Substrate inspection apparatus and method of inspecting substrate

Abstract

A substrate inspection apparatus and a method of inspecting a substrate are provided. The substrate inspection apparatus includes: an image sensor that captures an image of a substrate to generate base image data; and a combiner that divides the base image data into first image data including a first stain region and a first non-stain region and second image data including a second stain region and a second non-stain region, and merge the first image data and the second image data to generate merge image data including a merge stain region and a merge non-stain region, the merge stain region representing the first stain region and the second stain region, the merge non-stain region representing the first non-stain region and the second non-stain region.

Description

Technical Field

[0001] The embodiments relate to a substrate inspection apparatus and a method of inspecting a substrate. Background Art

[0002] During the manufacturing process of a display device, unwanted stains may form on a substrate. Some of the stains may form at the same positions on different substrates. The stains formed at the same positions on multiple substrates may be caused by problems in manufacturing facilities, process conditions, etc.

[0003] Such stains on the manufactured substrate can be detected by visual inspection through an automated facility. Human judgment may be involved in the substrate inspection process to avoid the quality control standard from becoming too strict because only some of the stains detected by the automated facility are visually recognized by the users of the display device, making some stains acceptable. Summary of the Invention

[0004] Embodiments provide a substrate inspection apparatus having improved accuracy and reliability.

[0005] Embodiments provide a method of inspecting a substrate having improved accuracy and reliability.

[0006] A substrate inspection apparatus according to an embodiment of the present disclosure includes: an image sensor that captures an image of a substrate to generate basic image data; and a combiner that divides the basic image data into first image data including a first stain region and a first stain-free region and second image data including a second stain region and a second stain-free region, and combines the first image data and the second image data to generate combined image data including a combined stain region and a combined stain-free region, the combined stain region representing the first stain region and the second stain region, and the combined stain-free region representing the first stain-free region and the second stain-free region.

[0007] In an embodiment, the combiner may determine whether the substrate is defective based on the combined image data.

[0008] In an embodiment, the combiner may generate the combined image data by normalizing the sum of the brightness of the first stain region and the brightness of the second stain region to calculate a first normal value and normalizing the sum of the brightness of the first stain-free region and the brightness of the second stain-free region to calculate a second normal value.

[0009] In an embodiment, the combiner may calculate the first normal value by subtracting a predetermined value from the sum of the brightness of the first stain region and the brightness of the second stain region, and calculate the second normal value by subtracting the predetermined value from the sum of the brightness of the first stain-free region and the brightness of the second stain-free region.

[0010] In an embodiment, the merged stain region may have the same brightness as a first normal value, and the merged stain - free region may have the same brightness as a second normal value.

[0011] In an embodiment, an image sensor may include: an imager that captures an image of a substrate to generate a plurality of captured image data of the substrate; an inspector that determines whether the substrate has a defect based on each of the plurality of captured image data; and a generator that generates base image data based on the plurality of captured image data.

[0012] A method for inspecting a substrate according to an embodiment of the present disclosure includes: generating base image data by capturing an image of the substrate; dividing the base image data into first image data including a first stain region and a first stain - free region and second image data including a second stain region and a second stain - free region; and merging the first image data and the second image data to generate merged image data including a merged stain region and a merged stain - free region, where the merged stain region represents the first stain region and the second stain region, and the merged stain - free region represents the first stain - free region and the second stain - free region.

[0013] In an embodiment, the method may further include: determining whether the substrate has a defect based on the merged image data.

[0014] In an embodiment, generating the merged image data may include: calculating a first normal value by normalizing the sum of the brightness of the first stain region and the brightness of the second stain region; and calculating a second normal value by normalizing the sum of the brightness of the first stain - free region and the brightness of the second stain - free region.

[0015] In an embodiment, calculating the first normal value and the second normal value may include: subtracting a predetermined value from the sum of the brightness of the first stain region and the brightness of the second stain region; and subtracting a predetermined value from the sum of the brightness of the first stain - free region and the brightness of the second stain - free region.

[0016] In an embodiment, the merged stain region may have the same brightness as a first normal value, and the merged stain - free region may have the same brightness as a second normal value.

[0017] In an embodiment, generating base image data by capturing an image of the substrate may include: generating first base image data by capturing an image of a first substrate; and generating second base image data by capturing an image of a second substrate.

[0018] In an embodiment, dividing the base image data into first image data and second image data may include: dividing the first base image data into first first image data including a first first stain region and first second image data including a first second stain region, where the first second stain region has the same position in the first second image data as the first first stain region has in the first first image data; and dividing the second base image data into second first image data including a second first stain region and second second image data including a second second stain region, where the second second stain region has the same position in the second second image data as the second first stain region has in the second first image data.

[0019] In an embodiment, generating merged image data by merging the first image data and the second image data may include: generating first merged image data including a first merged stain region representing the first first stain region and the first second stain region by merging the first first image data and the first second image data; and generating second merged image data including a second merged stain region representing the second first stain region and the second second stain region by merging the second first image data and the second second image data.

[0020] In an embodiment, generating merged image data by merging the first image data and the second image data may further include: generating merged image data including a merged stain region representing the first merged stain region and the second merged stain region by merging the first merged image data and the second merged image data.

[0021] In an embodiment, generating base image data by capturing an image of a substrate may include: generating first base image data including a first first stain region and a first second stain region by capturing an image of a first substrate; and generating second base image data including a second first stain region and a second second stain region by capturing an image of a second substrate, where the second first stain region has the same position in the second base image data as the first first stain region has in the first base image data, and the second second stain region has the same position in the second base image data as the first second stain region has in the first base image data.

[0022] In an embodiment, generating base image data by capturing an image of a substrate may further include: generating base image data including a first stain region representing the first first stain region and the second first stain region and a second stain region representing the first second stain region and the second second stain region by merging the first base image data and the second base image data.

[0023] In an embodiment, generating the basic image data may include: generating a plurality of captured image data of a plurality of exposure regions of a substrate; and generating the basic image data from the plurality of captured image data.

[0024] In an embodiment, generating the basic image data may further include: determining whether the substrate is defective based on each of the plurality of captured image data.

[0025] In an embodiment, the first image data may include first information regarding a first stained region and a first non-stained region, the second image data may include second information regarding a second stained region and a second non-stained region, the first information may include the position, size, shape, brightness of the first stained region, the position, size, shape, and brightness of the first non-stained region, and the second information may include the position, size, shape, brightness of the second stained region, the position, size, shape, and brightness of the second non-stained region.

[0026] In a substrate inspection apparatus and a method of inspecting a substrate according to an embodiment of the present disclosure, the basic image data of the substrate may be divided into a plurality of image data, and the divided image data may be combined to generate combined image data. Based on the combined image data, the detection degree of stains formed at fixed positions on one or more substrates repeatedly for the same reason can be improved. Therefore, since it is possible to more easily determine whether the substrate is defective, the reliability of the manufacturing process can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a block diagram schematically illustrating a substrate inspection apparatus according to an embodiment of the present disclosure.

[0028] Figure 2 is a flowchart illustrating a method of inspecting a substrate according to an embodiment of the present disclosure.

[0029] Figure 3 、 Figure 4 and Figure 5 is an illustration Figure 2 of an example of a method of inspecting a substrate.

[0030] Figure 6 、 Figure 7 、 Figure 8 and Figure 9 is an illustration Figure 2 of another example of a method of inspecting a substrate.

[0031] Figure 10 andFigure 11 It is a diagram Figure 2 of another example of a method for inspecting a substrate. Detailed implementation manners

[0032] Hereinafter, embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. In the drawings, the same reference numerals are used for the same components, and redundant descriptions of the same components will be omitted.

[0033] Figure 1 It is a block diagram schematically illustrating a substrate inspection apparatus according to an embodiment of the present disclosure.

[0034] Referring to Figure 1 , the substrate inspection apparatus 10 may include a worktable ST, an image sensor 100, and a combiner 200.

[0035] The substrate inspection apparatus 10 may be used in a manufacturing process of a display device. The substrate inspection apparatus 10 may determine a state of a substrate SUB included in the display device during the manufacturing process of the display device. For example, the substrate inspection apparatus 10 may determine whether the substrate SUB is defective by inspecting stains on the substrate SUB during the manufacturing process of the display device. In some embodiments, the substrate inspection apparatus 10 may be a separate and independent device, but the present disclosure is not limited thereto. In some embodiments, the substrate inspection apparatus 10 may be arranged in a device for a manufacturing process of a display device, such as an exposure device, a coating device, a cutting device, a cleaning device, etc.

[0036] The substrate SUB may include a transparent material or an opaque material. Examples of materials that may be used as the substrate SUB may include polyimide, quartz, glass, etc. These materials may be used alone or in combination with each other.

[0037] In an embodiment, the substrate SUB may refer to a display device being manufactured. The substrate SUB may also include at least one layer included in the display device. For example, the substrate SUB may also include at least one of an inorganic layer, an organic layer, and a metal layer included in the display device.

[0038] The worktable ST may be parallel to a plane defined by a first direction DR1 and a second direction DR2 intersecting the first direction DR1. For example, the second direction DR2 may be perpendicular to the first direction DR1. The worktable ST may support the substrate SUB.

[0039] The image sensor 100 may include an imager 110, an inspector 120, and a generator 130.

[0040] The imager 110 may be spaced apart from the workbench ST in a third direction DR3 that intersects each of the first direction DR1 and the second direction DR2. For example, the third direction DR3 may be perpendicular to each of the first direction DR1 and the second direction DR2. The imager 110 may capture (e.g., take) an image in a direction opposite to the third direction DR3. The imager 110 may capture an image of the substrate SUB. The imager 110 may generate captured image data including information about the appearance (such as shape, color, etc.) of the substrate SUB. For example, the imager 110 may include a camera module.

[0041] There may be multiple imagers 110. Each of the multiple imagers 110 may capture an image of a part of the substrate SUB, and thus may generate multiple captured image data of the substrate SUB. For example, the imagers 110 may be arranged along the first direction DR1, but the present disclosure is not limited thereto. For example, the substrate SUB may move on the workbench ST along the second direction DR2, and the imager 110 may repeatedly capture an image of the substrate SUB. Thus, the imager 110 may capture an image of the entire substrate SUB and may generate captured image data for the entire substrate SUB. The imager 110 may send the captured image data to the inspector 120.

[0042] The inspector 120 may inspect the captured image data received from the imager 110. The inspector 120 may determine whether the substrate SUB is defective through each of the multiple captured image data. In an embodiment, the inspector 120 may detect a stain area of the captured image data to determine whether the substrate SUB is defective. That is, the inspector 120 may detect a stain on the substrate SUB to determine whether the substrate SUB is defective.

[0043] The generator 130 may generate base image data corresponding to an image of the entire substrate SUB through the captured image data generated by the imager 110. The generator 130 may send the base image data to the combiner 200.

[0044] In an embodiment, the combiner 200 may divide the base image data to generate multiple image data. Each of the multiple image data may be an image of an area exposed with one mask during an exposure process in a manufacturing process of a display device. That is, the substrate SUB may include multiple exposure areas, and the multiple exposure areas may be sequentially exposed by moving the same mask or the position of the substrate SUB.

[0045] Since the multiple exposure regions of the substrate SUB can all be exposed using the same mask, the image data can be substantially similar to each other. For example, if there are foreign objects, defects, etc. in the mask used in the exposure process, each of the multiple image data may include a stain formed at the same position. However, the present disclosure is not limited thereto. Due to various reasons such as the equipment used in the manufacturing process of the display device, the conditions of the manufacturing process of the display device, etc., each of the multiple image data may include a stain formed at a fixed position for the same reason.

[0046] In an embodiment, the combiner 200 may combine multiple image data to generate combined image data.

[0047] In an embodiment, the combiner 200 may check the combined image data. The combiner 200 may determine whether the substrate SUB is defective based on the combined image data. In an embodiment, the combiner 200 may detect the combined stain region of the combined image data to determine whether the substrate SUB is defective. That is, the combiner 200 may detect the stain on the substrate SUB to determine whether the substrate SUB is defective.

[0048] In this case, since the combiner 200 can determine whether the substrate SUB is defective based on the combined image data, it is also possible to detect stains that are not detected in the uncombined image data (e.g., captured image data, image data, etc.). In other words, the detection degree of stains that fixedly appear at the same position on the substrate SUB can be improved by the combiner 200.

[0049] Although Figure 1 each of the imager 110, the checker 120, the generator 130, and the combiner 200 is illustrated as an independent part in the substrate inspection device 10, the present disclosure is not limited thereto. For example, the imager 110, the checker 120, the generator 130, and the combiner 200 may be implemented as one, two, or three independent parts.

[0050] Figure 2 is a flowchart illustrating a method for inspecting a substrate according to an embodiment of the present disclosure. Figure 3 、 Figure 4 and Figure 5 are diagrams illustrating Figure 2 examples of the method for inspecting a substrate.

[0051] Referring to Figure 2 、 Figure 3 、 Figure 4 and Figure 5 The method for inspecting a substrate (S10) described can be executed by the substrate inspection device 10 described with reference to Figure 1 In the following, redundant descriptions will be omitted or simplified.

[0052] Reference Figure 1 、 Figure 2 and Figure 3 In the method (S10) of inspecting a substrate, the image sensor 100 may generate base image data B_ID (S100) by capturing an image of the substrate SUB (e.g., by photographing the substrate SUB). For example, the imager 110 may generate captured image data by capturing an image of the substrate SUB, and the generator 130 may generate the base image data B_ID based on the captured image data. The base image data B_ID may be sent from the image sensor 100 to the combiner 200.

[0053] The base image data B_ID may include a plurality of image data. For example, the base image data B_ID may include first image data ID1, second image data ID2, third image data ID3, fourth image data ID4, fifth image data ID5, and sixth image data ID6.

[0054] The first image data ID1, the second image data ID2, the third image data ID3, the fourth image data ID4, the fifth image data ID5, and the sixth image data ID6 may respectively correspond to exposure regions in which the substrate SUB is exposed using a mask. For example, the exposure regions of the substrate SUB may be sequentially exposed as the first image data ID1, the second image data ID2, the third image data ID3, the fourth image data ID4, the fifth image data ID5, and the sixth image data ID6. However, the present disclosure is not limited thereto, and the order in which the exposure regions of the substrate SUB are exposed corresponding to the first image data ID1, the second image data ID2, the third image data ID3, the fourth image data ID4, the fifth image data ID5, and the sixth image data ID6 may be variously changed.

[0055] Reference Figure 1 、 Figure 2 、 Figure 3 and Figure 4 In the method (S10) of inspecting a substrate, the combiner 200 may divide the base image data B_ID into the first image data ID1, the second image data ID2, the third image data ID3, the fourth image data ID4, the fifth image data ID5, and the sixth image data ID6 based on the exposure regions and / or based on a stain pattern (S200). Although six image data are used to illustrate the concept, this is not a limitation of the present disclosure.

[0056] Each of the first image data ID1, the second image data ID2, the third image data ID3, the fourth image data ID4, the fifth image data ID5, and the sixth image data ID6 may include a stained area and a non-stained area. The stained area may be an area where a stain is located on the substrate SUB, and the non-stained area may be an area where the stain is not located on the substrate SUB.

[0057] For example, the first image data ID1 may include a first stained area SA1 and a first non-stained area NSA1, and the second image data ID2 may include a second stained area SA2 and a second non-stained area NSA2. The third image data ID3 may include a third stained area SA3 and a third non-stained area NSA3, and the fourth image data ID4 may include a fourth stained area SA4 and a fourth non-stained area NSA4. The fifth image data ID5 may include a fifth stained area SA5 and a fifth non-stained area NSA5, and the sixth image data ID6 may include a sixth stained area SA6 and a sixth non-stained area NSA6.

[0058] The first image data ID1 may include first information about the first stained area SA1 and the first non-stained area NSA1. For example, the first information may include the position, size, shape, and brightness of the first stained area SA1 and the position, size, shape, and brightness of the first non-stained area NSA1.

[0059] The second image data ID2 may include second information about the second stained area SA2 and the second non-stained area NSA2. For example, the second information may include the position, size, shape, and brightness of the second stained area SA2 and the position, size, shape, and brightness of the second non-stained area NSA2.

[0060] The third image data ID3 may include third information about the third stained area SA3 and the third non-stained area NSA3. For example, the third information may include the position, size, shape, and brightness of the third stained area SA3 and the position, size, shape, and brightness of the third non-stained area NSA3.

[0061] The fourth image data ID4 may include fourth information about the fourth stained area SA4 and the fourth non-stained area NSA4. For example, the fourth information may include the position, size, shape, and brightness of the fourth stained area SA4 and the position, size, shape, and brightness of the fourth non-stained area NSA4.

[0062] The fifth image data ID5 may include fifth information regarding a fifth stain area SA5 and a fifth non-stain area NSA5. For example, the fifth information may include the position, size, shape, and brightness of the fifth stain area SA5 and the position, size, shape, and brightness of the fifth non-stain area NSA5.

[0063] The sixth image data ID6 may include sixth information regarding a sixth stain area SA6 and a sixth non-stain area NSA6. For example, the sixth information may include the position, size, shape, and brightness of the sixth stain area SA6 and the position, size, shape, and brightness of the sixth non-stain area NSA6.

[0064] In an embodiment, the first stain area SA1, the second stain area SA2, the third stain area SA3, the fourth stain area SA4, the fifth stain area SA5, and the sixth stain area SA6 may generally be at the same positions within each of the first image data ID1, the second image data ID2, the third image data ID3, the fourth image data ID4, the fifth image data ID5, and the sixth image data ID6, respectively. The first non-stain area NSA1, the second non-stain area NSA2, the third non-stain area NSA3, the fourth non-stain area NSA4, the fifth non-stain area NSA5, and the sixth non-stain area NSA6 may generally refer to the same areas within each of the first image data ID1, the second image data ID2, the third image data ID3, the fourth image data ID4, the fifth image data ID5, and the sixth image data ID6, respectively.

[0065] For example, among the first image data ID1, second image data ID2, third image data ID3, fourth image data ID4, fifth image data ID5, and sixth image data ID6, the positions of the first stain area SA1, second stain area SA2, third stain area SA3, fourth stain area SA4, fifth stain area SA5, and sixth stain area SA6 can be substantially the same, and the positions of the first non-stain area NSA1, second non-stain area NSA2, third non-stain area NSA3, fourth non-stain area NSA4, fifth non-stain area NSA5, and sixth non-stain area NSA6 can be substantially the same. The sizes of the first stain area SA1, second stain area SA2, third stain area SA3, fourth stain area SA4, fifth stain area SA5, and sixth stain area SA6 can be substantially the same, and the sizes of the first non-stain area NSA1, second non-stain area NSA2, third non-stain area NSA3, fourth non-stain area NSA4, fifth non-stain area NSA5, and sixth non-stain area NSA6 can be substantially the same. The shapes of the first stain area SA1, second stain area SA2, third stain area SA3, fourth stain area SA4, fifth stain area SA5, and sixth stain area SA6 can be substantially the same, and the shapes of the first non-stain area NSA1, second non-stain area NSA2, third non-stain area NSA3, fourth non-stain area NSA4, fifth non-stain area NSA5, and sixth non-stain area NSA6 can be substantially the same.

[0066] However, the brightness of the first stain area SA1, second stain area SA2, third stain area SA3, fourth stain area SA4, fifth stain area SA5, and sixth stain area SA6 can be substantially different, and the brightness of the first non-stain area NSA1, second non-stain area NSA2, third non-stain area NSA3, fourth non-stain area NSA4, fifth non-stain area NSA5, and sixth non-stain area NSA6 can be substantially different.

[0067] Reference Figure 1 、 Figure 2 、 Figure 3 、 Figure 4 and Figure 5, in the method (S10) of inspecting a substrate, the combiner 200 may generate combined image data M_ID by combining first image data ID1, second image data ID2, third image data ID3, fourth image data ID4, fifth image data ID5, and sixth image data ID6 (S300). For example, the combined image data M_ID may be generated using first information, second information, third information, fourth information, fifth information, and sixth information respectively included in the first image data ID1, second image data ID2, third image data ID3, fourth image data ID4, fifth image data ID5, and sixth image data ID6.

[0068] The combined image data M_ID may include a combined stain area M_SA and a combined non-stain area M_NSA. The combined image data M_ID may include combined information about the combined stain area M_SA and the combined non-stain area M_NSA. For example, the combined information may include the position, size, shape, and brightness of the combined stain area M_SA and the position, size, shape, and brightness of the combined non-stain area M_NSA.

[0069] In an embodiment, the combined stain area M_SA may represent a first stain area SA1, a second stain area SA2, a third stain area SA3, a fourth stain area SA4, a fifth stain area SA5, and a sixth stain area SA6, and the combined non-stain area M_NSA may represent a first non-stain area NSA1, a second non-stain area NSA2, a third non-stain area NSA3, a fourth non-stain area NSA4, a fifth non-stain area NSA5, and a sixth non-stain area NSA6. "Represent" indicates the combined values of the positions, sizes, shapes, and brightnesses of the components.

[0070] For example, the position of the combined stain area M_SA in the combined image data M_ID may be substantially the same as the positions of the first stain area SA1, the second stain area SA2, the third stain area SA3, the fourth stain area SA4, the fifth stain area SA5, and the sixth stain area SA6 in the first image data ID1, second image data ID2, third image data ID3, fourth image data ID4, fifth image data ID5, and sixth image data ID6. The position of the combined non-stain area M_NSA in the combined image data M_ID may be substantially the same as the positions of the first non-stain area NSA1, the second non-stain area NSA2, the third non-stain area NSA3, the fourth non-stain area NSA4, the fifth non-stain area NSA5, and the sixth non-stain area NSA6 in the first image data ID1, second image data ID2, third image data ID3, fourth image data ID4, fifth image data ID5, and sixth image data ID6.

[0071] The size of the merged stain region M_SA can be substantially the same as the sizes of the first stain region SA1, the second stain region SA2, the third stain region SA3, the fourth stain region SA4, the fifth stain region SA5, and the sixth stain region SA6, and the size of the merged non-stain region M_NSA can be substantially the same as the sizes of the first non-stain region NSA1, the second non-stain region NSA2, the third non-stain region NSA3, the fourth non-stain region NSA4, the fifth non-stain region NSA5, and the sixth non-stain region NSA6. The shape of the merged stain region M_SA can be substantially the same as the shapes of the first stain region SA1, the second stain region SA2, the third stain region SA3, the fourth stain region SA4, the fifth stain region SA5, and the sixth stain region SA6, and the shape of the merged non-stain region M_NSA can be substantially the same as the shapes of the first non-stain region NSA1, the second non-stain region NSA2, the third non-stain region NSA3, the fourth non-stain region NSA4, the fifth non-stain region NSA5, and the sixth non-stain region NSA6.

[0072] However, the brightness of the merged stain region M_SA can be substantially different from the brightnesses of the first stain region SA1, the second stain region SA2, the third stain region SA3, the fourth stain region SA4, the fifth stain region SA5, and the sixth stain region SA6, and the brightness of the merged non-stain region M_NSA can be substantially different from the brightnesses of the first non-stain region NSA1, the second non-stain region NSA2, the third non-stain region NSA3, the fourth non-stain region NSA4, the fifth non-stain region NSA5, and the sixth non-stain region NSA6.

[0073] In an embodiment, the merger 200 can calculate a first normal value and a second normal value. The first normal value can be a value obtained by normalizing the sum of the brightnesses of each of the first stain region SA1, the second stain region SA2, the third stain region SA3, the fourth stain region SA4, the fifth stain region SA5, and the sixth stain region SA6, and the second normal value can be a value obtained by normalizing the sum of the brightnesses of each of the first non-stain region NSA1, the second non-stain region NSA2, the third non-stain region NSA3, the fourth non-stain region NSA4, the fifth non-stain region NSA5, and the sixth non-stain region NSA6.

[0074] In this case, the combiner 200 may calculate a first normal value and a second normal value such that the average of the first normal value and the second normal value may be equal to the average of the luminance of each of the first image data ID1, the second image data ID2, the third image data ID3, the fourth image data ID4, the fifth image data ID5, and the sixth image data ID6. For example, the combiner 200 may calculate the first normal value by subtracting a predetermined value (e.g., the average of the luminance of each of the first image data ID1, the second image data ID2, the third image data ID3, the fourth image data ID4, the fifth image data ID5, and the sixth image data ID6) from the sum of the luminance of each of the first stain area SA1, the second stain area SA2, the third stain area SA3, the fourth stain area SA4, the fifth stain area SA5, and the sixth stain area SA6. The combiner 200 may calculate the second normal value by subtracting the same predetermined value from the sum of the luminance of each of the first non-stain area NSA1, the second non-stain area NSA2, the third non-stain area NSA3, the fourth non-stain area NSA4, the fifth non-stain area NSA5, and the sixth non-stain area NSA6.

[0075] In an embodiment, the luminance of the combined stain area M_SA may be the same as the first normal value, and the luminance of the combined non-stain area M_NSA may be the same as the second normal value. Therefore, the difference between the luminance of the combined stain area M_SA and the luminance of the combined non-stain area M_NSA may be greater than all six differences between the luminance of each of the first stain area SA1, the second stain area SA2, the third stain area SA3, the fourth stain area SA4, the fifth stain area SA5, and the sixth stain area SA6 and the luminance of each of the first non-stain area NSA1, the second non-stain area NSA2, the third non-stain area NSA3, the fourth non-stain area NSA4, the fifth non-stain area NSA5, and the sixth non-stain area NSA6.

[0076] If each of the six differences between the luminance of the first stain area SA1, the second stain area SA2, the third stain area SA3, the fourth stain area SA4, the fifth stain area SA5, and the sixth stain area SA6 and the luminance of the first non-stain area NSA1, the second non-stain area NSA2, the third non-stain area NSA3, the fourth non-stain area NSA4, the fifth non-stain area NSA5, and the sixth non-stain area NSA6 is relatively small, the stain may not be detected by the first image data ID1, the second image data ID2, the third image data ID3, the fourth image data ID4, the fifth image data ID5, and the sixth image data ID6.

[0077] In an embodiment, since the difference in luminance between the merged stain area M_SA may be large compared to the difference in luminance between the luminance level of a single unmerged stain area and the luminance level of a single stain-free area, even if detection in each of the first image data ID1, the second image data ID2, the third image data ID3, the fourth image data ID4, the fifth image data ID5, and the sixth image data ID6 is avoided, stains formed at substantially the same position on the substrate SUB can be detected by the merged image data M_ID. Therefore, the detection of stains formed at fixed positions on the substrate SUB due to the same reason (e.g., foreign matter or defects in the mask, etc.) can be improved in accuracy.

[0078] In the method (S10) of inspecting a substrate, it is possible to determine whether the substrate SUB is defective (S400) based on the merged image data M_ID.

[0079] In an embodiment, the merger 200 can determine whether the substrate SUB is defective based on the merged image data M_ID.

[0080] In another embodiment, the merged image data M_ID can be provided to a user, and the user can determine whether the substrate SUB is defective based on the merged image data M_ID.

[0081] Although Figure 3 、 Figure 4 and Figure 5 illustrate that the base image data B_ID includes the first image data ID1, the second image data ID2, the third image data ID3, the fourth image data ID4, the fifth image data ID5, and the sixth image data ID6, the present disclosure is not limited thereto. For example, the base image data B_ID can include five or fewer or seven or more image data, and the number of image data can be variously changed according to the size of the substrate SUB, the mask used in the exposure process, the exposure ability, etc. In other words, the substrate SUB can include one or more exposure areas.

[0082] In the method (S10) for inspecting a substrate according to an embodiment of the present disclosure, the substrate inspection device 10 can generate a merged image data M_ID by merging the first image data ID1, the second image data ID2, the third image data ID3, the fourth image data ID4, the fifth image data ID5, and the sixth image data ID6 obtained by dividing the basic image data B_ID of the substrate SUB. Based on the merged image data M_ID, the detection of stains that are repeatedly formed at fixed positions on the substrate SUB due to the same reason (for example, foreign matter or defects in the mask, etc.) can be improved in accuracy. Therefore, since it can be more easily determined whether the substrate SUB is defective, the reliability of the manufacturing process can be improved.

[0083] Figure 6 , Figure 7 , Figure 8 and Figure 9 It is a graphic Figure 2 FIG. 1 is a diagram of another example of a method of inspecting a substrate.

[0084] For example, Figure 6 may correspond to the step (S100) of generating basic image data B_ID in the method (S10) of inspecting a substrate, Figure 7 may correspond to the step (S200) of dividing the basic image data B_ID in the method (S10) of inspecting a substrate, and Figure 8 and Figure 9 This may correspond to the step ( S300 ) of generating the merged image data M_ID in the method ( S10 ) of inspecting a substrate.

[0085] In the following, reference Figure 1 The substrate inspection apparatus 10 described and the reference Figure 2 , Figure 3 , Figure 4 and Figure 5 Any redundant description of the described method ( S10 ) will be omitted or simplified.

[0086] refer to Figure 1 , Figure 2 and Figure 6 , in the method of inspecting a substrate ( S10 ), the image sensor 100 may generate first basic image data B_ID1 by capturing an image of a first substrate SUB1 , and may generate second basic image data B_ID2 by capturing an image of a second substrate SUB2 ( S110 ).

[0087] In an embodiment, each of the first substrate SUB1 and the second substrate SUB2 may refer to a display device being manufactured. The first substrate SUB1 and the second substrate SUB2 may be display devices being manufactured by the same manufacturing process. Each of the first substrate SUB1 and the second substrate SUB2 may correspond to the Figure 1 substrate SUB.

[0088] For example, the imager 110 may generate first captured image data and second captured image data by capturing images of the first substrate SUB1 and the second substrate SUB2, respectively, and the generator 130 may generate first basic image data B_ID1 and second basic image data B_ID2 from the first captured image data and the second captured image data, respectively. Each of the first basic image data B_ID1 and the second basic image data B_ID2 may be sent from the image sensor 100 to the combiner 200.

[0089] Each of the first basic image data B_ID1 and the second basic image data B_ID2 may include a plurality of smaller image data. For example, the first basic image data B_ID1 may include first first image data ID1-1 (hereinafter, referred to as "1-1 image data"), first second image data ID2-1 (hereinafter, referred to as "2-1 image data"), first third image data ID3-1 (hereinafter, referred to as "3-1 image data"), first fourth image data ID4-1 (hereinafter, referred to as "4-1 image data"), first fifth image data ID5-1 (hereinafter, referred to as "5-1 image data"), and first sixth image data ID6-1 (hereinafter, referred to as "6-1 image data"). The second basic image data B_ID2 may include second first image data ID1-2 (hereinafter, referred to as "1-2 image data"), second second image data ID2-2 (hereinafter, referred to as "2-2 image data"), second third image data ID3-2 (hereinafter, referred to as "3-2 image data"), second fourth image data ID4-2 (hereinafter, referred to as "4-2 image data"), second fifth image data ID5-2 (hereinafter, referred to as "5-2 image data"), and second sixth image data ID6-2 (hereinafter, referred to as "6-2 image data").

[0090] Referring to Figure 1 、 Figure 2 、 Figure 6 and Figure 7, the combiner 200 can divide the first base image data B_ID1 into the 1-1 image data ID1-1, the 2-1 image data ID2-1, the 3-1 image data ID3-1, the 4-1 image data ID4-1, the 5-1 image data ID5-1, and the 6-1 image data ID6-1, and can divide the second base image data B_ID2 into the 1-2 image data ID1-2, the 2-2 image data ID2-2, the 3-2 image data ID3-2, the 4-2 image data ID4-2, the 5-2 image data ID5-2, and the 6-2 image data ID6-2 (S210).

[0091] The 1-1 image data ID1-1, the 2-1 image data ID2-1, the 3-1 image data ID3-1, the 4-1 image data ID4-1, the 5-1 image data ID5-1, and the 6-1 image data ID6-1, as well as the 1-2 image data ID1-2, the 2-2 image data ID2-2, the 3-2 image data ID3-2, the 4-2 image data ID4-2, the 5-2 image data ID5-2, and the 6-2 image data ID6-2 may include a stain area and a stain-free area.

[0092] For example, the 1-1 image data ID1-1 may include a first first stain area SA1-1 (hereinafter referred to as the "1-1 stain area") and a first first stain-free area NSA1-1 (hereinafter referred to as the "1-1 stain-free area"). The 2-1 image data ID2-1 may include a first second stain area SA2-1 (hereinafter referred to as the "2-1 stain area") and a first second stain-free area NSA2-1 (hereinafter referred to as the "2-1 stain-free area"). The 3-1 image data ID3-1 may include a first third stain area SA3-1 (hereinafter referred to as the "3-1 stain area") and a first third stain-free area NSA3-1 (hereinafter referred to as the "3-1 stain-free area"). The 4-1 image data ID4-1 may include a first fourth stain area SA4-1 (hereinafter referred to as the "4-1 stain area") and a first fourth stain-free area NSA4-1 (hereinafter referred to as the "4-1 stain-free area"). The 5-1 image data ID5-1 may include a first fifth stain area SA5-1 (hereinafter referred to as the "5-1 stain area") and a first fifth stain-free area NSA5-1 (hereinafter referred to as the "5-1 stain-free area"). The 6-1 image data ID6-1 may include a first sixth stain area SA6-1 (hereinafter referred to as the "6-1 stain area") and a first sixth stain-free area NSA6-1 (hereinafter referred to as the "6-1 stain-free area").

[0093] For example, the 1-2 image data ID1-2 may include a second first stain area SA1-2 (hereinafter referred to as the "1-2 stain area") and a second first non-stain area NSA1-2 (hereinafter referred to as the "1-2 non-stain area"). The 2-2 image data ID2-2 may include a second second stain area SA2-2 (hereinafter referred to as the "2-2 stain area") and a second second non-stain area NSA2-2 (hereinafter referred to as the "2-2 non-stain area"). The 3-2 image data ID3-2 may include a second third stain area SA3-2 (hereinafter referred to as the "3-2 stain area") and a second third non-stain area NSA3-2 (hereinafter referred to as the "3-2 non-stain area"). The 4-2 image data ID4-2 may include a second fourth stain area SA4-2 (hereinafter referred to as the "4-2 stain area") and a second fourth non-stain area NSA4-2 (hereinafter referred to as the "4-2 non-stain area"). The 5-2 image data ID5-2 may include a second fifth stain area SA5-2 (hereinafter referred to as the "5-2 stain area") and a second fifth non-stain area NSA5-2 (hereinafter referred to as the "5-2 non-stain area"). The 6-2 image data ID6-2 may include a second sixth stain area SA6-2 (hereinafter referred to as the "6-2 stain area") and a second sixth non-stain area NSA6-2 (hereinafter referred to as the "6-2 non-stain area").

[0094] The first - 1 image data ID1 - 1, the second - 1 image data ID2 - 1, the third - 1 image data ID3 - 1, the fourth - 1 image data ID4 - 1, the fifth - 1 image data ID5 - 1, and the sixth - 1 image data ID6 - 1 may respectively include the first - 1 information, the second - 1 information, the third - 1 information, the fourth - 1 information, the fifth - 1 information, and the sixth - 1 information regarding the first - 1 stain area SA1 - 1, the second - 1 stain area SA2 - 1, the third - 1 stain area SA3 - 1, the fourth - 1 stain area SA4 - 1, the fifth - 1 stain area SA5 - 1, and the sixth - 1 stain area SA6 - 1, as well as the first - 1 non - stain area NSA1 - 1, the second - 1 non - stain area NSA2 - 1, the third - 1 non - stain area NSA3 - 1, the fourth - 1 non - stain area NSA4 - 1, the fifth - 1 non - stain area NSA5 - 1, and the sixth - 1 non - stain area NSA6 - 1. For example, the first - 1 information, the second - 1 information, the third - 1 information, the fourth - 1 information, the fifth - 1 information, and the sixth - 1 information may respectively include the positions, sizes, shapes, and brightnesses of the first - 1 stain area SA1 - 1, the second - 1 stain area SA2 - 1, the third - 1 stain area SA3 - 1, the fourth - 1 stain area SA4 - 1, the fifth - 1 stain area SA5 - 1, and the sixth - 1 stain area SA6 - 1, and may respectively include the positions, sizes, shapes, and brightnesses of the first - 1 non - stain area NSA1 - 1, the second - 1 non - stain area NSA2 - 1, the third - 1 non - stain area NSA3 - 1, the fourth - 1 non - stain area NSA4 - 1, the fifth - 1 non - stain area NSA5 - 1, and the sixth - 1 non - stain area NSA6 - 1.

[0095] The 1st - 2 image data ID1 - 2, the 2nd - 2 image data ID2 - 2, the 3rd - 2 image data ID3 - 2, the 4th - 2 image data ID4 - 2, the 5th - 2 image data ID5 - 2, and the 6th - 2 image data ID6 - 2 may respectively include the 1st - 2 information, the 2nd - 2 information, the 3rd - 2 information, the 4th - 2 information, the 5th - 2 information, and the 6th - 2 information regarding the 1st - 2 stain area SA1 - 2, the 2nd - 2 stain area SA2 - 2, the 3rd - 2 stain area SA3 - 2, the 4th - 2 stain area SA4 - 2, the 5th - 2 stain area SA5 - 2, the 6th - 2 stain area SA6 - 2, and the 1st - 2 non - stain area NSA1 - 2, the 2nd - 2 non - stain area NSA2 - 2, the 3rd - 2 non - stain area NSA3 - 2, the 4th - 2 non - stain area NSA4 - 2, the 5th - 2 non - stain area NSA5 - 2, and the 6th - 2 non - stain area NSA6 - 2. For example, the 1st - 2 information, the 2nd - 2 information, the 3rd - 2 information, the 4th - 2 information, the 5th - 2 information, and the 6th - 2 information may respectively include the positions, sizes, shapes, and brightnesses of the 1st - 2 stain area SA1 - 2, the 2nd - 2 stain area SA2 - 2, the 3rd - 2 stain area SA3 - 2, the 4th - 2 stain area SA4 - 2, the 5th - 2 stain area SA5 - 2, and the 6th - 2 stain area SA6 - 2, and may respectively include the positions, sizes, shapes, and brightnesses of the 1st - 2 non - stain area NSA1 - 2, the 2nd - 2 non - stain area NSA2 - 2, the 3rd - 2 non - stain area NSA3 - 2, the 4th - 2 non - stain area NSA4 - 2, the 5th - 2 non - stain area NSA5 - 2, and the 6th - 2 non - stain area NSA6 - 2.

[0096] In an embodiment, the first stain area SA1-1, the second stain area SA2-1, the third stain area SA3-1, the fourth stain area SA4-1, the fifth stain area SA5-1, and the sixth stain area SA6-1, as well as the first non-stain area NSA1-2, the second non-stain area NSA2-2, the third non-stain area NSA3-2, the fourth non-stain area NSA4-2, the fifth non-stain area NSA5-2, and the sixth non-stain area NSA6-2 may have the same positions in their respective unmerged image data, and the first non-stain area NSA1-1, the second non-stain area NSA2-1, the third non-stain area NSA3-1, the fourth non-stain area NSA4-1, the fifth non-stain area NSA5-1, and the sixth non-stain area NSA6-1, as well as the first non-stain area NSA1-2, the second non-stain area NSA2-2, the third non-stain area NSA3-2, the fourth non-stain area NSA4-2, the fifth non-stain area NSA5-2, and the sixth non-stain area NSA6-2 may have the same positions in their respective unmerged image data.

[0097] For example, the positions of the first-1 stain areas SA1-1, second-1 stain areas SA2-1, third-1 stain areas SA3-1, fourth-1 stain areas SA4-1, fifth-1 stain areas SA5-1, and sixth-1 stain areas SA6-1 in the first-1 image data ID1-1, second-1 image data ID2-1, third-1 image data ID3-1, fourth-1 image data ID4-1, fifth-1 image data ID5-1, and sixth-1 image data ID6-1 may be substantially the same as the positions of the first-2 stain areas SA1-2, second-2 stain areas SA2-2, third-2 stain areas SA3-2, fourth-2 stain areas SA4-2, fifth-2 stain areas SA5-2, and sixth-2 stain areas SA6-2 in the first-2 image data ID1-2, second-2 image data ID2-2, third-2 image data ID3-2, fourth-2 image data ID4-2, fifth-2 image data ID5-2, and sixth-2 image data ID6-2. The positions of the first-1 non-stain areas NSA1-1, second-1 non-stain areas NSA2-1, third-1 non-stain areas NSA3-1, fourth-1 non-stain areas NSA4-1, fifth-1 non-stain areas NSA5-1, and sixth-1 non-stain areas NSA6-1 in the first-1 image data ID1-1, second-1 image data ID2-1, third-1 image data ID3-1, fourth-1 image data ID4-1, fifth-1 image data ID5-1, and sixth-1 image data ID6-1 may be substantially the same as the positions of the first-2 non-stain areas NSA1-2, second-2 non-stain areas NSA2-2, third-2 non-stain areas NSA3-2, fourth-2 non-stain areas NSA4-2, fifth-2 non-stain areas NSA5-2, and sixth-2 non-stain areas NSA6-2 in the first-2 image data ID1-2, second-2 image data ID2-2, third-2 image data ID3-2, fourth-2 image data ID4-2, fifth-2 image data ID5-2, and sixth-2 image data ID6-2.

[0098] The sizes and shapes of the first to first stain regions SA1-1, second to first stain regions SA2-1, third to first stain regions SA3-1, fourth to first stain regions SA4-1, fifth to first stain regions SA5-1, and sixth to first stain regions SA6-1 may be substantially the same as the sizes and shapes of the first to second stain regions SA1-2, second to second stain regions SA2-2, third to second stain regions SA3-2, fourth to second stain regions SA4-2, fifth to second stain regions SA5-2, and sixth to second stain regions SA6-2, respectively. The sizes and shapes of the first to first stain-free regions NSA1-1, second to first stain-free regions NSA2-1, third to first stain-free regions NSA3-1, fourth to first stain-free regions NSA4-1, fifth to first stain-free regions NSA5-1, and sixth to first stain-free regions NSA6-1 may be substantially the same as the sizes and shapes of the first to second stain-free regions NSA1-2, second to second stain-free regions NSA2-2, third to second stain-free regions NSA3-2, fourth to second stain-free regions NSA4-2, fifth to second stain-free regions NSA5-2, and sixth to second stain-free regions NSA6-2, respectively.

[0099] However, the brightness of the first to first stain regions SA1-1, second to first stain regions SA2-1, third to first stain regions SA3-1, fourth to first stain regions SA4-1, fifth to first stain regions SA5-1, and sixth to first stain regions SA6-1 may be different from the brightness of the first to second stain regions SA1-2, second to second stain regions SA2-2, third to second stain regions SA3-2, fourth to second stain regions SA4-2, fifth to second stain regions SA5-2, and sixth to second stain regions SA6-2, respectively. The brightness of the first to first stain-free regions NSA1-1, second to first stain-free regions NSA2-1, third to first stain-free regions NSA3-1, fourth to first stain-free regions NSA4-1, fifth to first stain-free regions NSA5-1, and sixth to first stain-free regions NSA6-1 may be different from the brightness of the first to second stain-free regions NSA1-2, second to second stain-free regions NSA2-2, third to second stain-free regions NSA3-2, fourth to second stain-free regions NSA4-2, fifth to second stain-free regions NSA5-2, and sixth to second stain-free regions NSA6-2, respectively.

[0100] Reference Figure 1 、 Figure 2 、 Figure 8 and Figure 9 and Figure 8As shown in the figure, the first merged image data M_ID1 is obtained by merging the 1-1 image data ID1-1, the 2-1 image data ID2-1, the 3-1 image data ID3-1, the 4-1 image data ID4-1, the 5-1 image data ID5-1, and the 6-1 image data ID6-1, and the second merged image data M_ID2 is obtained by merging the 1-2 image data ID1-2, the 2-2 image data ID2-2, the 3-2 image data ID3-2, the 4-2 image data ID4-2, the 5-2 image data ID5-2, and the 6-2 image data ID6-2 (S310).

[0101] The merger 200 can generate the first merged image data M_ID1 by merging the 1-1 image data ID1-1, the 2-1 image data ID2-1, the 3-1 image data ID3-1, the 4-1 image data ID4-1, the 5-1 image data ID5-1, and the 6-1 image data ID6-1. The merger 200 can generate the second merged image data M_ID2 by merging the 1-2 image data ID1-2, the 2-2 image data ID2-2, the 3-2 image data ID3-2, the 4-2 image data ID4-2, the 5-2 image data ID5-2, and the 6-2 image data ID6-2.

[0102] The first merged image data M_ID1 may include a first merged stain area M_SA1 and a first merged stain-free area M_NSA1, and the second merged image data M_ID2 may include a second merged stain area M_SA2 and a second merged stain-free area M_NSA2.

[0103] The first merged image data M_ID1 may include first merged information regarding the first merged stain area M_SA1 and the first merged stain-free area M_NSA1. For example, the first merged information may include the position, size, shape, and brightness of the first merged stain area M_SA1 and the position, size, shape, and brightness of the first merged stain-free area M_NSA1.

[0104] The second merged image data M_ID2 may include second merged information regarding the second merged stain area M_SA2 and the second merged stain-free area M_NSA2. For example, the second merged information may include the position, size, shape, and brightness of the second merged stain area M_SA2 and the position, size, shape, and brightness of the second merged stain-free area M_NSA2.

[0105] In an embodiment, the first merged stain area M_SA1 may represent the 1-1 stain area SA1-1, the 2-1 stain area SA2-1, the 3-1 stain area SA3-1, the 4-1 stain area SA4-1, the 5-1 stain area SA5-1, and the 6-1 stain area SA6-1, and the first merged stain-free area M_NSA1 may represent the 1-1 stain-free area NSA1-1, the 2-1 stain-free area NSA2-1, the 3-1 stain-free area NSA3-1, the 4-1 stain-free area NSA4-1, the 5-1 stain-free area NSA5-1, and the 6-1 stain-free area NSA6-1. The second merged stain area M_SA2 may represent the 1-2 stain area SA1-2, the 2-2 stain area SA2-2, the 3-2 stain area SA3-2, the 4-2 stain area SA4-2, the 5-2 stain area SA5-2, and the 6-2 stain area SA6-2, and the second merged stain-free area M_NSA2 may represent the 1-2 stain-free area NSA1-2, the 2-2 stain-free area NSA2-2, the 3-2 stain-free area NSA3-2, the 4-2 stain-free area NSA4-2, the 5-2 stain-free area NSA5-2, and the 6-2 stain-free area NSA6-2.

[0106] For example, the position of the first merged stain area M_SA1 in the first merged image data M_ID1 may be substantially the same as the positions of the 1-1 stain area SA1-1, the 2-1 stain area SA2-1, the 3-1 stain area SA3-1, the 4-1 stain area SA4-1, the 5-1 stain area SA5-1, and the 6-1 stain area SA6-1 in the 1-1 image data ID1-1, the 2-1 image data ID2-1, the 3-1 image data ID3-1, the 4-1 image data ID4-1, the 5-1 image data ID5-1, and the 6-1 image data ID6-1. The position of the first merged stain-free area M_NSA1 may be substantially the same as the positions of the 1-1 stain-free area NSA1-1, the 2-1 stain-free area NSA2-1, the 3-1 stain-free area NSA3-1, the 4-1 stain-free area NSA4-1, the 5-1 stain-free area NSA5-1, and the 6-1 stain-free area NSA6-1 in the 1-1 image data ID1-1, the 2-1 image data ID2-1, the 3-1 image data ID3-1, the 4-1 image data ID4-1, the 5-1 image data ID5-1, and the 6-1 image data ID6-1.

[0107] The size and shape of the first merged stain region M_SA1 can be substantially the same as those of the 1-1st stain region SA1-1, the 2-1st stain region SA2-1, the 3-1st stain region SA3-1, the 4-1st stain region SA4-1, the 5-1st stain region SA5-1, and the 6-1st stain region SA6-1, and the size and shape of the first merged stain-free region M_NSA1 can be substantially the same as those of the 1-1st stain-free region NSA1-1, the 2-1st stain-free region NSA2-1, the 3-1st stain-free region NSA3-1, the 4-1st stain-free region NSA4-1, the 5-1st stain-free region NSA5-1, and the 6-1st stain-free region NSA6-1. However, the brightness of the first merged stain region M_SA1 can be substantially different from that of the 1-1st stain region SA1-1, the 2-1st stain region SA2-1, the 3-1st stain region SA3-1, the 4-1st stain region SA4-1, the 5-1st stain region SA5-1, and the 6-1st stain region SA6-1, and the brightness of the first merged stain-free region M_NSA1 can be substantially different from that of the 1-1st stain-free region NSA1-1, the 2-1st stain-free region NSA2-1, the 3-1st stain-free region NSA3-1, the 4-1st stain-free region NSA4-1, the 5-1st stain-free region NSA5-1, and the 6-1st stain-free region NSA6-1.

[0108] For example, the position of the second merged stain region M_SA2 in the second merged image data M_ID2 can be substantially the same as that of the 1-2nd stain region SA1-2, the 2-2nd stain region SA2-2, the 3-2nd stain region SA3-2, the 4-2nd stain region SA4-2, the 5-2nd stain region SA5-2, and the 6-2nd stain region SA6-2 in the 1-2nd image data ID1-2, the 2-2nd image data ID2-2, the 3-2nd image data ID3-2, the 4-2nd image data ID4-2, the 5-2nd image data ID5-2, and the 6-2nd image data ID6-2. The position of the second merged stain-free region M_NSA2 in the second merged image data M_ID2 can be substantially the same as that of the 1-2nd stain-free region NSA1-2, the 2-2nd stain-free region NSA2-2, the 3-2nd stain-free region NSA3-2, the 4-2nd stain-free region NSA4-2, the 5-2nd stain-free region NSA5-2, and the 6-2nd stain-free region NSA6-2 in the 1-2nd image data ID1-2, the 2-2nd image data ID2-2, the 3-2nd image data ID3-2, the 4-2nd image data ID4-2, the 5-2nd image data ID5-2, and the 6-2nd image data ID6-2.

[0109] The size and shape of the second merged stain region M_SA2 can be substantially the same as those of the 1-2nd stain regions SA1-2, 2-2nd stain regions SA2-2, 3-2nd stain regions SA3-2, 4-2nd stain regions SA4-2, 5-2nd stain regions SA5-2, and 6-2nd stain regions SA6-2, and the size and shape of the second merged stain-free region M_NSA2 can be substantially the same as those of the 1-2nd stain-free regions NSA1-2, 2-2nd stain-free regions NSA2-2, 3-2nd stain-free regions NSA3-2, 4-2nd stain-free regions NSA4-2, 5-2nd stain-free regions NSA5-2, and 6-2nd stain-free regions NSA6-2. However, the brightness of the second merged stain region M_SA2 can be substantially different from that of the 1-2nd stain regions SA1-2, 2-2nd stain regions SA2-2, 3-2nd stain regions SA3-2, 4-2nd stain regions SA4-2, 5-2nd stain regions SA5-2, and 6-2nd stain regions SA6-2, and the brightness of the second merged stain-free region M_NSA2 can be substantially different from that of the 1-2nd stain-free regions NSA1-2, 2-2nd stain-free regions NSA2-2, 3-2nd stain-free regions NSA3-2, 4-2nd stain-free regions NSA4-2, 5-2nd stain-free regions NSA5-2, and 6-2nd stain-free regions NSA6-2.

[0110] Next, the combiner 200 can generate merged image data M_ID by merging the first merged image data M_ID1 and the second merged image data M_ID2. For example, the merged image data M_ID can be generated using the first merge information and the second merge information respectively included in the first merged image data M_ID1 and the second merged image data M_ID2.

[0111] The merged image data M_ID can include a merged stain region M_SA and a merged stain-free region M_NSA. The merged image data M_ID can include merge information about the merged stain region M_SA and the merged stain-free region M_NSA. For example, the merge information can include the position, size, shape, and brightness of the merged stain region M_SA and the position, size, shape, and brightness of the merged stain-free region M_NSA.

[0112] In an embodiment, the merged stain region M_SA can correspond to the first merged stain region M_SA1 and the second merged stain region M_SA2, and the merged stain-free region M_NSA can correspond to the first merged stain-free region M_NSA1 and the second merged stain-free region M_NSA2.

[0113] For example, the positions of the merged stain regions M_SA in the merged image data M_ID may be substantially the same as the positions of the first merged stain region M_SA1 and the second merged stain region M_SA2 in the first merged image data M_ID1 and the second merged image data M_ID2, and the positions of the merged stain-free regions M_NSA in the merged image data M_ID may be substantially the same as the positions of the first merged stain-free region M_NSA1 and the second merged stain-free region M_NSA2 in the first merged image data M_ID1 and the second merged image data M_ID2.

[0114] The size and shape of the merged stain region M_SA may be substantially the same as the size and shape of the first merged stain region M_SA1 and the second merged stain region M_SA2, and the size and shape of the merged stain-free region M_NSA may be substantially the same as the size and shape of the first merged stain-free region M_NSA1 and the second merged stain-free region M_NSA2. However, the brightness of the merged stain region M_SA may be substantially different from the brightness of the first merged stain region M_SA1 and the second merged stain region M_SA2, and the brightness of the merged stain-free region M_NSA may be substantially different from the brightness of the first merged stain-free region M_NSA1 and the second merged stain-free region M_NSA2.

[0115] In an embodiment, the difference between the brightness of the merged stain region M_SA and the brightness of the merged stain-free region M_NSA may be greater than each of the differences between the brightness of each of the first to sixth stain regions SA1-1 to SA6-1 and the brightness of each of the first to sixth stain-free regions NSA1-1 to NSA6-1, and may be greater than each of the differences between the brightness of each of the first to sixth stain regions SA1-2 to SA6-2 and the brightness of each of the first to sixth stain-free regions NSA1-2 to NSA6-2.

[0116] Therefore, even if a stain formed at substantially the same position on the first substrate SUB1 and the second substrate SUB2 is not separately detected in the first - 1 image data ID1 - 1, the second - 1 image data ID2 - 1, the third - 1 image data ID3 - 1, the fourth - 1 image data ID4 - 1, the fifth - 1 image data ID5 - 1, and the sixth - 1 image data ID6 - 1, and the first - 2 image data ID1 - 2, the second - 2 image data ID2 - 2, the third - 2 image data ID3 - 2, the fourth - 2 image data ID4 - 2, the fifth - 2 image data ID5 - 2, and the sixth - 2 image data ID6 - 2, the stain can be detected by merging the image data M_ID. Therefore, the detection accuracy of stains formed at fixed positions on multiple substrates (e.g., the first substrate SUB1 and the second substrate SUB2) due to the same reason can be improved.

[0117] In the method (S10) of inspecting a substrate according to an embodiment of the present disclosure, the substrate inspection device 10 can generate merged image data M_ID by merging the first - 1 image data ID1 - 1, the second - 1 image data ID2 - 1, the third - 1 image data ID3 - 1, the fourth - 1 image data ID4 - 1, the fifth - 1 image data ID5 - 1, and the sixth - 1 image data ID6 - 1, and the first - 2 image data ID1 - 2, the second - 2 image data ID2 - 2, the third - 2 image data ID3 - 2, the fourth - 2 image data ID4 - 2, the fifth - 2 image data ID5 - 2, and the sixth - 2 image data ID6 - 2 obtained by dividing the first basic image data B_ID1 and the second basic image data B_ID2 of the first substrate SUB1 and the second substrate SUB2. Based on the merged image data M_ID, the detection accuracy of stains repeatedly formed at fixed positions on the first substrate SUB1 and the second substrate SUB2 due to the same reason (e.g., foreign objects or defects in the mask, etc.) can be improved. Therefore, since it is possible to more easily determine whether the first substrate SUB1 and the second substrate SUB2 are defective, the reliability of the manufacturing process can be improved.

[0118] Figure 10 and Figure 11 is a diagram Figure 2 showing another example of the method of inspecting a substrate.

[0119] For example, Figure 10 and Figure 11 can correspond to the step (S100) of generating the basic image data B_ID in the method (S10) of inspecting a substrate.

[0120] Hereinafter, the above - mentioned substrate inspection device 10 with reference to Figure 1 and the reference to Figure 2 、Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 and Figure 9 Any redundant descriptions presented by the method (S10) described will be omitted or simplified.

[0121] Reference Figure 1 , Figure 2 , Figure 10 and Figure 11 , in the method (S10) of inspecting a substrate, the image sensor 100 may generate base image data B_ID (S120) by capturing images of a first substrate SUB1 and a second substrate SUB2.

[0122] First, the image sensor 100 may generate first base image data B_ID1 by capturing an image of the first substrate SUB1, and may generate second base image data B_ID2 by capturing an image of the second substrate SUB2. Each of the first substrate SUB1 and the second substrate SUB2 may be Figure 1 the substrate SUB.

[0123] For example, the imager 110 may generate first captured image data and second captured image data by capturing an image of the first substrate SUB1 and an image of the second substrate SUB2, respectively. The generator 130 may generate first base image data B_ID1 and second base image data B_ID2 from the first captured image data and the second captured image data, respectively. Each of the first base image data B_ID1 and the second base image data B_ID2 may be sent from the image sensor 100 to the combiner 200.

[0124] Each of the first base image data B_ID1 and the second base image data B_ID2 may include image data. For example, the first base image data B_ID1 may include first - 1 image data ID1 - 1, second - 1 image data ID2 - 1, third - 1 image data ID3 - 1, fourth - 1 image data ID4 - 1, fifth - 1 image data ID5 - 1, and sixth - 1 image data ID6 - 1. The second base image data B_ID2 may include first - 2 image data ID1 - 2, second - 2 image data ID2 - 2, third - 2 image data ID3 - 2, fourth - 2 image data ID4 - 2, fifth - 2 image data ID5 - 2, and sixth - 2 image data ID6 - 2.

[0125] The first - 1 image data ID1 - 1, the second - 1 image data ID2 - 1, the third - 1 image data ID3 - 1, the fourth - 1 image data ID4 - 1, the fifth - 1 image data ID5 - 1, and the sixth - 1 image data ID6 - 1, as well as the first - 2 image data ID1 - 2, the second - 2 image data ID2 - 2, the third - 2 image data ID3 - 2, the fourth - 2 image data ID4 - 2, the fifth - 2 image data ID5 - 2, and the sixth - 2 image data ID6 - 2 may include a stained area and a non - stained area.

[0126] For example, the first - 1 image data ID1 - 1 may include the first - 1 stained area SA1 - 1 and the first - 1 non - stained area NSA1 - 1, and the second - 1 image data ID2 - 1 may include the second - 1 stained area SA2 - 1 and the second - 1 non - stained area NSA2 - 1. The third - 1 image data ID3 - 1 may include the third - 1 stained area SA3 - 1 and the third - 1 non - stained area NSA3 - 1, and the fourth - 1 image data ID4 - 1 may include the fourth - 1 stained area SA4 - 1 and the fourth - 1 non - stained area NSA4 - 1. The fifth - 1 image data ID5 - 1 may include the fifth - 1 stained area SA5 - 1 and the fifth - 1 non - stained area NSA5 - 1, and the sixth - 1 image data ID6 - 1 may include the sixth - 1 stained area SA6 - 1 and the sixth - 1 non - stained area NSA6 - 1.

[0127] For example, the first - 2 image data ID1 - 2 may include the first - 2 stained area SA1 - 2 and the first - 2 non - stained area NSA1 - 2, and the second - 2 image data ID2 - 2 may include the second - 2 stained area SA2 - 2 and the second - 2 non - stained area NSA2 - 2. The third - 2 image data ID3 - 2 may include the third - 2 stained area SA3 - 2 and the third - 2 non - stained area NSA3 - 2, and the fourth - 2 image data ID4 - 2 may include the fourth - 2 stained area SA4 - 2 and the fourth - 2 non - stained area NSA4 - 2. The fifth - 2 image data ID5 - 2 may include the fifth - 2 stained area SA5 - 2 and the fifth - 2 non - stained area NSA5 - 2, and the sixth - 2 image data ID6 - 2 may include the sixth - 2 stained area SA6 - 2 and the sixth - 2 non - stained area NSA6 - 2.

[0128] The first - 1 image data ID1 - 1, the second - 1 image data ID2 - 1, the third - 1 image data ID3 - 1, the fourth - 1 image data ID4 - 1, the fifth - 1 image data ID5 - 1, and the sixth - 1 image data ID6 - 1 may respectively include the first - 1 information, the second - 1 information, the third - 1 information, the fourth - 1 information, the fifth - 1 information, and the sixth - 1 information regarding the first - 1 stain area SA1 - 1, the second - 1 stain area SA2 - 1, the third - 1 stain area SA3 - 1, the fourth - 1 stain area SA4 - 1, the fifth - 1 stain area SA5 - 1, and the sixth - 1 stain area SA6 - 1, as well as the first - 1 non - stain area NSA1 - 1, the second - 1 non - stain area NSA2 - 1, the third - 1 non - stain area NSA3 - 1, the fourth - 1 non - stain area NSA4 - 1, the fifth - 1 non - stain area NSA5 - 1, and the sixth - 1 non - stain area NSA6 - 1. For example, the first - 1 information, the second - 1 information, the third - 1 information, the fourth - 1 information, the fifth - 1 information, and the sixth - 1 information may respectively include the positions, dimensions, shapes, and brightnesses of the first - 1 stain area SA1 - 1, the second - 1 stain area SA2 - 1, the third - 1 stain area SA3 - 1, the fourth - 1 stain area SA4 - 1, the fifth - 1 stain area SA5 - 1, and the sixth - 1 stain area SA6 - 1, and may respectively include the positions, dimensions, shapes, and brightnesses of the first - 1 non - stain area NSA1 - 1, the second - 1 non - stain area NSA2 - 1, the third - 1 non - stain area NSA3 - 1, the fourth - 1 non - stain area NSA4 - 1, the fifth - 1 non - stain area NSA5 - 1, and the sixth - 1 non - stain area NSA6 - 1.

[0129] The first - second image data ID1 - 2, the second - second image data ID2 - 2, the third - second image data ID3 - 2, the fourth - second image data ID4 - 2, the fifth - second image data ID5 - 2, and the sixth - second image data ID6 - 2 may respectively include the first - second information, the second - second information, the third - second information, the fourth - second information, the fifth - second information, and the sixth - second information regarding the first - second stain area SA1 - 2, the second - second stain area SA2 - 2, the third - second stain area SA3 - 2, the fourth - second stain area SA4 - 2, the fifth - second stain area SA5 - 2, the sixth - second stain area SA6 - 2, and the first - second non - stain area NSA1 - 2, the second - second non - stain area NSA2 - 2, the third - second non - stain area NSA3 - 2, the fourth - second non - stain area NSA4 - 2, the fifth - second non - stain area NSA5 - 2, and the sixth - second non - stain area NSA6 - 2. For example, the first - second information, the second - second information, the third - second information, the fourth - second information, the fifth - second information, and the sixth - second information may respectively include the position, size, shape, and brightness of the first - second stain area SA1 - 2, the second - second stain area SA2 - 2, the third - second stain area SA3 - 2, the fourth - second stain area SA4 - 2, the fifth - second stain area SA5 - 2, and the sixth - second stain area SA6 - 2, and may respectively include the position, size, shape, and brightness of the first - second non - stain area NSA1 - 2, the second - second non - stain area NSA2 - 2, the third - second non - stain area NSA3 - 2, the fourth - second non - stain area NSA4 - 2, the fifth - second non - stain area NSA5 - 2, and the sixth - second non - stain area NSA6 - 2.

[0130] In an embodiment, the first stain region SA1-1, the second stain region SA2-1, the third stain region SA3-1, the fourth stain region SA4-1, the fifth stain region SA5-1, and the sixth stain region SA6-1, as well as the first non-stain region NSA1-2, the second non-stain region NSA2-2, the third non-stain region NSA3-2, the fourth non-stain region NSA4-2, the fifth non-stain region NSA5-2, and the sixth non-stain region NSA6-2 can be approximately in the same position relative to each other within their respective image data, and the first non-stain region NSA1-1, the second non-stain region NSA2-1, the third non-stain region NSA3-1, the fourth non-stain region NSA4-1, the fifth non-stain region NSA5-1, and the sixth non-stain region NSA6-1, as well as the first non-stain region NSA1-2, the second non-stain region NSA2-2, the third non-stain region NSA3-2, the fourth non-stain region NSA4-2, the fifth non-stain region NSA5-2, and the sixth non-stain region NSA6-2 can be approximately in the same position relative to each other within their respective image data.

[0131] For example, the positions of the 1-1 stain regions SA1-1, 2-1 stain regions SA2-1, 3-1 stain regions SA3-1, 4-1 stain regions SA4-1, 5-1 stain regions SA5-1, and 6-1 stain regions SA6-1 in the corresponding 1-1 image data ID1-1, 2-1 image data ID2-1, 3-1 image data ID3-1, 4-1 image data ID4-1, 5-1 image data ID5-1, and 6-1 image data ID6-1 may be substantially the same as the positions of the 1-2 stain regions SA1-2, 2-2 stain regions SA2-2, 3-2 stain regions SA3-2, 4-2 stain regions SA4-2, 5-2 stain regions SA5-2, and 6-2 stain regions SA6-2 in the corresponding 1-2 image data ID1-2, 2-2 image data ID2-2, 3-2 image data ID3-2, 4-2 image data ID4-2, 5-2 image data ID5-2, and 6-2 image data ID6-2. The positions of the 1-1 stain-free regions NSA1-1, 2-1 stain-free regions NSA2-1, 3-1 stain-free regions NSA3-1, 4-1 stain-free regions NSA4-1, 5-1 stain-free regions NSA5-1, and 6-1 stain-free regions NSA6-1 in the 1-1 image data ID1-1, 2-1 image data ID2-1, 3-1 image data ID3-1, 4-1 image data ID4-1, 5-1 image data ID5-1, and 6-1 image data ID6-1 may be substantially the same as each other as the positions of the 1-2 stain-free regions NSA1-2, 2-2 stain-free regions NSA2-2, 3-2 stain-free regions NSA3-2, 4-2 stain-free regions NSA4-2, 5-2 stain-free regions NSA5-2, and 6-2 stain-free regions NSA6-2 in the 1-2 image data ID1-2, 2-2 image data ID2-2, 3-2 image data ID3-2, 4-2 image data ID4-2, 5-2 image data ID5-2, and 6-2 image data ID6-2.

[0132] The sizes and shapes of the first stain area SA1-1, the second stain area SA2-1, the third stain area SA3-1, the fourth stain area SA4-1, the fifth stain area SA5-1, and the sixth stain area SA6-1 can be substantially the same as those of the first stain area SA1-2, the second stain area SA2-2, the third stain area SA3-2, the fourth stain area SA4-2, the fifth stain area SA5-2, and the sixth stain area SA6-2. Also, the sizes and shapes of the first non-stain area NSA1-1, the second non-stain area NSA2-1, the third non-stain area NSA3-1, the fourth non-stain area NSA4-1, the fifth non-stain area NSA5-1, and the sixth non-stain area NSA6-1 can be substantially the same as those of the first non-stain area NSA1-2, the second non-stain area NSA2-2, the third non-stain area NSA3-2, the fourth non-stain area NSA4-2, the fifth non-stain area NSA5-2, and the sixth non-stain area NSA6-2.

[0133] However, the brightness of the first stain area SA1-1, the second stain area SA2-1, the third stain area SA3-1, the fourth stain area SA4-1, the fifth stain area SA5-1, and the sixth stain area SA6-1 can be different from that of the first stain area SA1-2, the second stain area SA2-2, the third stain area SA3-2, the fourth stain area SA4-2, the fifth stain area SA5-2, and the sixth stain area SA6-2 respectively. Also, the brightness of the first non-stain area NSA1-1, the second non-stain area NSA2-1, the third non-stain area NSA3-1, the fourth non-stain area NSA4-1, the fifth non-stain area NSA5-1, and the sixth non-stain area NSA6-1 can be different from that of the first non-stain area NSA1-2, the second non-stain area NSA2-2, the third non-stain area NSA3-2, the fourth non-stain area NSA4-2, the fifth non-stain area NSA5-2, and the sixth non-stain area NSA6-2 respectively.

[0134] Next, the combiner 200 can generate the base image data B_ID by combining the first base image data B_ID1 and the second base image data B_ID2.

[0135] The base image data B_ID may include image data. For example, the base image data B_ID may include first image data ID1, second image data ID2, third image data ID3, fourth image data ID4, fifth image data ID5, and sixth image data ID6.

[0136] Each of the first image data ID1, second image data ID2, third image data ID3, fourth image data ID4, fifth image data ID5, and sixth image data ID6 may include a stained area and a non-stained area. For example, the first image data ID1 may include a first stained area SA1 and a first non-stained area NSA1, and the second image data ID2 may include a second stained area SA2 and a second non-stained area NSA2. The third image data ID3 may include a third stained area SA3 and a third non-stained area NSA3, and the fourth image data ID4 may include a fourth stained area SA4 and a fourth non-stained area NSA4. The fifth image data ID5 may include a fifth stained area SA5 and a fifth non-stained area NSA5, and the sixth image data ID6 may include a sixth stained area SA6 and a sixth non-stained area NSA6.

[0137] The first image data ID1, second image data ID2, third image data ID3, fourth image data ID4, fifth image data ID5, and sixth image data ID6 may respectively include first information, second information, third information, fourth information, fifth information, and sixth information regarding the first stained area SA1, second stained area SA2, third stained area SA3, fourth stained area SA4, fifth stained area SA5, and sixth stained area SA6, and the first non-stained area NSA1, second non-stained area NSA2, third non-stained area NSA3, fourth non-stained area NSA4, fifth non-stained area NSA5, and sixth non-stained area NSA6. For example, the first information, second information, third information, fourth information, fifth information, and sixth information may respectively include the position, size, shape, and brightness of the first stained area SA1, second stained area SA2, third stained area SA3, fourth stained area SA4, fifth stained area SA5, and sixth stained area SA6, and may respectively include the position, size, shape, and brightness of the first non-stained area NSA1, second non-stained area NSA2, third non-stained area NSA3, fourth non-stained area NSA4, fifth non-stained area NSA5, and sixth non-stained area NSA6.

[0138] In an embodiment, the first stain region SA1 may represent the 1-1 stain region SA1-1 and the 1-2 stain region SA1-2, and the first non-stain region NSA1 may represent the 1-1 non-stain region NSA1-1 and the 1-2 non-stain region NSA1-2. The second stain region SA2 may represent the 2-1 stain region SA2-1 and the 2-2 stain region SA2-2, and the second non-stain region NSA2 may represent the 2-1 non-stain region NSA2-1 and the 2-2 non-stain region NSA2-2. The third stain region SA3 may represent the 3-1 stain region SA3-1 and the 3-2 stain region SA3-2, and the third non-stain region NSA3 may represent the 3-1 non-stain region NSA3-1 and the 3-2 non-stain region NSA3-2. The fourth stain region SA4 may represent the 4-1 stain region SA4-1 and the 4-2 stain region SA4-2, and the fourth non-stain region NSA4 may represent the 4-1 non-stain region NSA4-1 and the 4-2 non-stain region NSA4-2. The fifth stain region SA5 may represent the 5-1 stain region SA5-1 and the 5-2 stain region SA5-2, and the fifth non-stain region NSA5 may represent the 5-1 non-stain region NSA5-1 and the 5-2 non-stain region NSA5-2. The sixth stain region SA6 may represent the 6-1 stain region SA6-1 and the 6-2 stain region SA6-2, and the sixth non-stain region NSA6 may represent the 6-1 non-stain region NSA6-1 and the 6-2 non-stain region NSA6-2. "Represent" indicates the combined values of the position, size, shape, and brightness of the components.

[0139] In an embodiment, the difference between the luminance of each of the first stain region SA1, the second stain region SA2, the third stain region SA3, the fourth stain region SA4, the fifth stain region SA5, and the sixth stain region SA6 and the luminance of each of the first non-stain region NSA1, the second non-stain region NSA2, the third non-stain region NSA3, the fourth non-stain region NSA4, the fifth non-stain region NSA5, and the sixth non-stain region NSA6 may be greater than the difference between the luminance of each of the first - 1 stain region SA1 - 1, the second - 1 stain region SA2 - 1, the third - 1 stain region SA3 - 1, the fourth - 1 stain region SA4 - 1, the fifth - 1 stain region SA5 - 1, and the sixth - 1 stain region SA6 - 1 and the luminance of each of the first - 1 non-stain region NSA1 - 1, the second - 1 non-stain region NSA2 - 1, the third - 1 non-stain region NSA3 - 1, the fourth - 1 non-stain region NSA4 - 1, the fifth - 1 non-stain region NSA5 - 1, and the sixth - 1 non-stain region NSA6 - 1, and may be greater than the difference between the luminance of each of the first - 2 stain region SA1 - 2, the second - 2 stain region SA2 - 2, the third - 2 stain region SA3 - 2, the fourth - 2 stain region SA4 - 2, the fifth - 2 stain region SA5 - 2, and the sixth - 2 stain region SA6 - 2 and the luminance of each of the first - 2 non-stain region NSA1 - 2, the second - 2 non-stain region NSA2 - 2, the third - 2 non-stain region NSA3 - 2, the fourth - 2 non-stain region NSA4 - 2, the fifth - 2 non-stain region NSA5 - 2, and the sixth - 2 non-stain region NSA6 - 2.

[0140] Although Figure 6 , Figure 7 , Figure 8 , Figure 9 , Figure 10 and Figure 11 illustrate the image data of the first substrate SUB1 and the second substrate SUB2 being merged, the present disclosure is not limited thereto. For example, the image data of three or more substrates may be merged.

[0141] In a method (S10) of inspecting a substrate according to an embodiment of the present disclosure, a substrate inspection apparatus 10 may generate merged image data M_ID by merging first image data ID1, second image data ID2, third image data ID3, fourth image data ID4, fifth image data ID5, and sixth image data ID6 obtained by merging and dividing first base image data B_ID1 and second base image data B_ID2 of a first substrate SUB1 and a second substrate SUB2. Based on the merged image data M_ID, it is possible to improve the detection accuracy of stains formed repeatedly at fixed positions on the first substrate SUB1 and the second substrate SUB2 for the same reason (e.g., foreign substances or defects in a mask, etc.). Therefore, since it is possible to more easily determine whether the first substrate SUB1 and the second substrate SUB2 are defective, the reliability of the manufacturing process can be improved.

[0142] The present disclosure can be applied to manufacturing processes of various display devices. For example, the present disclosure is applicable to manufacturing processes of various display devices (such as display devices for vehicles, ships, and airplanes, display devices for portable communication devices, display devices for exhibitions or information transmission, medical display devices, etc.).

[0143] The foregoing is an illustration of embodiments and is not to be construed as a limitation of the embodiments. Although some embodiments have been described, those skilled in the art will readily understand that many modifications are possible in the embodiments without departing substantially from the novel teachings and advantages of the inventive concept. Therefore, all such modifications are intended to be included within the scope of the inventive concept as defined in the claims. Accordingly, it is to be understood that the foregoing is an illustration of various embodiments and is not to be construed as limited to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims.

Claims

1. A substrate inspection device, comprising: an image sensor that captures an image of the substrate to generate underlying image data; as well as A merger, the merger dividing the basic image data into first image data including a first stain area and a first stain-free area and second image data including a second stain area and a second stain-free area, and merging the first image data and the second image data to generate merged image data including a merged stain area and a merged stain-free area, the merged stain area representing the first stain area and the second stain area, and the merged stain-free area representing the first stain-free area and the second stain-free area.

2. The substrate inspection apparatus according to claim 1, wherein: The merger determines whether the substrate is defective based on the merged image data.

3. The substrate inspection apparatus according to claim 1, wherein: The merger generates the merged image data by calculating a first normal value by normalizing the sum of the brightness of the first stain area and the brightness of the second stain area and calculating a second normal value by normalizing the sum of the brightness of the first non-stain area and the brightness of the second non-stain area.

4. The substrate inspection apparatus according to claim 3, wherein: the merger calculates the first normal value by subtracting a predetermined value from the sum of the brightness of the first stain area and the brightness of the second stain area, and calculates the second normal value by subtracting the predetermined value from the sum of the brightness of the first stain-free area and the brightness of the second stain-free area, The merged stain area has the same brightness as the first normal value, and The merged spot-free area has the same brightness as the second normal value.

5. A method for inspecting a substrate, the method comprising: generating base image data by capturing an image of a substrate; Dividing the basic image data into first image data including a first stain area and a first stain-free area and second image data including a second stain area and a second stain-free area; as well as The first image data and the second image data are merged to generate merged image data including a merged stain area and a merged stain-free area, wherein the merged stain area represents the first stain area and the second stain area, and the merged stain-free area represents the first stain-free area and the second stain-free area.

6. The method for inspecting a substrate according to claim 5, further comprising: Based on the merged image data, it is determined whether the substrate is defective.

7. The method for inspecting a substrate according to claim 5, wherein: Generating the merged image data includes: Calculating a first normal value by normalizing the sum of the brightness of the first stain area and the brightness of the second stain area; and The second normal value is calculated by normalizing the sum of the brightness of the first spot-free area and the brightness of the second spot-free area.

8. The method for inspecting a substrate according to claim 5, wherein: Generating the base image data by capturing the image of the substrate comprises: generating first base image data by capturing an image of a first substrate; and generating second base image data by capturing an image of a second substrate, and Dividing the basic image data into the first image data and the second image data comprises: dividing the first basic image data into first first image data including a first first stain region and first second image data including a first second stain region, wherein the first second stain region has the same position in the first second image data as the position of the first first stain region in the first first image data; and The second basic image data is divided into second first image data including a second first stain area and second second image data including a second second stain area, wherein the second second stain area has the same position in the second second image data as the position of the second first stain area in the second first image data.

9. The method for inspecting a substrate according to claim 8, wherein: Generating the merged image data by merging the first image data and the second image data includes: Generate first merged image data including a first merged stain area representing the first first stain area and the first second stain area by merging the first first image data and the first second image data; generating second merged image data including a second merged stain area representing the second first stain area and the second second stain area by merging the second first image data and the second second image data; and The merged image data including the merged stain region indicating the first merged stain region and the second merged stain region is generated by merging the first merged image data and the second merged image data.

10. The method for inspecting a substrate according to claim 5, wherein: Generating the base image data by capturing the image of the substrate comprises: generating first base image data including a first first stain region and a first second stain region by capturing an image of a first substrate; generating second basic image data including a second first stain region and a second second stain region by capturing an image of a second substrate, wherein the second first stain region has the same position in the second basic image data as the first first stain region in the first basic image data, and the second second stain region has the same position in the second basic image data as the first second stain region in the first basic image data; and The basic image data including the first stain region representing the first first stain region and the second first stain region and the second stain region representing the first second stain region and the second second stain region are generated by merging the first basic image data and the second basic image data.

Images