KR101681947B1 - Device and method for classifying defects in tft backplane - Google Patents

Abstract

The present invention relates to a method for classifying defects in a TFT backplane. According to an embodiment of the present invention, the method for classifying defects in a TFT backplane comprises: a step of setting an ROI by obtaining a color image by a selective reflection characteristic for each wavelength in the TFT backplane when a light source is irradiated and generating each ROI mask; a step of performing a period comparison test based on periodicity which a natural pattern of the TFT backplane has; a step of mapping a defect image detected through the period comparison test on each of the ROI masks; a step of determining an ROI region to which the detected defect belongs; and a step of comparing a color characteristic of the ROI region to which the defect belongs with a color attribute of the defect location and classifying types of defects by analyzing results of size measurements of defects.

Description

TECHNICAL FIELD [0001] The present invention relates to a defect classification method and apparatus for a TFT substrate,

The present invention relates to a defect classification method and apparatus for a TFT substrate, and more particularly, to a defect classification method and apparatus for a defect detection method using a color image by a selective reflection characteristic for each wavelength when scanning a light source on a TFT substrate, And more particularly, to a method and apparatus for increasing the accuracy of defect classification in an inspection process by automatically detecting a defect type by mapping a defect portion to each mask.

Active-matrix organic light-emitting diode (AMOLED) displays use a current driven scheme, while active matrix liquid-crystal displays (AMLCDs) use a voltage driven scheme. The AMOLED display consists of a thin-film transistor (TFT) backplane, organic light-emitting diodes (OLEDs), and an encapsulating fixture. The pixel circuit, a major part of the TFT backplane, provides digital switching and stable current. The main role of OLEDs is to emit light by the current supplied by the pixel circuit. The main characteristic of the OLEDs is determined by the intensity of the current supplied through the driving TFT. Therefore, the electrical characteristics of the TFT circuit and the uniformity of the TFT element are very important factors for AMOLED displays. Therefore, Thin-Film Transistor (TFT) inspection is very important in the production of AMOLED displays.

TFT backplane inspection is an optical test using an array test and an optical system. However, the array test is not used because of the problem of precise positioning of inspection time and defects.

Optical inspection is mainly used to detect fine defects which have major influence on image quality. It is used in most production lines due to the effect of preliminary inspections performed before in-

The micro-defect of the TFT circuit is to photograph a pixel circuit area by using a camera of high resolution and to detect a defect existing in various patterns of the pixel circuit.

Initially, the structure of the TFT-LCD was regularly arranged in a lattice pattern, so that only the texture component having a repetitive pattern was removed, and a method of extracting the defect from the image was used. However, in recent years, the pattern structure is very complicated, so that various complex patterns can not be removed by this method.

Therefore, in recent years, the period (pitch) of a pattern is found from an image acquired from a TFT-LCD substrate, the pattern is matched with a predetermined period (design value) and corrected, (Periodic comparison inspection) is performed by obtaining an image from which each pattern is removed by using the difference between the peripheral image and the pitch image around the defect, and the detected image is classified into ROIs (region of interest) to classify types of defects.

Defects are caused by various causes in the manufacturing process, and there are many types of defects in the process. Defects are largely divided into killer defects and nonkiller defects. A fatal defect is a fatal one that causes a circuit anomaly, such as a loss of a specific part, a flow of material and aggregation with surrounding lines, and a non-fatal defect means that it can simply be cleaned with dirt, or removed with a simple aftertreatment. Therefore, the purpose of defect inspection is to extract defective areas and to extract only fatal defects through information on this area.

It is very important to determine which region (ROI) of a defect belongs to a defective position in order to classify a defect after locating a defect by this periodical comparison inspection method. However, it is very difficult to automatically distinguish each ROI using a program by using a monochrome image as shown in Fig.

The TFT backplane, which is filtered in the inspection process and has a fatal defect, is sent to the repair process for defect repair together with the defect position information.

In the repair process, defects detected in the inspection process are classified into open, remain, short, and particle as shown in FIG. 1, and open (chemical vapor Deposition process. For shorts, use a laser to cut unnecessarily connected circuitry, and remove the remnants or non-cleaned particles with a laser.

Currently, the operator resides for repair, and the defects detected in the inspection process are manually classified to determine the repair method and repair is performed. Because of this, much of the time spent on expensive repair equipment is used for manual inspection for defect classification and classification accuracy is dependent on operator skill, which is costly and inefficient in terms of productivity.

Therefore, there is a need for a method and apparatus capable of automatically classifying defects detected in the inspection process with high accuracy.

Accordingly, an object of the present invention is to solve the problems of the prior art as described above, and it is an object of the present invention to provide a method of setting ROI by using a color image by selective reflection characteristic for each wavelength when scanning a light source on a TFT substrate, And a method and apparatus for increasing the accuracy of defect classification in the inspection process by automatically detecting the type of defects by inspecting the detailed position, color, and size of the defective area by mapping the defective part to each ROI mask .

According to another aspect of the present invention, there is provided a defect classification method for a TFT substrate, comprising: obtaining a color image by a selective reflection characteristic for each wavelength of a TFT substrate upon irradiation of a light source to set an ROI, Performing period comparison inspection based on periodicity of a unique pattern of the TFT substrate; mapping the detected defect image to each ROI mask through the period comparison inspection; Determining a ROI region to which the defect belongs, comparing color characteristics of the ROI region with the color attribute of the defect image, and classifying the defect type by analyzing a result of measuring the size of the defect .

In addition, a defect classification apparatus for a TFT substrate according to an embodiment of the present invention includes a light source section for irradiating light to a TFT substrate, and a color image acquiring section for acquiring a color image by selective reflection characteristics of wavelengths of the TFT substrate upon irradiation of the light source by the light source section, An inspection unit for performing cycle comparison inspection on the basis of the periodicity of the inherent pattern of the TFT substrate; and an inspection unit for detecting the defect image, An ROI region determination unit for determining an ROI region to which the detected defect belongs; and a ROI region determination unit for determining a color property of the ROI region to which the defect belongs and a color property of the defect position And a defect classifying section for classifying a defect type according to the color attribute comparison and the size analysis of the defect do.

According to the present invention, after determining the position of a defect by the periodical comparison inspection method, in determining the defect location belonging to a region (ROI) of the pixel for defect classification, the color image And the ROI mask is mapped to the defect image to determine the ROI area to which the defect position belongs. Further, defect types can be automatically and precisely classified by comparing the color characteristics of the ROI region with the color attributes of the defect image, measuring the size of the defect, and analyzing the detection positions of the defects comprehensively.

Therefore, while the operator has been resident and the defects detected in the inspection process have to be manually classified and the types of defects can not be classified quickly, according to the present invention, by using the ROI mask, A comprehensive analysis of size, color and detection location allows automatic classification of defect types.

Fig. 1 is a conceptual diagram showing the types of defects to be detected in the TFT backplane inspection.
2 is a diagram showing a monochrome image of a channel portion of a TFT pixel.
3 is a block diagram illustrating a specific module configuration according to an embodiment of the present invention.
4 is a diagram and a graph showing the interference phenomenon of light in the thin film and the selective reflection characteristic according to wavelength.
5 and 6 illustrate photographs showing that each ROI can be distinguished by a color image as an actual AMOLED TFT image.
FIG. 7 shows an example of each ROI mask classified into five ROIs for one pixel of a TFT substrate according to an embodiment of the present invention.
8 to 12 are diagrams illustrating an example of classifying types of defects using ROI classification and ROI-specific masks according to an embodiment of the present invention.
13 is a flowchart for explaining an embodiment for detecting and classifying TFT defects.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

In the following description of the embodiments of the present invention, descriptions of techniques which are well known in the technical field of the present invention and are not directly related to the present invention will be omitted. In addition, detailed description of components having substantially the same configuration and function will be omitted.

For the same reason, some of the elements in the accompanying drawings are exaggerated, omitted, or schematically shown, and the size of each element does not entirely reflect the actual size. Accordingly, the present invention is not limited by the relative size or spacing depicted in the accompanying drawings.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of a defect classification method and apparatus for a TFT substrate according to the present invention will be described with reference to the drawings.

3 is a block diagram illustrating a specific module configuration according to a preferred embodiment of the present invention.

3, the defect classification apparatus for a TFT substrate according to the present invention includes a light source 110, an ROI mask generator 130, an inspection unit 150, a mapping unit 170, an ROI region determination unit 190, A comparison unit 210 and a defect classification unit 230. [

The light source unit 110 irradiates the TFT substrate with a light source to detect defects of the TFT substrate, thereby obtaining a color image by a selective reflection characteristic of each wavelength of the TFT substrate. This will be described in detail with reference to FIG.

4 is a diagram and a graph showing the interference phenomenon of light in the thin film and the selective reflection characteristic according to wavelength.

As shown in FIG. 4, when the light from the light source is incident on the thin film through the optical system, a part of the light is reflected on the surface (Reflected light ray 1) and a part is reflected on the interface between the thin film and the substrate ray 2). Since these reflected waves are coherent light emitted from the same light source, they exhibit interference with each other, which causes reinforcement and destructive interference according to wavelengths. Therefore, it seems that the reflectance differs depending on the wavelength.

The reflectance R for each wavelength in multiple thin films is summarized as follows.

R (?) = F (n (?), K (?), T)

Where n is the refractive index, k is the extinction coefficient, and t is the thickness.

That is, the measured reflected light reflects the reflectance distribution according to the wavelength depending on the thickness of the thin film and the physical properties of the thin film, and reflects light having a color corresponding to the reflectance distribution even at the input of white light. Most of the dielectric thin films and polymers that are the materials through which light is transmitted can distinguish colors using optical interference.

As described above, according to the present invention, a ROI mask is generated by irradiating a light source to a TFT substrate including a pattern, and reflecting light having different colors by a reflectance distribution according to the thickness of the thin film and physical properties of the thin film, Can be used to detect TFT defects.

5 and 6 illustrate photographs showing that each ROI can be distinguished by a color image as an actual AMOLED TFT image. FIG. 5 shows an ROI image of a bottom-gate of a TFT, in which source, drain, gate, contact, and substrate regions are distinguished by color. Figure 6 shows an active matrix backplane array formed on a flexible plastic substrate. A represents a size of 2 mu m, B represents a thickness of an insulating material between inner-layer circuit layers, and is a size of +/- 5 mu m.

For reference, an ROI generally refers to a region of interest among different regions. In the present invention, ROI can be used as a region of interest in which a metal, a substrate, a source, and a drain in a pixel (or a plurality of pixels) are separately classified.

The ROI mask generation unit 130 obtains a color image by the selective reflection characteristic of each wavelength of the TFT substrate when irradiating the light source by the light source unit 110, thereby separating the ROIs and generating an ROI mask. That is, a mask of an ROI for each region is generated by using an image of a defect-free portion (normal).

In the case of a TFT, the ROI can be divided into regions such as a source, a drain, a gate, a channel, and a capacitor in order to determine an area to which a detected defect belongs. As shown in FIGS. 5 and 6, since the color coordinates (red, green, blue) of the respective regions are different from each other, the desired ROIs can be separated by determining the color coordinate range of the corresponding region. How many ROIs are grouped depends on how the ROI classification of the color coordinate range is performed.

Fig. 7 shows an example of each ROI-specific mask classified into five ROIs for one pixel of the TFT substrate. Referring to FIG. 7, the inspection ROI by color coordinate grouping of the acquired color image can be identified and the generated ROI-specific mask can be confirmed. (a) shows one pixel of the TFT array, (b) shows a substrate ROI mask, (c) shows an active ROI mask d, gate 1 (Gate1) 2 (Gate 2) ROI mask, and (f) a Data ROI mask.

As shown in FIG. 7, in the ROI mask, the image of the base or bottom surface is shown as white for the image (a) and the rest of the area is shown as black, and the image of the active area is shown as white, (D) shows that the image of gate 1 is white and the rest of the area is black, (e) the image of gate 2 is white and the rest of the area is black, and (f) The image is shown in white and the rest in black.

The types of defects can be determined by mapping the ROI masks (a) to (f) processed as described above to the defective part.

The inspection unit 150 performs the periodical comparison inspection based on the periodicity of the inherent pattern of the TFT substrate.

On the other hand, in the periodical comparison inspection, it is necessary to accurately align the positions of all the repeated patterns, so that it takes a long time to correct the rotation and the movement when the image is picked up. Also, the image may be incorrectly extracted at the periphery of the lens due to distortion of the lens. In order to compensate for this, a pitch (repetition period) is obtained through feature matching from the input image, and these sizes are reset to an appropriate size. This will convert each pitch area to a specified pitch size, which will allow for quick and easy correction due to rotation, projection, and camera distortion. A typical transformation used here is a projective transformation.

The mapping unit 170 maps the defect image detected through the period comparison check to the ROI mask. That is, in order to determine which ROI the detected defect corresponds to, it is possible to map the position of the defect to each mask.

The ROI area determining unit 190 determines an ROI area to which the detected defect belongs. The detailed position in the pixel of the defect can be determined according to which ROI region the detected defect is detected. This will be described below with reference to FIG.

The color property comparison unit 210 compares the color property of the ROI region to which the defect belongs with the color property of the defect position.

The defect classifying unit 230 classifies types of defects according to the ROI region in which the defects are detected, the color property comparison result, and the size of the defects.

An algorithm can be used that programmatically implements a heuristic method that is applied when an operator manually classifies defects. That is, if the operator manually programmes the logic to determine open, resume, shot, and particle, it becomes a classification algorithm. In the case of open, main, and short fatal defects, these must be precisely defined. In the case of particles, only those that affect the next process are selected.

Specifically, the defect classifier 230 determines that the defect is located on the metal line (Gate.Data), the metal is not formed due to the defect, and the color property of the defect is the same as the substrate The defect is classified as open.

For example, as shown in FIG. 8, the defective portion is found by the periodical comparison inspection method (refer to the upper right image defect position), and the defect position and size are directly applied to each ROI mask (ROI mask 1, (ROI (Active) mask, ROI (Gate1) mask, ROI (Gate2) mask, ROI (Data mask), and ROI (Data mask)

7, the first ROI (substrate) mask is obtained by masking the base or bottom surface with white (the rest is displayed in black), and the second ROI (active) mask is masked with the active region (Gate 1) is treated as white and gate 1 is treated as white and the rest is treated as black. The ROI (Gate 2) mask of No. 4 treats gate 2 as white And the remainder is processed in black. In ROI (Data) mask 5, the data area is processed in white and the rest is processed in black.

It is natural that the above white and black can be processed in different colors, respectively.

The upper left image of FIG. 8 represents one pixel, including the defect detected in the period comparison, and the upper right image is an image representing the position of the detected defect in the pixel.

That is, if the defect position (upper left) is mapped to the five ROI marks as shown in FIG. 8, the white part indicating the defect position is completely included in the white part of the mask No. 4, .

Therefore, since the defect belongs to the fourth ROI region, the defect position is on the Gate2 region (metal line), and the color of the defect region coincides with the color of the base region (see the upper left color image in FIG. 8) Is not formed and the base is visible, the type of the defect is classified as 'open' corresponding to the metal line being opened.

Otherwise, if the position of the detected defect belongs to the substrate and does not contact the metal line, and if the color attribute of the defect is judged to be the same category as the metal, if the size of the defect is larger than a predetermined size , And remained. Here, the predetermined size of the defect refers to a size larger than a certain size in which a defect determined by the designer in advance by experience and theory can affect the performance.

For example, as shown in FIG. 9, a defective area (see the upper right side of FIG. 9) is mapped to each of five ROI masks. As a result, defects overlap the white of the ROI mask 1, It belongs to Marks.

Therefore, the defect belongs to the ROI region No. 1, the defect position is on the bottom surface, and the color of the defective portion belongs to the category of metal (see upper left color image) It does not overlap with the mast No. 5). That is, the defective portion is a metal formed at a position where a metal should not be formed, but does not cause a short circuit due to contact with another metal, so that the type of the defect is classified as a reset.

In another case, the position of the detected defect belongs to the substrate, is in contact with the metal line, and is classified as a short when the size of the defect is larger than a predetermined size.

For example, as shown in FIG. 10, when a defect is mapped to each of five ROI masks, a metal line (Data area) in which the defect belongs to the first ROI (substrate) area and the defect location is the fifth mask, And the color of the defective portion belongs to the category of metal, so it is classified as a short. That is, the defects are detected at positions where the defects are adhered to the metal line, and the defects are classified as short because the color of the defects is measured with metal and the size is more than a certain level.

In other cases, if the position of the detected defect belongs to a substrate, the color property of the defect is different from the bottom or metal, the defect size is larger than a predetermined size, If they are not separated by at least one, they are classified as particles. In other words, as shown in FIGS. 11 and 12, a defect of a color different from that of a bottom surface or a metal at the connection portion of the active region and the gate, and may be classified as a particle when the defect condition is not open, main, or short.

For example, as shown in FIG. 11, when defects are mapped to five ROI masks, defects mainly belong to ROI 1 and ROI (data) 5, and defects are colored Since it differs from the surface or metal, it is classified as particles.

FIG. 11 shows a case where the particle size is large, and FIG. 12 shows a case where the particle size is small.

13 is a flowchart for explaining an embodiment for detecting and classifying TFT defects.

Referring to FIG. 13, a step S310 of obtaining a color image by selective reflection characteristics of wavelengths of a TFT substrate at the time of irradiating a light source to distinguish ROIs and generating ROI masks is performed. That is, a mask of an ROI for each region is generated by using an image of a defect-free portion (normal).

The reflected light measured by the light source irradiation reflects the reflectance distribution according to the wavelength depending on the thickness of the thin film and the physical properties of the thin film, and reflects the light having the color corresponding to the reflectance distribution even at the white light input. Most of the dielectric thin films and polymers that are the materials through which light is transmitted can distinguish colors using optical interference.

As described above, according to the present invention, a ROI mask is generated by irradiating a light source to a TFT substrate including a pattern, and reflecting light having different colors by a reflectance distribution according to the thickness of the thin film and physical properties of the thin film, Can be used to detect TFT defects.

Next, a step S320 of performing a periodical comparison inspection based on the periodicity of the inherent pattern of the TFT substrate is performed.

On the other hand, in the periodical comparison inspection, it is necessary to accurately align the positions of all the repeated patterns, so that it takes a long time to correct the rotation and the movement when the image is picked up. Also, the image may be incorrectly extracted at the periphery of the lens due to distortion of the lens. In order to compensate for this, the pitch is obtained from feature matching from the input image, and these sizes are reset to an appropriate size. This will convert each pitch area to a specified pitch size, which will allow for quick and easy correction due to rotation, projection, and camera distortion. A typical transformation used here is a projective transformation.

And mapping the detected defect image to the respective ROI masks through the periodical comparison inspection (S330). That is, in order to determine which ROI the detected defect corresponds to, it is possible to map the position of the defect to each mask.

Next, a step S340 of determining a sub-pixel in the pixel corresponding to the matching mask as a result of mapping the position of the detected defect to the ROI mark (S340).

Then, the color characteristic of the ROI region to which the defect belongs is compared with the color attribute of the defect position, and the size of the defect is measured (S350)

Next, the kind of the defect can be classified (S360) by using the position of the detected defect in the pixel, the color characteristic, and the size of the defect. At this time, it is possible to use an algorithm that is implemented by programming a heuristic method applied when an operator manually classifies defects.

The classification of the defect types can be classified by applying the above-mentioned matters.

According to the present invention, after determining the position of a defect by the periodical comparison inspection method, in determining the defect location belonging to a region (ROI) of the pixel for defect classification, the color image And the ROI mask is mapped to the defect image to determine the ROI area to which the defect position belongs. In addition, defect types can be automatically and precisely classified by comparing the color characteristics of the ROI region with the color attributes of the defect positions, measuring the size of the defects, and comprehensively analyzing the positions of the defects in the pixels.

Therefore, while the operator has been resident and the defects detected in the inspection process have to be manually classified and the types of defects can not be classified quickly, according to the present invention, by using the ROI mask, A comprehensive analysis of size, color and detection location allows automatic classification of defect types.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. , And are not intended to limit the scope of the present invention. It will be apparent to those skilled in the art that other modifications based on the technical idea of the present invention may be practiced without departing from the invention as set forth herein.

110: light source unit 130: ROI mask generation unit
150: Inspection unit 170:
190: ROI area determining unit 210: Color property comparing unit
230: Defect classification section

Claims (10)

The color image is obtained by reflecting the light of different colors when the light source is irradiated according to the thin film thickness and thin film properties of the TFT substrate, and the ROI for the inspection region is set. Generating an ROI mask using a phenomenon of reflecting light having different colors by a reflectance distribution according to a wavelength of a thin film physical property;
Performing a periodical comparison inspection based on a periodicity of an inherent pattern of the TFT substrate;
Mapping the detected defect image to each ROI mask through the period comparison check;
Determining an ROI region to which the detected defect belongs; And
Comparing the color characteristic of the ROI region with the color attribute of the defect position and classifying the defect type by analyzing the result of measuring the size of the defect,
The method of claim 1,
Classifying the defect as open when the position of the detected defect is on the metal line and the color attribute of the defect is judged to be in the same category as the substrate because no metal is formed due to the defect ;Wow
Wherein the position of the detected defect belongs to the substrate and does not contact the metal line, the color attribute of the defect is judged to be in the same category as the metal, and if the defect size is larger than a predetermined size, and a classification step
Classifying the defect into a short when the position of the detected defect belongs to a substrate and is in contact with a metal line and the size of the defect is larger than a predetermined size;
Wherein the position of the detected defect belongs to the substrate and the color attribute of the defect is different from the bottom face or the metal and the defect size is greater than a predetermined size and is divided into at least one of the open, Classifying the particles into particles; Wherein the defect classification method comprises the steps of: The method of claim 1, wherein classifying the defect type comprises:
Wherein the heuristic method is implemented by an algorithm that is implemented by programming a heuristic method applied when an operator manually classifies defects. delete delete delete delete The method according to claim 1,
Further comprising correcting rotation, projection, and camera lens distortion during the periodical comparison inspection. 8. The method of claim 7,
Wherein a projective transformation is used for the correction. delete delete

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