the level of the difference value. FIG.
4 is a block diagram of a panel inspection method according to a second embodiment of the present invention;
5A is a graph relating to a Gaussian mixture model of a normal panel.
5B is a graph relating to a Gaussian mixture model of a panel to be inspected.

Hereinafter, a panel inspection method according to the present invention will be described with reference to the drawings.

1 is a block diagram of a panel inspection method according to a first embodiment of the present invention.

Referring to FIG. 1, a panel inspection method according to a first embodiment of the present invention includes displaying first to third image patterns of a panel to be inspected, displaying first to third image patterns Converting the gradation values of the first to third photographed images into the first to third modulation gradation values, and outputting the second modulation gradation value and the first or third Calculating a difference value from the modulation gradation value, modulating a level of the difference value, and estimating a defective area of the panel to be inspected.

In the step of displaying the first to third image patterns of the panel to be inspected, the second image pattern may be a white pattern, but the present invention is not limited thereto, It can be used as a reference image pattern in the present invention.

The first or third image pattern may be a 127-gradation pattern, a 200-gradation pattern, a mosaic pattern, or a black pattern, but is not limited thereto. In particular, in the case of a 200-gradation pattern and a 127-gradation pattern, relatively stained is visually recognized. However, the present invention is not limited to this, and visibility may vary depending on the type of stains.

In the step of photographing the displayed first through third image patterns and generating the first through third photographic images, the inspection target panel is photographed by the camera while changing patterns displayed on the inspection target panel. The camera may use an area camera for photographing the entire area of the screen, and a line camera for photographing the entire area of the screen, but the present invention is not limited thereto.

Wherein the step of converting the gradation values of the first to third photographed images into the first to third modulation gradation values includes: a first gradation value of the first photographed image, a second gradation value of the second photographed image, The third gradation value of the photographed image is compared with the reference gradation value and added or subtracted by the first to third set gradation values.

For example, if the reference tone value is 130 tones, the first tone value of the first shot image is 127 tones, the second tone value of the second shot image is 255 tones, and the third tone value of the third shot image is 200 tones The difference between the 127th gray level value as the first gray level value and the 130th gray level value as the reference gray level value corresponds to the first set gray level value and the value becomes 3 gray level value. In this case, the third tone value, which is the first setting tone value, is added to the first tone value. In other words, when the gradation value of each pixel of the first captured image approximates to the 127th gradation value and the third gradation value which is the first set gradation value is added to each pixel, the gradation value of each pixel is approximated to the 130th gradation value To the first modulation gradation value.

The difference between the second tone value 255 tone value and the reference tone value 130 tone value corresponds to the second set tone value, and the value corresponds to the 125 tone value. In this case, the second set tone value 125 is subtracted from the second tone value. In other words, if the tone value of each pixel of the second shot image approximates 255 tone values and subtracts 125 tone values as the second set tone value in each pixel, the tone value of each pixel is approximately 130 tone value To the second modulation gradation value.

The difference between the third gradation value 200 gradation value and the reference gradation value 130 gradation value corresponds to the third set gradation value, and the value corresponds to 70 gradation value. In this case, the third tone value 70 is subtracted from the third tone value. In other words, if the gradation value of each pixel of the third photographic image approximates 200 gradation values and the third gradation value of 70 pixels is subtracted from each pixel, the gradation value of each pixel is approximately 130 gradations To the third modulation gradation value.

Calculating a difference value between the second modulation gradation value and the first or third modulation gradation value, calculating a difference value between the second modulation gradation value and the first modulation gradation value, The difference value with the third modulation gradation value can be obtained.

For example, if the second image pattern displayed on the speckled panel is a white pattern, the speckle may not be well visible. Therefore, when the panel is photographed by a camera, each pixel in the photographed image can have a value in the vicinity of 255 gradations. Therefore, if the 125th gray scale value is subtracted from the 255th gray scale value, all pixels in the photographed image can have a value in the vicinity of 130 gray scales.

However, when the first image pattern having 127 gradations is displayed on the uneven panel, the unevenness can be visually recognized. Therefore, when the panel is photographed with a camera, a portion of the pixels in the photographed image that does not have an unevenness can have a value in the vicinity of 127 gradations, and a portion with unevenness can have a gradation value that is greatly different from 127 gradations. Therefore, when the third tone value, which is the second setting tone value, is added to the tone value of each pixel, only the unevenness-free portion of the pixels in the photographed image can have a value in the vicinity of 130 tones, I can have a tonal value. At this time, if the difference value between the first modulation gradation value and the second modulation gradation value is calculated, the uneven portion can have the approximate 0 gradation value, and the portion without the unevenness can have the non-zero gradation value have.

In the step of modulating the level of the difference value, it is possible to modulate the difference value of the speckled area and the difference value of the speckled area so that the difference becomes large.

In the defective area estimation step of the inspection target panel, the defective area of the inspection target panel can be estimated by analyzing the difference value in which the level is modulated.

As a method of analyzing the difference value in which the level is modulated, when the gray level value in the specific area is compared with the gray level value of the surrounding area and the difference is larger than the setting range, the specific area can be estimated as a defective area. The setting range may vary depending on the resolution of the panel used for the inspection.

Meanwhile, by analyzing the difference value between the first modulation gradation value and the second modulation gradation value, the defective area of the panel to be inspected is estimated, and the difference value between the second modulation gradation value and the third modulation gradation value is analyzed, Of the defective area. In both cases, if the defective area does not appear, the panel to be inspected can be regarded as a regular product. In order to increase the reliability of the inspection, it is possible to estimate the defective area of the panel to be inspected by calculating modulation gradation values of various patterns not limited to the first to third image patterns.

FIG. 2A is an image represented by a first modulation gray level value using a 127 gray level image pattern, FIG. 2B is an image expressed by a second modulation gray level value by using a white image pattern, FIG. 2C is an image expressed by a first modulation gray level value, FIG. 8 is a diagram showing an image obtained by calculating the difference value of the second modulation gradation value and modulating the level of the difference value; FIG.

Referring to FIGS. 2A, 2B, and 2C, dotted lines indicate areas where dots are formed, and images obtained by photographing 127 patterns are not visible. However, white dots are not well visible. In the case of the image in which the level of the difference value is modulated, the unevenness is best seen.

FIG. 3A is a diagram showing tone values of respective pixels of an image represented by a first modulation tone value using a 127-tone tone image pattern, FIG. 2B is a diagram illustrating tone values of pixels of an image expressed by a second modulation tone value using a white image pattern, FIG. 2C is a diagram showing a difference value between the first modulation gradation value and the second modulation gradation value, and a gray level value of each pixel of an image obtained by modulating the level of the difference value. FIG.

Referring to FIG. 3A, in the case of the 127 gray image pattern, the gray level value in the pixel area where the smear appears can be represented by a gray level value (120 gray level) which is somewhat lower than the surrounding (130 gray level). Referring to FIG. 3B, in the case of the white gradation image pattern, the gray scale values of all the pixels can be expressed by 130 gray levels. This is because the white image pattern of the panel is not well visible, and the pixel tone value of the white pattern image imaged by the panel is represented by a value in the vicinity of 130 gray scales.

Referring to FIG. 3C, when the difference value between the first modulation gradation value and the second modulation gradation value is calculated, the pixel showing the same gradation value becomes the 0 gradation value, and the gradation value of the pixel showing the unevenness becomes -10 .

Referring to FIG. 3D, the difference value level adjusting unit adjusts the offset of each pixel by 127 in order to amplify the tone value of each pixel by setting the gain of the difference value to 10, A pixel having a gray level value as shown in FIG. 3D can be implemented. Specifically, among the pixels having the difference value between the first modulation gradation value and the second modulation gradation value as the tone value, the pixels in the unevenness area are 0 gradations. When the 0 gradation value is multiplied by 10 and the gain is 10, the 0 gradation value is output. If the offset is 127, the 127 gradation value obtained by adding the 127 gradation value from the 0 gradation value finally becomes the gradation value of each pixel. The pixels in the area where the smudges exist are -10 gradation values. When the gain is 10, -100 gradation values are obtained. When the offset is adjusted, -100 + 127 = 27 gradation values. As a result, the gradation values of the uneven area and the uneven area differ by 100 gradations through the difference value level adjustment, so that the contrast ratio becomes large and the unevenness can be visually recognized.

In the case of the 127-gray image pattern, although the pattern is visible in the panel, it may not be clearly displayed. In the case of the white image pattern, there may be a spot in the panel but it may not be visually recognized. However, the image of each pattern is photographed with the camera for panel inspection, and the gradation value of each image is compared with the reference value and added / subtracted by the set gradation value, the modulation gradation value of each photographed image is calculated therefrom, It is possible to confirm that the unevenness is visibly recognized by adjusting the level of the difference value.

On the other hand, the gray level values represented in FIGS. 3A, 3B, and 3C are approximate values, and in practice, each pixel can have a gray level value near the gray level value.

The type of stain that can occur within the panel can vary to an indeterminate degree. Therefore, in order to detect the above-mentioned unevenness, the pattern of the panel to be inspected must be continuously inspected while changing, so that the predictability, accuracy, and promptness may be deteriorated. However, when the panel inspection method according to the first embodiment of the present invention is used, the predictability can be increased, and the accuracy and promptness can be increased.

4 is a block diagram of a panel inspection method according to a second embodiment of the present invention.

In the second embodiment of the present invention, the same elements as those described in the first embodiment of the present invention are denoted by the same reference numerals, and a detailed description thereof will be omitted.

Referring to FIG. 4, the panel inspection method according to the second embodiment of the present invention includes displaying first through third image patterns of a panel to be inspected, photographing the first through third image patterns, Converting the gradation values of the first to third photographed images into the first to third modulation gradation values, calculating a difference between the second modulation gradation value and the first or third modulation gradation value, Calculating a Gaussian mixture model of the fourth image pattern of the panel to be inspected, a Gaussian mixture model of the fourth image pattern of the normal panels, A comparison step of a Gaussian mixture model of the fourth image pattern of the panel to be inspected and a Gaussian mixture model of the fourth image pattern of the normal panels, Can.

The Gaussian Mixture Model represents the number of pixels of the gray level of the normal panel as a Gaussian distribution. And a Gaussian distribution of a number of normal panels.

In the defective panel estimation step, it can be estimated that the pixels in the specific area among the pixels in the panel are blurred in the specific area when there is a large difference between the gray values in comparison with the pixels in the surrounding area. For example, if the first leftmost pixel on the upper left side of the panel to be inspected is expressed as coordinates (1, 1) and the rightmost pixel on the lower side of the panel to be inspected is represented as coordinates (100.100) The panel's coordinates (2,10), (2,11), (2,12), (3,10), (3,11), (4,10), (4,11) (2,10), (2,11), (2,12), (3,10), and (3,10) when the grayscale value of the pixel of the pixel of interest is 20 grayscale and the grayscale value of the pixel corresponding to the surrounding coordinates is 100 grayscale 3, 11), (4, 10), (4, 11), and (4, 12).

The Gaussian mixture model of the fourth image pattern of the panel to be inspected and the Gaussian mixture model of the fourth image pattern of the normal panels are compared when it is estimated that the subject panel is defective in the defective panel.

A Gaussian mixture model of the fourth image pattern of the panel to be inspected is implemented and a Gaussian mixture model of the fourth image pattern of the normal panels is implemented so that the Gaussian mixture model comparison step compares the tone values of the respective image patterns with each other . In the monochromatic determination step, it is possible to determine whether the panel is defective based on the gray level values compared for each pixel.

The fourth pattern in the image of the fourth pattern may be the same as or different from any one of the first through third patterns, but is not limited thereto.

The panel inspection method according to the second embodiment of the present invention compares the Gaussian mixture model of each panel to determine whether the panel is defective, thereby further improving the reliability.

5A and 5B are graphs showing a Gaussian mixture model of the normal panel and the inspection target panels.

The horizontal axis of the graph represents the tone value, and the vertical axis represents the number of pixels per tone.

Referring to FIG. 5A, a Gaussian model of normal panels is generated, and a Gaussian mixture model of normal panels is generated by mixing the Gaussian models. It can be seen that the pixels having the tone values near the first and second tone values appear most in the Gaussian mixture model of the normal panels.

Referring to FIG. 5B, in the Gaussian mixture model of the panel to be inspected, it can be seen that not only the first and second gradation but also many pixels having values near the third gradation value are displayed. This is because a plurality of pixels having a third gray level value not appearing in the Gaussian mixture model of the normal panels are displayed, and the panel to be inspected can be determined as a panel including a blur defect.

On the other hand, in order to increase the reliability in determining whether or not the panel to be inspected is defective, the number of Gaussian models of the normal panel may be increased and used as a Gaussian Mixture Model of the normal panels.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is clearly understood that the same is by way of illustration and example only and is not to be taken by way of limitation, It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention. Therefore, the technical scope of the present invention should not be limited to the contents described in the detailed description of the specification, but should be defined by the claims.

Claims (5)

  1. Displaying the first to third image patterns on an inspection target panel, respectively;
    Photographing the first to third image patterns displayed on the panel to be inspected, respectively, to implement first to third photographed images;
    The second gradation value of the second photographed image, and the third gradation value of the third photographed image by the first to third set gradation values, Calculating a first modulation gradation value, a second modulation gradation value of the second captured image, and a third modulation gradation value of the third captured image;
    A difference value between the modulation gradation value of at least one of the second modulation gradation value of the second captured image and the third modulation gradation value of the third captured image and the first modulation gradation value of the first captured image is Calculating; And
    And inspecting the presence or absence of defects in the panel to be inspected by analyzing the difference value.
  2. The method according to claim 1,
    And changing a gradation of the difference value to calculate a fourth set gradation value.
  3. 3. The method of claim 2,
    Wherein the fourth set tone value is set such that a difference between the tone value of the defective area and the tone value of the other area becomes larger.
  4. The method according to claim 1,
    Wherein the first image pattern is a white image pattern.
  5. The method according to claim 1,
    Generating a first Gaussian mixture model of a fourth image pattern of the panel to be inspected;
    Generating a second Gaussian mixture model of a fourth image pattern of the normal panel; And
    And comparing the first and second Gaussian mixture models to determine whether the inspection target panel is defective.
KR1020120138831A 2012-12-03 2012-12-03 Panel Inspection Method KR101946581B1 (en)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20090074388A (en) * 2008-01-02 2009-07-07 삼성전자주식회사 Apparatus for inspecting of display panel and method thereof
KR20090094694A (en) * 2008-03-03 2009-09-08 엘지디스플레이 주식회사 Test apparatus and method for liquid crystal display
KR20120052767A (en) * 2010-11-16 2012-05-24 한국전자통신연구원 Apparatus and method for separating image

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20090074388A (en) * 2008-01-02 2009-07-07 삼성전자주식회사 Apparatus for inspecting of display panel and method thereof
KR20090094694A (en) * 2008-03-03 2009-09-08 엘지디스플레이 주식회사 Test apparatus and method for liquid crystal display
KR20120052767A (en) * 2010-11-16 2012-05-24 한국전자통신연구원 Apparatus and method for separating image

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