KR20140064435A - Apparatus and method for mura defect detection of display device - Google Patents

Apparatus and method for mura defect detection of display device Download PDF

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KR20140064435A
KR20140064435A KR1020120131768A KR20120131768A KR20140064435A KR 20140064435 A KR20140064435 A KR 20140064435A KR 1020120131768 A KR1020120131768 A KR 1020120131768A KR 20120131768 A KR20120131768 A KR 20120131768A KR 20140064435 A KR20140064435 A KR 20140064435A
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candidate
image
candidate regions
mura
feature
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KR1020120131768A
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Korean (ko)
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KR102020596B1 (en
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이상린
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엘지디스플레이 주식회사
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G—PHYSICS
    • G02—OPTICS
    • G02F—OPTICAL DEVICES OR ARRANGEMENTS FOR THE CONTROL OF LIGHT BY MODIFICATION OF THE OPTICAL PROPERTIES OF THE MEDIA OF THE ELEMENTS INVOLVED THEREIN; NON-LINEAR OPTICS; FREQUENCY-CHANGING OF LIGHT; OPTICAL LOGIC ELEMENTS; OPTICAL ANALOGUE/DIGITAL CONVERTERS
    • G02F1/00—Devices 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
    • G02F1/01—Devices 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 
    • G02F1/13—Devices 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
    • G02F1/1306—Details
    • G02F1/1309—Repairing; Testing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G01N2021/9513—Liquid crystal panels

Abstract

The present invention relates to an apparatus and a method for detecting a Mura of a display device. According to an embodiment of the present invention, there is provided a method of detecting blur in a display device, comprising: detecting image candidate regions by analyzing image information obtained from an image displayed on a display panel; Extracting features of the candidate regions; Loading a plurality of weights for each feature of an image; Calculating a quantization value of the candidate regions by applying a weight to each feature of the candidate regions; And comparing the quantified value of the candidate candidate regions with a reference value to detect the variance.

Description

TECHNICAL FIELD [0001] The present invention relates to a display device,

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to image quality distortion detection of a display device, and more particularly, to a Mura detection device and method of a liquid crystal display device.

As a liquid crystal display device (LCD) becomes larger in size, mura defects, that is, stain defects and image quality distortions are increasing in size and frequency. "Mura" means a stain in Japanese, which means that a specific area is displayed unevenly when the entire screen is displayed at a constant gray level.

The mura detection method according to the related art distinguishes all defects in which the contrast of the boundary is a conspicuous level irrespective of the shape and size of the defect. Generally, a defect of mura is discriminated by inspection on the eye of a person. However, there is a limitation in detecting mura defects in the conventional method as the size of the liquid crystal display device becomes larger. In addition, the degree of detection of mura defects may vary depending on the work skill of the operator, and there is a problem that the deviation of the mura detection increases with the larger screen.

1 is a view schematically showing a mura detection method according to the prior art.

Referring to FIG. 1, a mura inspection method using a SEMU developed by Semiconductor Equipment and Materials International (SEMI) has been proposed in order to improve the subjective inspection method of mura.

A method for detecting a mura in accordance with the related art using a SEMU will be described. After the input image data is preprocessed, an image is displayed on a display panel (S1).

Thereafter, the image displayed on the display panel is photographed with a CCD camera to obtain image information, and a non-image candidate area is detected based on the obtained image information (S2).

Thereafter, the luminance difference and the cognitive characteristic information with respect to the background are compared with respect to the candidate candidate region

SEMU method to quantify the distortion value of the image for the candidate candidate region (S3).

Thereafter, the final mura region is detected based on the quantization value generated in S3 (S4).

Although the mura detection method according to the related art eliminates subjective error factors of human beings, it is difficult to detect noise due to non-uniformity of luminance generated due to characteristics of a liquid crystal display device (LCD) .

In addition, although there is a possibility that due to a variety of factors, the characteristics of the candidate regions may be different from each other, the prior art can not reflect the importance of these various features.

SUMMARY OF THE INVENTION The present invention has been made to solve the above-mentioned problems, and it is an object of the present invention to provide an apparatus and method for detecting dust in a display device.

Other features and advantages of the invention will be set forth in the description which follows, or may be obvious to those skilled in the art from the description and the claims.

According to an aspect of the present invention, there is provided a method for detecting a mura of a display device, comprising: detecting image candidate regions by analyzing image information obtained from an image displayed on a display panel; Extracting features of the candidate regions; Loading a plurality of weights for each feature of an image; Calculating a quantization value of the candidate regions by applying a weight to each feature of the candidate regions; And comparing the quantified value of the candidate candidate regions with a reference value to detect the null.

According to an aspect of the present invention, there is provided a feature detection apparatus for a display device, including: a feature extractor for extracting features of candidate candidate regions obtained from an image displayed on a display panel; A weight applying unit for loading a weight corresponding to a feature of the candidate candidate regions among a plurality of weighted values provided for each feature of the image; A quantization unit for calculating a quantization value of the candidate candidate regions by applying a weight to each feature of the candidate candidate regions; And a non-matching determining unit for comparing the quantized value of the candidate non-candidate areas with a reference value to detect non-matching information.

According to the means for solving the above problems, the apparatus and method for detecting mura in the display apparatus according to the above-described embodiment of the present invention can improve the mura region detection performance.

The apparatus and method for detecting a deviation of a display apparatus according to an embodiment of the present invention described above can prevent an excessive detection of a mura and improve an error deviation of the mura detection.

The apparatus and method for detecting the defects of the display device according to the embodiments of the present invention can classify defects for products produced by the manufacturing process and provide a criterion for determining the level of defects, have.

Other features and effects of the present invention may be newly understood through the embodiments of the present invention in addition to the features and effects of the present invention mentioned above.

1 schematically shows a mura detection method according to the prior art;
Fig. 2 and Fig. 3 are diagrams showing a mura detection apparatus according to an embodiment of the present invention; Fig.
4 is a diagram showing a mura detection method according to an embodiment of the present invention.
5 is a diagram showing an example in which the weights of the mura detection method according to the embodiment of the present invention are applied.
FIG. 6 is a diagram showing a quantization value in which weights are reflected for each candidate candidate. FIG.
7 is a diagram showing a result of detection of mura according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, a preferred embodiment of an apparatus and method for detecting dust in a display device according to the present invention will be described in detail with reference to the drawings.

FIG. 2 and FIG. 3 are views showing a mura detection apparatus according to an embodiment of the present invention.

2 and 3, the mura detection apparatus according to the embodiment of the present invention includes a test image supply unit 200, a mura candidate region detection unit 300, and a mura detection unit 400. The mura detection unit 400 includes an image feature extraction unit 410, a weight application unit 420, a quantization unit 430, and a mura determination unit 440.

The display panel 100 drives a plurality of pixels formed in a matrix form according to the test image data supplied from the test image supply unit 200 to display an image.

Here, each of the plurality of pixels may be composed of three or four sub-pixels. For example, the sub-pixel may be divided into a red sub-pixel, a green sub-pixel, and a blue sub-pixel. The red sub-pixel, the green sub-pixel, and the blue sub-pixel constitute one pixel.

As another example, the sub-pixel may be divided into a red sub-pixel, a green sub-pixel, a blue sub-pixel, and a white sub-pixel. The red sub-pixel, the green sub- Pixels.

The display panel 100 may be a liquid crystal panel or an organic light emitting diode (OLED) panel.

When the liquid crystal panel is applied to the display panel 100, the liquid crystal panels may include a plurality of gate lines and a plurality of data lines arranged to cross each other and defining a plurality of pixels, a thin film transistor (TFT) And a lower substrate on which the lower substrate is formed. The liquid crystal display device further includes an upper substrate on which red, green, and blue color filters are formed, and a liquid crystal layer is formed between the lower substrate and the upper substrate.

Although not shown in the drawing, a driving circuit for driving the display panel 100 is provided. The driving circuit includes a gate driver for applying a scan signal to a gate line, a data driver for applying a video data signal to a data line, and a timing controller for controlling the elements, which will not be described in detail. .

The test image supply unit 200 generates first through fourth test images having different gray level values, and supplies the generated first through fourth test image data to the display panel 100.

For example, the first test image may be displayed at 32 gray levels, the second test image may be displayed at 64 gradations, the third test image may be displayed at 128 gradations, and the fourth test image may be displayed at 255 gradations. At this time, the test image supply unit 200 may sequentially arrange the first to fourth test images and supply the test images to the display panel 100. Alternatively, one of the first to fourth test images may be displayed on the display panel 100 .

The non-candidate candidate region detecting unit 300 captures a test image displayed on the display panel 100, acquires image information from the image displayed on the display panel 100, and analyzes the obtained image information to detect non-candidate regions. Thereafter, the non-candidate candidate region detection unit 300 provides the false candidate region information to the false match detection unit 400. [

Specifically, the non-candidate candidate region detecting unit 300 detects candidate candidate region information obtained by capturing a test image displayed on the front surface of the display panel 100, and outputs the candidate candidate region information to the image feature extracting unit 410 included in the non- to provide.

At this time, when the first to fourth test images are displayed on the display panel 100, unevenness candidate region information of the first to fourth test images corresponding to the first to fourth test images is provided to the image feature extraction unit 410.

On the other hand, if only one test image is displayed among the first to fourth test images on the display panel 100, the unrequited candidate region detecting unit 300 generates one unrequited candidate region information corresponding to the one test image, (410).

Referring to FIG. 3, the configuration and operation of the deviation detection unit 400 according to the embodiment of the present invention will be described in detail.

The image feature extraction unit 410 extracts features of candidate candidate regions obtained from the test image displayed on the display panel 100.

Specifically, the image feature extraction unit 410 extracts the feature of the image for each candidate region based on the candidate candidate region information provided by the candidate candidate region detection unit 300, as shown in FIG.

Here, the features of the candidate candidate regions may include the circularity, the size, the contrast, the density, the center point, the diagonal component, the horizontal variance, the vertical variance, and the horizontal and vertical variance of the image.

The weight applying unit 420 loads one weight or a plurality of weights corresponding to the characteristics of the candidate regions from among a plurality of weight values prepared for each feature of the candidate candidate region.

Here, as shown in FIG. 5, the weights are prepared in advance for each feature of the image displayed in the candidate candidate regions and stored in a memory (not shown). Of the plurality of weight values stored in the memory, And at least one weight corresponding to the weighting value. Here, FIG. 5 is a diagram illustrating an example in which weights of the mura detection method according to the embodiment of the present invention are applied.

The quantization unit 430 calculates the quantization value of each of the candidate candidate regions by applying the weighted value corresponding to the feature of the candidate candidate regions to the data value of the feature candidate regions.

Here, the data values of the features of the candidate regions may be sorted in descending order, and the weight may be applied to the data values. As shown in FIG. 6, it is also possible to calculate the quantization value of each candidate region by combining weighted values for each of the plurality of candidate regions, and combining the weighted values. Here, FIG. 6 is a diagram showing a quantization value in which weights are reflected for each candidate candidate.

The mura judging unit 440 compares the quantified value of the mura candidate regions calculated by the quantifying unit 430 with a predetermined reference value to judge final mura. As shown in FIG. 7, when the quantification value of the candidate candidate regions is equal to or greater than the reference value, the corresponding candidate candidate region is finally judged as null. Here, FIG. 7 is a diagram showing the result of detection of mura according to an embodiment of the present invention.

For example, the mura judgment unit 440 sets a rank for the candidate candidate regions based on the quantization value of candidate candidate regions, and detects the candidate according to a predetermined rank. The number of mura that is finally detected can be freely set, and if the number of final mura is set to one, a mura candidate corresponding to the first rank among the predetermined rank is detected as the final mura. If the number of the semi-final and final mura are set to two, the candidate mura corresponding to the first and second rank among the predetermined rank can be detected as the final mura.

As shown in Fig. 3, information of the finally detected mura is provided to the bad classification system 500, and in the bad classification system 500, based on the mura information, And determine the level of failure. Through this, it is possible to reduce the defects of products and improve the production yield by reflecting the information of mura to improvement work of future manufacturing process.

Hereinafter, a method of detecting the brightness of a display device according to an embodiment of the present invention will be described with reference to FIGS. 4 to 7. FIG.

First, the non-candidate candidate region detection unit 300 analyzes the image information obtained from the displayed image on the display panel to detect unadjacent candidate regions (S10).

Here, the test image supply unit 200 generates the first to fourth test images having different tone values, supplies the generated data of the first to fourth test images to the display panel 100, 100). ≪ / RTI >

At this time, the first test image may be displayed at 32 gray levels, the second test image may be displayed at 64 gradations, the third test image may be displayed at 128 gradations, and the fourth test image may be displayed at 255 gradations. At this time, the test image supply unit 200 may sequentially arrange the first to fourth test images and supply the test images to the display panel 100. Alternatively, one of the first to fourth test images may be displayed on the display panel 100 .

The non-candidate candidate region detecting unit 300 captures a test image displayed on the display panel 100, acquires image information from the image displayed on the display panel 100, and analyzes the obtained image information to detect non-candidate regions. Then, the non-candidate candidate region detection unit 300 provides the detected candidate region information to the image feature extraction unit 410 of the region detection unit 400.

Subsequently, the image feature extraction unit 410 extracts features of the candidate regions (S20). At this time, the image feature extraction unit 410 extracts features of candidate candidate regions obtained from the test image displayed on the display panel 100.

For example, the image feature extraction unit 410 extracts image features for each candidate candidate region based on the candidate candidate region information provided by the candidate candidate region detection unit 300. At this time, the features of the candidate candidate regions may include the circularity, the size, the contrast, the density, the center point, the diagonal component, the horizontal variance, the vertical variance and the horizontal and vertical variance.

Then, the weight applying unit 420 loads a plurality of weights for each feature of the image, and applies a weight to each feature of the candidate candidate regions (S30). At this time, the weights are prepared in advance for each feature of the image displayed in the candidate candidate regions and stored in the memory, and the weight application unit 420 receives at least one of the plurality of weight values stored in the memory, Lt; / RTI >

Then, the quantization unit 430 calculates quantization values of the candidate regions by applying the weight (S40). The quantization unit 430 calculates the quantization value of each of the candidate candidate regions by applying the weighted value corresponding to the feature of the candidate candidate regions to the data value of the feature candidate regions.

Here, the data values of the features of the candidate regions may be sorted in descending order, and the weight may be applied to the data values. As shown in FIG. 6, it is also possible to calculate the quantization value of each candidate region by combining weighted values for each of the plurality of candidate regions, and combining the weighted values.

Then, a quantization value of the candidate candidate regions is compared with a reference value to detect a null (S50). The mura judging unit 440 compares the quantified value of the mura candidate regions calculated by the quantifying unit 430 with a predetermined reference value to judge final mura. As shown in FIG. 7, when the quantization value of the unallocated regions is equal to or greater than the reference value, the corresponding unacceptable candidate region is finally judged as null.

Here, the mura judging unit 440 sets a rank for the candidate candidate regions based on the quantization value of the candidate candidate regions, and detects the candidate candidate regions according to the predetermined ranking.

On the other hand, the number of mura that is finally detected can be freely set, and if the number of final mura is set to 1, the mura candidate corresponding to the first rank among the predetermined ranks is detected as the final mura. If the number of the semi-final and final mura are set to two, the candidate mura corresponding to the first and second rank among the predetermined rank can be detected as the final mura.

The apparatus and method for detecting mura in the display apparatus according to the embodiment of the present invention can improve the mura region detection performance. Further, excessive detection of the mura or improvement of the error deviation of the mura detection can be achieved.

It will be understood by those skilled in the art that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive.

The scope of the present invention is defined by the appended claims rather than the detailed description and all changes or modifications derived from the meaning and scope of the claims and their equivalents are to be construed as being included within the scope of the present invention do.

100: display panel 200: test image supply unit
300: No candidate candidate region detection unit 400:
410: image feature extraction unit 420: weight application unit
430: quantification unit 440:

Claims (10)

Detecting image candidate regions by analyzing image information obtained from an image displayed on a display panel;
Extracting features of the candidate regions;
Loading a plurality of weights for each feature of an image;
Calculating a quantization value of the candidate regions by applying a weight to each feature of the candidate regions; And
And comparing the quantified value of the candidate candidate regions with a reference value to detect a mura. The method according to claim 1,
The features of the candidate candidate regions are:
Wherein the image includes a circularity, a size, a contrast, a density, a center point, a diagonal component, a horizontal dispersion, a vertical dispersion, and a horizontal and vertical dispersion ratio. The method according to claim 1,
Wherein a quantization value of each of the candidate candidate regions is calculated by applying a weight to each feature of the candidate candidate region. The method of claim 3,
Arranging a data value of each of the features of the candidate regions by a predetermined reference,
And weighting is applied to each of the plurality of display devices. The method according to claim 1,
In the detecting step,
Setting a ranking for the mura candidate regions based on the quantization value of the mura candidate regions,
And detects at least one deviation according to the set order. An image feature extraction unit for extracting features of candidate candidate regions obtained from an image displayed on a display panel;
A weight applying unit for loading a weight corresponding to a feature of the candidate candidate regions among a plurality of weighted values provided for each feature of the image;
A quantization unit for calculating a quantization value of the candidate candidate regions by applying a weight to each feature of the candidate candidate regions; And
And a non-uniformity determination unit for comparing the quantized values of the non-uniformity candidate areas with a reference value to detect the unevenness. The method according to claim 6,
The features of the candidate regions are:
Contrast, density, center point, diagonal component, horizontal dispersion, vertical dispersion, and horizontal / vertical dispersion ratio,
Wherein a plurality of weights for each feature of the image are stored in a memory. The method according to claim 6,
The quantification unit may include:
Wherein a quantization value of each of the candidate candidate areas is calculated by applying a weight for each feature of the candidate candidate area. The method according to claim 6,
The above-
Setting a ranking for the mura candidate regions based on the quantization value of the mura candidate regions,
And detects at least one deviation according to a predetermined order. The method according to claim 6,
A test image supply unit for supplying a test image to the display panel; And
Further comprising a candidate candidate area detector for obtaining image information from the image displayed on the display panel and analyzing the obtained image information to detect candidate candidate areas.
KR1020120131768A 2012-11-20 2012-11-20 Apparatus and Method for Mura Defect Detection of Display Device KR102020596B1 (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2019215336A (en) * 2018-05-24 2019-12-19 キーサイト テクノロジーズ， インク． Unevenness detection in master panel of flat panel display during manufacturing
CN112233633A (en) * 2020-10-28 2021-01-15 福州京东方光电科技有限公司 Brightness compensation method, device, equipment and readable storage medium

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Publication number Priority date Publication date Assignee Title
KR20050104855A (en) * 2004-04-30 2005-11-03 삼성전자주식회사 Inspecting apparatus and method of inspecting the display panel
JP2008170325A (en) * 2007-01-12 2008-07-24 Seiko Epson Corp Stain flaw detection method and stain flaw detection device
KR20100033476A (en) * 2007-04-18 2010-03-30 마이크로닉 레이저 시스템즈 에이비 Method and apparatus for mura detection and metrology

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20050104855A (en) * 2004-04-30 2005-11-03 삼성전자주식회사 Inspecting apparatus and method of inspecting the display panel
JP2008170325A (en) * 2007-01-12 2008-07-24 Seiko Epson Corp Stain flaw detection method and stain flaw detection device
KR20100033476A (en) * 2007-04-18 2010-03-30 마이크로닉 레이저 시스템즈 에이비 Method and apparatus for mura detection and metrology

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2019215336A (en) * 2018-05-24 2019-12-19 キーサイト テクノロジーズ， インク． Unevenness detection in master panel of flat panel display during manufacturing
CN112233633A (en) * 2020-10-28 2021-01-15 福州京东方光电科技有限公司 Brightness compensation method, device, equipment and readable storage medium
CN112233633B (en) * 2020-10-28 2022-04-15 福州京东方光电科技有限公司 Brightness compensation method, device, equipment and readable storage medium

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