Defect quality determination method and apparatus

Abstract

To correctly determine the quality of a defect that has occurred by correctly measuring the occurrence position in the height direction of a three-dimensional structure and determining the relative height with a pattern of a background.SOLUTION: A method for judging defect quality includes: acquiring plural images with a predetermined step in a height direction by optical imaging means (22) to an inspection subject (10) which includes multilayer transparent thin films (1, 2, 3, 4, 5, 6); calculating sharpness of partial images from luminance differences to adjacent pixels against each pixel of the plural images; calculating height information of the partial images from an image number which a calculating result of the sharpness at a same pixel position is maximum over all images of the plural images; obtaining three-dimensional information of all the images from calculating the height position; and judging the defect quality of the inspection subject based on the three-dimensional information.SELECTED DRAWING: Figure 4B

Description

Technical Field

[0001] The present invention relates to a defect pass / fail determination method and apparatus that measure height information of defects generated during the manufacture of an object to be inspected such as a semiconductor wafer or a thin film transistor display device using a multilayer thin film by optical imaging means, identify the height information of the defect occurrence site and the layer in which the defect occurred, and determine the pass / fail of the defect.

Background Art

[0002] In the manufacture of semiconductors, thin film transistor displays, and similar devices using multilayer thin films, minute patterns are formed using photolithography. In these manufacturing processes, defects such as pattern abnormalities and pinholes occur due to various factors, which cause a reduction in yield. In order to remove the factors causing the yield reduction and increase production efficiency, inspections are performed on the defects generated by managing these manufacturing processes, and the factors are identified.

[0003] Among such manufacturing processes, in the process of continuously forming a plurality of layers, there are cases where inspections for pattern abnormalities, pinholes, and similar defects cannot be performed during the process of forming each layer. For this reason, as the only means, it is necessary to perform a defect inspection after the formation of the final layer and identify the process that caused the corresponding defect from the height position information of the defect. For example, in the encapsulation process of flexible organic EL (Electro-Luminescence), a technique is used in which an inorganic thin film such as silicon nitride and an organic thin film such as polyimide are laminated multiple times to prevent oxygen and moisture in the air from entering the device. However, the presence of minute pinholes generated in each layer is fatal to the life of the device. In particular, it is necessary to accurately measure the position where pinholes generated in different layers are present in close proximity and determine the pass / fail of the product. However, the formation of these layers needs to be performed in a vacuum or a nitrogen atmosphere in a short time, and the object cannot be inspected by stopping the process halfway.

[0004] FIG. 1 shows a cross-sectional structure of a general flexible organic EL display device. The organic EL emits light by the electric circuit pattern 1. The electric circuit pattern 1 is formed on a base material composed of a first base material 5 and a second base material 6, and is sealed by a transparent film composed of a first sealing layer (inorganic film) 2, a second sealing layer (organic film) 3, and a third sealing layer (inorganic film) 4. Usually, the first sealing layer 2 and the third sealing layer 4 are silicon nitride films which are inorganic substances, and are formed by a Chemical Vapor Deposition (CVD) process. Polyimide, which is an organic substance, is used for the second sealing layer 3, and the second sealing layer 3 is formed, for example, by using an inkjet printing device. The first base material 5 is a transparent substrate. As the first base material 5, for example, resin substrates such as polyethylene terephthalate (PET) and polycarbonate (PC) are used.

[0005] FIG. 2 shows a state in which two pinholes 7A and 7B are formed in the sealing layer 4 in the process of forming a sealing layer (for example, a transparent film for sealing an organic material or an inorganic material). The pinholes 7A and 7B exist in the silicon nitride film of the third sealing layer 4. Even if oxygen (O2) and water (H2O) in the air penetrate into the polyimide film, the oxygen (O2) and water (H2O) in the air are blocked by the first sealing layer 2 (silicon nitride film). And the EL elements directly under the pinholes 7A and 7B are not immediately destroyed, and the pinholes 7A and 7B are not fatal defects for the organic EL display device.

[0006] On the one hand, FIG. 3 also shows a state in which pinholes 8A and 8B, which are defects, exist in a portion where the first sealing layer 2 and the silicon nitride films of the third sealing layer 4 are close to each other (with the vertical direction as the axis) in the process of forming the sealing layer of the flexible organic EL display device. In this case, oxygen and water that have entered from the pinhole 8A of the sealing layer 4 in contact with air penetrate into the polyimide film (sealing layer 3) over time. And finally, oxygen and water reach the pinhole 8B of the sealing layer 2, and the EL element directly below the pinhole 8B is destroyed. The destruction of the EL element in this way and the shortening of the life of the organic EL display device are fatal to the display device.

[0007] As described by giving an example of an organic EL, it is necessary to determine whether defects (for example, pinholes and foreign matters) on the sealing layer of the display device are fatal to the display device as shown in FIG. 3. However, since the thickness of the sealing layer is about several μm, in order to specify the sealing transparent film in which a defect has occurred, it is necessary to measure the height of the defect with a resolution on the sub-micron order. As a technique that satisfies such precise measurement accuracy, for example, a distance measuring device using a laser triangulation method, a white interferometer (Japanese Unexamined Patent Application Publication No. 2013-19767 (Patent Document 1)), and a confocal microscope (Japanese Unexamined Patent Application Publication No. 2012-237647 (Patent Document 2)) and the like are known.

[0008] Japanese Patent Publication No. 2014-148735 (Patent Document 3) discloses a multi-focus confocal Raman spectroscopic microscope, in which Raman scattered light from a sample is observed using a laser observation optical system. Further, in creating a three-dimensional profile map, a plurality of images corresponding to the focal distance are obtained for a three-dimensional sample by means of a camera device such as an optical microscope, and these images are combined to evaluate the in-focus degree at which the light intensity and luminance contrast are maximized, and a technique for generating a three-dimensional profile map (height map) of the sample is disclosed in Japanese Patent Publication No. 2012-220490 (Patent Document 4). Furthermore, Japanese Patent Publication No. 2005-172805 (Patent Document 5) discloses a technique for generating luminance / height information (three-dimensional information) regarding a sample based on the maximum intensity points of confocal images obtained for each Z relative position by moving a Z revolver at a moving pitch ΔZ in a scanning confocal microscope.

Prior Art Documents

Patent Documents

[0009]

Patent Document 1

Patent Document 2

Patent Document 3

Patent Document 4

Patent Document 5

Patent Document 6

Summary of the Invention

Problems to be Solved by the Invention

[0010] Distance measurement devices using the triangulation method with a laser, height measurement techniques such as white interferometers and confocal microscopes can measure the height of flat pattern defects of 10 μm or more. However, it is not possible to accurately measure the height information of defects with a size of several μm (for example, pinholes and foreign objects) or uneven foreign objects. In particular, defects of 1 μm or less cannot be measured.

[0011] Measurement methods based on the interference between the light reflected by the object and the reference light, or detection methods that condense the reflected light reflected by the object using a confocal optical system, cannot detect defects with a size of 1 μm or less. When the size of the object is 1 μm or less, or when the object has irregularities on its surface and the illumination light used for observation is scattered without specular reflection, the reflected light itself cannot be observed, so defects cannot be detected. And conventional height measurement devices cannot detect defects of 1 μm or less.

[0012] The laser observation optical system disclosed in Patent Document 3 does not have a function to inspect defects of an object such as a semiconductor wafer, a thin film transistor, particularly a sealing layer (sealing transparent film) of a flexible organic EL display device, and calculate the height information of the defects.

[0013] Furthermore, Depth From Defocus (DFD) or Depth From Focus (DFF) is known, which acquires an image while changing the focal position and extracts the height information of the object based on the amount of luminance change in a portion with a steep luminance change (Japanese Patent Laid-Open Publication No. 11-337313 (Patent Document 6)). However, in the conventional DFD processing as shown in Patent Document 6, due to errors caused by positional deviation between images and pixel noise of the image, for an object with an imaging pixel of about 1 pixel, such as a pattern defect with a size of 1 μm or less or a minute defect (pinhole or foreign object) on the layer, the height information cannot be accurately measured due to the positional deviation of the image and the error of pixel noise. The absolute position in the height direction in the measurement of height information using DFD or DFF depends on the accuracy of the machine connecting the substrate and the microscope.

[0014] At present, in display devices using EL elements, in order to improve manufacturing efficiency, a G6 size glass substrate (1500×1850 mm) is used. And since the stage for loading (mounting) the substrate provided inside the inspection apparatus becomes larger in accordance with the G6 size, it is not realistic to adjust the absolute position in the height direction of the substrate surface within 1 μm. For this reason, it is very difficult to measure the height information (absolute position in the height direction) of defects generated in the pattern formed on the glass substrate in units of 1 μm.

[0015] The technologies disclosed in Patent Document 4 and Patent Document 5 generate a three-dimensional profile map (height map) of a sample, but do not have a function of calculating the height information of defects in a sealing transparent film such as a flexible organic EL display device using a resolution of less than μm units.

[0016] The present invention has been made based on the above circumstances, and an object of the present invention is to accurately measure the generation position in the height direction of a three-dimensional structure even for pattern defects having a size of less than 1 μm and minute pinholes in a film (layer), etc., and to determine the height relative to the background pattern, thereby providing a defect determination method and apparatus for accurately determining the quality of the generated defects.

Means for Solving the Problems

[0017] The above object of the present invention is achieved by including the following steps. That is, for an object to be inspected used in a multilayer transparent thin film, acquiring a plurality of images with a predetermined step width in the height direction by optical imaging means, calculating the sharpness of a partial image from the luminance difference between adjacent pixels for each pixel of the plurality of images, calculating the height information of the partial image from the image number having the maximum calculation result of the sharpness at the same pixel position in all the images of the plurality of images, obtaining three-dimensional information of the entire image from the calculation of the height information, and determining the quality of defects of the object to be inspected based on the three-dimensional information. By including these, the object of the present invention is achieved.

[0018] The above object of the present invention is efficiently achieved by further including the following steps. That is, detecting the pattern defect of the image with the maximum sharpness, extracting the image with the maximum density of the partial image with high sharpness from the plurality of images, setting the image as the reference position 1 in the height direction of the three-dimensional pattern structure, and further measuring the height of the pattern defect in the three-dimensional pattern structure generated from the relationship between the height information of the pattern defect and the reference position 1, or by further including the following steps, that is, detecting the pattern defect of the image with the maximum sharpness, extracting the image with the most sharp interference image of the interference fringes generated at the end of the transparent thin film from the plurality of images, setting the image as the reference position 2 in the height direction of the three-dimensional pattern structure, and measuring the height of the pattern defect in the three-dimensional pattern structure generated from the relationship between the height information of the pattern defect and the reference position 2, or by further including the following step, that is, further including repairing the pattern defect by using the height information of the pattern defect, the object of the present invention is efficiently achieved.

[0019] The above object of the present invention is achieved by comprising the following configuration. That is, an imaging means that acquires a plurality of image data of an inspection object having a multi-layer transparent thin film with image numbers attached by an optical imaging means that can move up and down with a predetermined step width, an extraction unit that extracts the characteristics of the image data, an evaluation value calculation unit that calculates an evaluation value based on the characteristics, an evaluation value comparison unit that compares the previous evaluation value whose positional relationship with the evaluation value in the image data matches with the evaluation value and generates a comparison result, an evaluation value storage unit that stores the evaluation value based on the comparison result, an image number storage unit that stores the image number based on the comparison result, a three-dimensional information extraction unit that extracts three-dimensional information of the inspection object based on the image number stored in the image number storage unit, a three-dimensional information extraction unit that extracts height information of defects existing in the inspection object based on the three-dimensional information, and a pass / fail determination unit that determines the pass / fail of the inspection object based on the difference in the height information between the defects when there are a plurality of defects. By comprising these components, the object of the present invention is achieved.

[0020] The above object of the present invention is efficiently achieved by comprising the following configurations. That is, the three-dimensional information extraction unit extracts the three-dimensional information based on the image number with the highest evaluation value, or the evaluation value is calculated based on the luminance difference between the target pixel and adjacent pixels adjacent to the target pixel, or the three-dimensional information extraction unit determines the reference of the height information based on the evaluation value of the image data in which the interference fringes of the electrode pattern of the inspection object and the sealing layer of the inspection object are photographed, or the defect is a pattern defect, a pinhole or a foreign object, or the evaluation value is the sharpness calculated based on the difference between the luminance value of the target pixel and the luminance values of the adjacent pixels, or the inspection object is an organic EL display device, or the inspection object is a flexible organic EL display device formed on a flexible substrate, or the present invention further comprises at least one function of repairing the defect based on the height information calculated by the three-dimensional information extraction unit, or the object of the present invention is efficiently achieved by further including a function of selecting the function according to the height information.

Advantages of the Invention

[0021] According to the present invention, the heights of minute patterns and foreign objects are observed not only by their own reflected light but also by observing the decrease in the observation light passing through the edge portions of the patterns and the vicinity of the foreign objects. As a result, it becomes possible to accurately measure the height information of pinholes and foreign objects having a diameter of about 1 μm, which were impossible to measure with conventional techniques.

[0022] In the present invention, in the process of acquiring and processing a plurality of images, even if a horizontal position error is caused by the vibration of the apparatus itself or the floor between the acquired images, by considering the horizontal amplitude between the images, it is possible to accurately measure the height information of minute defects that occur without error. When comparing the image sharpness of pattern edges and foreign objects between images, the sharpness values between adjacent pixel positions of the target pixel position are compared, and the image number stored in the surroundings is replaced with the image number having the highest sharpness. In this way, the vibration resistance characteristics of the entire apparatus can be improved, and the above process greatly contributes to the cost reduction of the inspection apparatus.

[0023] Further, according to the present invention, in the manufacture of a thin-film multilayer device, since it is possible to identify the layer in which pinholes having a diameter of about 1 μm, which were impossible to measure with conventional techniques, are generated, it becomes possible to select an optimal repair means according to the substance of the film forming that layer.

Brief Description of the Drawings

[0024]

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Mode for Carrying Out the Invention

[0025] In the present invention, in the manufacture of semiconductors, display devices, and the like using multilayer transparent thin films, the height information of minute pattern defects generated during film formation is measured by optical inspection means. The height information of the defect generation site and the film type in which the defect has occurred are specified, and the quality of the defect is determined. More specifically, the microscope imaging device has a mechanism for mechanically scanning the focal position in the height direction, continuously captures and stores a plurality of images while scanning in the height direction, and calculates the contrast difference between adjacent pixels of the image information as a numerical evaluation value. For each pixel of the obtained evaluation value, the magnitude is determined between images, the number of the sharpest image of the pattern edge image is selected, and the image number is converted to the vertical height position at which the image was obtained to measure the vertical height of the corresponding image portion. The position serving as the height reference is obtained from the density of the maximum evaluation value of the contrast or from the interference fringe image generated at the end of the transparent film. For the images of defect points extracted as minute image points such as pattern abnormalities, pinholes, and foreign matters, evaluation values are also calculated by the same method, and the height of the defect points is measured based on the relative positional relationship with the reference height. From the vertical height at which the defect has occurred, the layer in which the defect point has occurred is specified, and the quality of the defect is determined.

[0026] Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0027] First, a configuration example of an embodiment of the present invention will be described with reference to FIGS. 4A and 4B.

[0028] In this embodiment, the flexible organic EL display device 10 is the object to be inspected. The display device 10 is mounted on a predetermined stage (not shown) and installed below the microscope 20. An objective lens 21 is mounted on the inspection object side of the lens barrel of the microscope 20, and an imaging camera 22 is mounted on the opposite side. The sequence control unit 30 controls the height direction drive motor 23 and the image acquisition unit 31. The height direction drive motor 23 is connected to the microscope 20 via a rack and pinion or the like. When the motor 23 is driven by the sequence control unit 30, the microscope 20 moves up and down. The camera 22 continuously images the flexible organic EL display device 10, and the image acquisition unit 31 acquires image data from the camera 22 according to the instructions of the sequence control unit 30. The image memory 32 stores the images sent from the image acquisition unit 31. The sequence control unit 30 controls the height of the microscope 20 at a predetermined step width (up and down amount) via the height direction drive motor 23, adjusts the focusing position of the objective lens 21, and can image the display device 10 that is the inspection object. Since the predetermined step width is the resolution in the height direction, the smaller the predetermined step width, the more images can be acquired for each height direction within the measurement range. Conversely, the larger the predetermined step width, the fewer images can be acquired for each height direction within the measurement range. By adjusting the predetermined step width, the resolution of the images in the height direction can be adjusted. In this way, when the microscope 20 moves in the height of the measurement range, all the images corresponding to the predetermined step width are stored in the image memory 32. When the optical system of the microscope 20 is configured as an infinity-corrected optical system, instead of the motor 23 driving the microscope 20 up and down, the motor 23 may drive only the objective lens 21 up and down.

[0029] The data stored in the image memory 32 is processed by the determination processing unit described below. The determination processing unit includes an edge processing unit 40 that extracts pattern edges, a foreign object processing unit 50 that extracts and processes minute foreign objects, and a pass / fail determination unit 60 that determines the pass / fail of defects based on the three-dimensional information ED from the edge processing unit 40 and the foreign object three-dimensional information FM from the foreign object processing unit 50. It is composed of a foreign object processing unit 50 that extracts and processes minute foreign objects, and a pass / fail determination unit 60 that determines the pass / fail of defects based on the three-dimensional information ED from the edge processing unit 40 and the foreign object three-dimensional information FM from the foreign object processing unit 50.

[0030] The edge processing unit 40 includes a pattern edge extraction unit 41 that extracts the edges of a pattern, an edge evaluation value calculation unit 42 that calculates an edge evaluation value, an edge evaluation value comparison unit 43 that compares the edge evaluation values, an edge evaluation value storage unit 44 that stores the edge evaluation values, an edge image number storage unit 45 that stores the numbers of edge images (including symbols and their equivalents), and an edge three-dimensional information extraction unit 46 that extracts edge three-dimensional information ED based on the information in the edge evaluation value storage unit 44 and the edge image number storage unit 45. The foreign matter processing unit 50 includes a minute foreign matter extraction unit 51, a foreign matter evaluation value calculation unit 52 that calculates a foreign matter evaluation value, a foreign matter evaluation value comparison unit 53 that compares the foreign matter evaluation values, a foreign matter evaluation value storage unit 54 that stores the foreign matter evaluation values, a foreign matter image number storage unit 55 that stores the numbers of foreign matter images (including symbols and their equivalents), and a foreign matter three-dimensional information extraction unit 56 that extracts foreign matter three-dimensional information FM based on the information in the foreign matter evaluation value storage unit 54 and the foreign matter image number storage unit 55.

[0031] In such a configuration, an operation example is shown in the flowchart in FIG. 5. First, by driving the sequence control unit 30, the flexible organic EL display device 10, which is the inspection object, is imaged using the microscope 20 (step S100). Next, the edge processing unit 40 extracts the pattern edges of the image (step S200), and the foreign matter processing unit 50 extracts minute foreign matters in the image (step S300). The order of the edge extraction process and the foreign matter extraction process may be changeable. The edge three-dimensional information extraction unit 46 in the edge processing unit 40 and the foreign matter three-dimensional information extraction unit 56 in the foreign matter processing unit 50 perform the extraction process of three-dimensional information (step S400). The three-dimensional information ED from the edge three-dimensional information extraction unit 46 and the foreign matter three-dimensional information FM from the foreign matter three-dimensional information extraction unit 56 are input to the pass / fail determination unit 60, and the pass / fail of the defect is determined (step S500).

[0032] First, a method for extracting edge pixels such as patterns and defects from the image acquired within the measurement range will be described.

[0033] In principle, in the image information, an evaluation value for evaluating a target pixel is calculated based on the difference in luminance values between the target pixel of interest and adjacent pixels adjacent to the target pixel. Then, the evaluation value is compared with a predetermined reference threshold value to determine the degree of defocus of the partial image around the target pixel. The apparatus determines whether the target pixel is an edge pixel such as a pattern or a defect based on the determination result of the degree of defocus. When the partial image is not defocused, the partial image has high sharpness. When the partial image is defocused, the partial image has low sharpness. As will be described later, even when extracting micro foreign object pixels, the degree of defocus of pixels that may be foreign objects is determined using the same method.

[0034] Existing in the image as image information, examples of patterns and defects to be extracted include electrode patterns of the flexible organic EL display device 10, organic films, inorganic films and their similar patterns, pinholes and their similar defects. And in order to evaluate the target pixel, variables representing the pixel are defined as follows. That is, when the positions of the respective pixels constituting the image are the horizontal position i and the vertical position j in the image, the gray value (luminance value) of any pixel in the image is represented by G(i, j).

[0035] Next, a method for calculating the edge evaluation value E(i, j) using a function will be described. The functions to be used are the function MAX(X, Y) that compares X and Y and outputs the larger value, and the function ABS(X) that outputs the absolute value of X. Using these functions, the edge evaluation value E(i, j) can be calculated. Then, by comparing the edge evaluation value E(i, j) with the edge threshold value, the edge pixels (edge partial image) of the pattern can be extracted. The calculated If the calculated edge evaluation value E(i, j) is greater than the edge threshold value, the pixel having G(i, j) is regarded as an edge pixel. Those for which the calculated edge evaluation value E(i, j) is not greater than the edge threshold value are regarded as not being edge pixels. More specifically, the edge evaluation value E(i, j) is given as in Equation 1. And whether the pixel having G(i, j) is an edge pixel is determined using Equation 2. [Number 1] Edge evaluation value E(i,j) = MAX(MAX(ABS(G(i,j) - G(i+1,j)), ABS(G(i,j) - G(i,j+1))), MAX(ABS(G(i,j) - G(i-1,j)), ABS(G(i,j) - G(i,j-1))) [Number 2] Edge evaluation value E(i,j) > Edge threshold The processing of the edge processing unit 40 for extracting edge pixels such as patterns and defects is performed as follows.

[0036] First, the pattern edge extraction unit 41 acquires the gray value G(i,j) of an arbitrary pixel of the image, the gray values G(i-1,j) and G(i+1,j) of the pixels adjacent to the arbitrary pixel in the horizontal direction of the image, and the gray values G(i,j-1) and G(i,j+1) of the pixels adjacent to the arbitrary pixel in the vertical direction of the image from the image memory 32. Then, the edge evaluation value calculation unit 42 calculates the edge evaluation value E(i,j) using the calculation method shown in Equation 1 and compares the edge evaluation value E(i,j) with the edge threshold. As a result of the comparison, if the edge evaluation value E(i,j) is greater than the edge threshold, the pixel having G(i,j) is regarded as an edge pixel. If the edge evaluation value E(i,j) is not greater than the edge threshold, the pixel having G(i,j) is not regarded as an edge pixel.

[0037] The edge evaluation value comparison unit 43 compares the next obtained edge evaluation value E(i,j) with the edge evaluation stored value EM(i,j) stored in the edge evaluation value storage unit 44 at the corresponding position (horizontal position i, vertical position j). The edge evaluation stored value EM(i,j) is the previous edge evaluation value E(i,j) obtained before the edge evaluation value E(i,j) is subsequently obtained. As a result of the comparison, if the edge evaluation value E(i,j) is greater than the edge evaluation stored value EM(i,j), the edge evaluation value storage unit 44 rewrites the edge evaluation stored value EM(i,j) with the edge evaluation value E(i,j). When the rewriting is performed, the edge image number storage unit 45 updates the edge image number EN(i,j) which is the corresponding position element to the image number being currently processed, and associates the edge evaluation value E(i,j) with the image number. The number may not be a number, but may be a symbol that can be distinguished from others. In this way, for all the pixels of the image number being currently processed, it is sequentially determined whether they are edge pixels or not. According to the determination, the edge evaluation stored value EM(i,j) and the edge image number EN(i,j) are respectively rewritten with the current edge evaluation value E(i,j) and the image number N being processed.

[0038] When the above processing for all the pixels in the image of the image number N being currently processed is completed, the same processing is performed on the image of the next image number. And when the processing for all the images of all the image numbers is completed, the edge three-dimensional information extraction unit 46 generates height information of edge pixels such as patterns and defects based on the image number EN(i,j) stored in the edge image number storage unit 45.

[0039] Next, the foreign object processing unit 50 that extracts an image of a minute foreign object and extracts three-dimensional information of the minute foreign object will be described. The process of extracting an image of a minute foreign object may be performed after the process of generating pattern edge height information, may be performed before the process, or may be performed simultaneously in parallel.

[0040] In the case of the extraction process of edge pixels such as patterns and defects, when assuming the positions of each pixel constituting the image as the horizontal position i and the vertical position j in the image, the gray value (luminance value) of an arbitrary pixel in the image is represented as G(i, j). The function used in the method for calculating the foreign matter evaluation value F(i, j) is the function MIN(X, Y) that compares X and Y and gives the smaller value. Using this function, the foreign matter evaluation value F(i, j) can be calculated.

[0041] By comparing the foreign matter evaluation value F(i, j) with the minute foreign matter threshold, minute foreign matter pixels (minute foreign matter partial images) of the pattern can be extracted. If the calculated foreign matter evaluation value F(i, j) is greater than the minute foreign matter threshold, the pixel having G(i, j) is regarded as a minute foreign matter pixel, and if the calculated foreign matter evaluation value F(i, j) is not greater than the minute foreign matter threshold, the pixel having G(i, j) is regarded as not being a minute foreign matter pixel. More specifically, the foreign matter evaluation value F(i, j) is given as in Equation 3. And whether the pixel having G(i, j) is a minute foreign matter pixel or not is determined using Equation 4. [Equation 3] Foreign matter evaluation value F(i, j) =MIN(G(i - 1, j)+G(i + 1, j)-G(i, j)*2, G(i, j - 1)+ G(i, j + 1)-G(i, j)*2) [Equation 4] Foreign matter evaluation value F(i, j)>Minute foreign matter threshold By using Equation 4, pixels with lower luminance than surrounding pixels in both the horizontal and vertical directions on the image, that is, dark spots with a size of about one pixel, can be extracted.

[0042] The process of the foreign matter processing unit 50 for extracting minute foreign matter pixels is performed as follows.

[0043] First, the minute foreign object extraction unit 51 acquires from the image memory 32 the gray value G(i, j) of an arbitrary pixel of the image, the gray values G(i - 1, j) and G(i + 1, j) of arbitrary pixels adjacent in the horizontal direction, and further the gray values G(i, j - 1) and G(i, j + 1) of arbitrary pixels adjacent in the vertical direction. Then, the foreign object evaluation value calculation unit 52 calculates the foreign object evaluation value F(i, j) using the calculation method shown in Equation 3, and compares the foreign object evaluation value F(i, j) with the minute foreign object threshold. As a result of the comparison, if the minute foreign object evaluation value F(i, j) is greater than the foreign object threshold, the pixel having G(i, j) is regarded as a minute foreign object pixel, and if the minute foreign object evaluation value F(i, j) is not greater than the foreign object threshold, the pixel having G(i, j) is not regarded as a minute foreign object pixel.

[0044] Next, the foreign object evaluation value comparison unit 53 compares the next-acquired foreign object evaluation value F(i, j) with the foreign object evaluation stored value FM(i, j) at the corresponding position (horizontal position i, vertical position j) stored in the foreign object evaluation value storage unit 54. The foreign object evaluation stored value FM(i, j) is the foreign object evaluation value F(i, j) acquired before the next acquisition of the foreign object evaluation value F(i, j). As a result of the comparison, when the foreign object evaluation value F(i, j) is greater than the foreign object evaluation stored value FM(i, j), the foreign object evaluation value storage unit 54 rewrites the foreign object evaluation stored value FM(i, j) with the next-acquired foreign object evaluation value F(i, j). When the rewriting of the foreign object evaluation stored value FM(i, j) is performed, the foreign object image number storage unit 55 updates the foreign object image number FN(i, j) which is an element at the corresponding position to the current processing image number N, and associates the foreign object evaluation value F(i, j) with the image number N.

[0045] For all pixels of the currently processed image number N, it is sequentially determined whether they are foreign object pixels or not. According to the determination, the foreign object evaluation stored value FM(i, j) and the foreign object image number FN(i, j) are respectively rewritten with the currently processed edge evaluation value E(i, j) and the image number N. Then, when the processing for all pixels in the image of the currently processed image number N is completed, the same processing is performed on the image of the next image number (N + 1).

[0046] When the processing for the images of all the image numbers is completed, the foreign object three-dimensional information extraction unit 56 generates height information of the minute foreign objects based on the foreign object image numbers FN(i,j) stored in the foreign object image number storage unit 55.

[0047] Based on the relative heights between the edge three-dimensional information ED (height information of the edge pixels of the pattern) from the edge three-dimensional information extraction unit 46 and the foreign object three-dimensional information FM (height information of the minute foreign objects) from the foreign object three-dimensional information extraction unit 56, the pass / fail determination unit 60 performs a pass / fail determination on the flexible organic EL display device 10 as a sample and outputs the determination result.

[0048] First, as an example of the inspection object (sample), FIG. 6 shows a state where two minute foreign objects 101 and 102 have occurred in one pixel of the flexible organic EL display device 10. As will be described later, the first minute foreign object 101 is located on the plane 10-30 where the 30th image is in focus, and the second minute foreign object 102 is located on the plane 10-40 where the 40th image is in focus. The reference plane for the height is the electrode pattern 103. In this embodiment, since the images are acquired at intervals of 0.1 μm, it is detected that the minute foreign object 101 is at a position 2.0 μm above the pattern, and the minute foreign object 102 is at a position 3.0 μm.

[0049] Next, the reference for the height of the three-dimensional information (structure) will be described. As a structure in which the pattern edge of the circuit of the flexible organic EL is difficult to detect, the shape of the light-emitting layer 107 may be rectangular (window) shaped formed by the organic film 105 covering the cathode electrode 104 as shown in FIG. 7. In the following description, the light-emitting portion is referred to as a window. The illumination light is reflected by the interface between the organic film 105 forming the window and the transparent film 106. An annular interference fringe is formed around the window by the illumination light using coaxial epi-illumination. The interference fringe can be observed using image processing. The innermost interference fringe 107A is the edge (edge) of the window 107 of the organic film 105. As a result, the edge evaluation value E(i,j) detected in the region with this edge is suitable as a reference for the height in the three-dimensional information (structure). That is, although the pattern edge of the circuit of the flexible organic EL substrate is difficult to detect due to its structure, the edge (edge) of the window 107 of the organic film 105 is suitable as a reference for the height of the three-dimensional information (structure). Furthermore, when the flexible organic EL substrate is observed from the vertical direction of the flexible organic EL substrate (for example, using a microscope), the cathode electrode 104 acts like a mirror surface, and an annular strong interference fringe is observed around the window by the illumination light reflected in the opposite direction at the interface between the organic film 105 forming the window and the transparent film 106. The state is shown in FIG. 8. FIG. 8 depicts the edge image 104A of the cathode electrode 104, the edge image 107A of the organic EL light-emitting layer 107, and the interference image 107B generated at the end of the organic film 105 forming the window.

[0050] The details of each operation described above will be described with reference to the flowchart.

[0051] A detailed operation example of imaging (step S100 in FIG. 5) will be described with reference to the flowchart of FIG. 9. First, the sequence control unit 30 initializes the image number (step S101), and adjusts the height of the microscope 20 to the measurement start position by driving the height direction drive motor 23 (step S102). In this state, the objective lens 10 and the camera 22 are in a relationship of the in-focus position, and the image of the display device 10 is captured via the image reading unit 31 (step S103), and the image data is stored in the image memory 32 (step S104). Next, the microscope 20 is moved in the height direction by a predetermined step width (step S105), and it is determined whether the height of the microscope 20 is within the measurement range (step S106). When the height of the microscope 20 is within the measurement range, the image of the display device 10 is repeatedly captured, and the image data is sequentially stored. If the height of the microscope 20 is outside the measurement range, the currently processed image number N is stored in the image memory 32 as the maximum image number Nmax (step S107), and then the imaging is completed.

[0052] Next, the details of the pattern edge extraction operation (step S200 in FIG. 5) of the display device 10 will be described with reference to the flowcharts of FIGS. 10A and 10B. First, values indicating the image number N, the height of the lens barrel portion of the microscope 20, the horizontal position i and the vertical position j, and the number of edge pixels EC(N) are initialized (step S201). Next, the edge evaluation value calculation unit 42 acquires the gray values G(i,j), G(i+1,j), G(i,j+1), G(i-1,j), G(i,j-1) of the image number N being processed from the image memory 16 (step S202), and calculates the edge evaluation value E(i,j) based on Equation 1 from the gray values (step S203). The edge evaluation value comparison unit 43 compares the edge evaluation value E(i,j) with the edge threshold (step S204). When the edge evaluation value E(i,j) is greater than the edge threshold, the edge evaluation value comparison unit 43 rewrites the edge evaluation stored value EM(i,j) in the edge evaluation value storage unit 44 with the calculated edge evaluation value E(i,j) (step S205), and rewrites the edge image number EN(i,j) in the edge image number storage unit 45 with the image number N being processed (step S206). Further, the number of edge pixels EC(N) indicating the number of pixels determined to be edge pixels of the image number N is incremented ("+1") (step S207), and the horizontal position i of the pixel is incremented ("+1") (step S208). Also, in step S204 above, when the edge evaluation value E(i,j) is less than or equal to the edge threshold, only one increase ("+1") in the horizontal position i of the pixel is performed (step S208).

[0053] Thereafter, the horizontal position i of the pixel is compared with the maximum horizontal position imax (the position where the horizontal position is the end of the image) (step S209). When the horizontal position i of the pixel is greater than or equal to the maximum horizontal position imax, the vertical position j is incremented ("+1") (step S210), and the horizontal position i is initialized (step S210). When the horizontal position i of the pixel is less than the maximum horizontal position imax, the process returns to step S202 above, the gray values G(i,j), G(i+1,j), G(i,j+1), G(i-1,j), G(i,j-1) of the pixel are reacquired, and the process of extracting the edge pixels of the pattern is performed.

[0054] Next, the vertical position j of the pixel is compared with the maximum vertical position jmax (step S211). If the vertical position j of the pixel is greater than or equal to the maximum vertical position jmax, the image number N is incremented ("+1"), and the vertical position j is initialized (step S212). If the vertical position j of the pixel is less than the maximum vertical position jmax, the process returns to step S202 above. Similarly, the gray values G(i,j), G(i+1,j), G(i,j+1), G(i-1,j), G(i,j-1) of the pixel are obtained, and the process of extracting the edge pixels of the pattern is performed.

[0055] Thereafter, the image number N is compared with the maximum image number Nmax (step S213). If the image number N is greater than or equal to the maximum image number Nmax, the process of extracting the edge pixels of the pattern ends. If the image number N is less than the maximum image number Nmax, the process returns to step S202 above, and the process of extracting the edge pixels of the pattern is performed.

[0056] Next, the details of the minute foreign object image extraction operation (step S300 in FIG. 5) of the display device 10 will be described with reference to the flowcharts of FIGS. 11A and 11B. First, values indicating the image number N, the height of the microscope, the horizontal position i and the vertical position j, and the foreign object pixel count FC(N) are initialized (step S301). Next, the foreign object evaluation value calculation unit 52 acquires the gray values G(i-1,j), G(i,j), G(i+1,j), G(i,j-1), G(i,j+1) of the image (step S302), and then calculates the foreign object evaluation value F(i,j) according to Equation 3 (step S303). Then, the foreign object evaluation value comparison unit 53 compares the foreign object evaluation value F(i,j) with the minute foreign object threshold value (step S304). When the foreign object evaluation value F(i,j) is greater than the minute foreign object threshold value, the foreign object value comparison unit 53 rewrites the foreign object evaluation storage value FM(i,j) in the foreign object evaluation value storage unit 54 with the minute foreign object evaluation value F(i,j) (step S305), and the foreign object image number FN(i,j) in the foreign object image number storage unit 55 is rewritten with the image number N being processed currently (step S306). The foreign object pixel count FC(N) indicating the number of pixels determined to be foreign objects in the image number N is incremented by 1 ("+1") (step S307), and the horizontal position i of the pixel is incremented by 1 ("+1") (step S308). When the foreign object evaluation value F(i,j) is less than or equal to the minute foreign object threshold value, only an increment of 1 ("+1") in the horizontal position i of the pixel is performed (step S308).

[0057] Next, the horizontal position i of the pixel is compared with the maximum horizontal position imax (the position where the horizontal position is at the edge of the image) (step S309). When the horizontal position i of the pixel is greater than or equal to the maximum horizontal position imax, the vertical position j of the pixel is incremented ("+1"), and the horizontal position i is initialized (step S310). When the horizontal position i of the pixel is less than the maximum horizontal position imax, the process returns to step S302 above, and the gray values G(i - 1, j), G(i, j), G(i + 1, j), G(i, j - 1), G(i, j + 1) of the pixel are re-acquired (step S302), and the process of extracting the minute foreign object image is performed. The vertical position j of the pixel is compared with the maximum vertical position jmax (step S311). When the vertical position j of the pixel is greater than or equal to the maximum vertical position jmax, the image number N is incremented ("+1"), and the vertical position j is initialized (step S312). When the vertical position j of the pixel is less than the maximum vertical position jmax, the process returns to step S302 above, and the gray values G(i - 1, j), G(i, j), G(i + 1, j), G(i, j - 1), G(i, j + 1) of the pixel are acquired in the same manner, and the process of extracting the minute foreign object image is performed. The image number is compared with the maximum image number (step S313). When the image number N is greater than or equal to the maximum image number Nmax, the process of extracting the minute foreign object image is terminated. When the image number N is less than the maximum image number Nmax, the process returns to step S302 above, and the process of extracting the minute foreign object image is performed.

[0058] Next, the details of the 3D information extraction operation (step S400 in FIG. 5) in the edge 3D information extraction unit 46 and the foreign object 3D information extraction unit 56 will be described with reference to the flowcharts of FIGS. 12A and 12B. Here, an example will be described in which the edge process is performed first and then the foreign object process, but the order can be changed, and both processes can also be performed in parallel.

[0059] First, detect the image number N with the largest number of edge pixels. First, initialize the image number N and the maximum edge pixel value ECmax (step S401), and obtain the edge pixel number EC(N) (step S402). Next, determine whether the edge pixel number EC(N) is greater than the maximum edge pixel value ECmax (step S403). If the edge pixel number EC(N) is greater than the maximum edge pixel value ECmax, the device replaces the maximum edge pixel value image number ECNmax with the image number N (step S404), and the image number N is incremented ("+1") (step S405). If the edge pixel number EC(N) is less than or equal to the maximum edge pixel value ECmax, only the image number N is incremented ("+1") (step S405). Thereafter, determine whether the image number N is greater than or equal to the maximum image number Nmax (step S406). If the image number N is less than the maximum image number Nmax, the process returns to step S403 above, and the above process is repeated. In this way, the device detects the image number with the largest number of edge pixels and detects the image number at which the electrode pattern of the display device 10 is in focus.

[0060] In the next step, the image number N with the largest number of foreign object pixels is detected. First, the image number N, the first maximum value of the number of foreign object pixels FCN1max, and the second maximum value of the number of foreign object pixels FCN2max are initialized (step S407), and the number of foreign object pixels FCN(N) is obtained (step S408). Next, the device determines whether the number of foreign object pixels FCN(N) is greater than the first maximum value of the number of foreign object pixels FCN1max (step S409). If the number of foreign object pixels FCN(N) is greater than the first maximum value of the number of foreign object pixels FCN1max, the device rewrites the value of the first foreign object pixel number image number FCN1max to the value of the second foreign object pixel number image number FCN2max, and rewrites the value of the first foreign object pixel number image number to the image number N (step S410). The image number N is incremented ("+1") (step S411). Also, if it is determined that the number of foreign object pixels FCN(N) is less than or equal to the first maximum value of the number of foreign object pixels FCN1max, the image number N is incremented by only one ("+1") (step S411). Then, the device determines whether the image number N is greater than or equal to the maximum image number Nmax (step S412). If the image number N is less than the maximum image number Nmax, the process returns to step S409 above, and the above process is repeatedly executed. In this way, the image number having the maximum number of foreign object pixels, that is, the first foreign object pixel number image number FCN1max is detected, and the image number having the maximum number of foreign object rewritten pixels or the second largest image number, that is, the second foreign object pixel number image number FCN2max is detected. The image number at which the foreign object is in focus is detected.

[0061] Finally, the first difference between the first foreign object pixel number image number FCN1max and the maximum edge pixel number image number ECNmax, and the second difference between the second foreign object pixel number image number FCN2max and the maximum edge pixel number image number ECNmax are calculated. Then, the height information of the foreign object is extracted based on the first difference and the second difference (step S413).

[0062] Next, based on the image actually taken of a sample of flexible organic EL (for one pixel), the process of extracting the three-dimensional information (height information) of the edge of the sample and the minute foreign object will be described step by step.

[0063] First, the height of the microscope is changed at equal intervals (a predetermined step width, for example, 0.1 μm) in the height direction, and 40 images of a flexible organic EL (1 pixel size) that is the object to be inspected are acquired. Then, the first, tenth, thirtieth, and fortieth images out of the 40 acquired images are arranged in the height direction as shown in FIG. 13. As shown in FIG. 13, the first image 10-1 is an image acquired from a height 1 μm below the electrode pattern 103. Images are acquired every 0.1 μm upward from a height 1 μm below the electrode pattern 103. The image 10-10 where the electrode pattern 103 is in perfect focus is acquired as the tenth image. The image 10-30 where the first foreign object 101 is in focus is acquired as the thirtieth image. The image 10-40 where the second foreign object 102 is in focus is acquired as the fortieth image. A focused image is characterized in that the difference in luminance values between the pixel of interest and the adjacent pixels is large and the sharpness of the image is high. For example, an interference image of a sample, a defect in a film, and the like in a focused image exist as thin lines or dots having a large brightness difference (luminance difference) with respect to the surroundings, that is, as a partial image with high sharpness (not defocused).

[0064] Here, the flexible organic EL (1 pixel size) is imaged in a region where 20 pixels are arranged in the vertical direction and 20 pixels are arranged in the horizontal direction, and the state of being converted into array data of luminance values is shown in FIGS. 14 to 17. FIGS. 14 to 17 show the array data of the luminance values (gray values) of the first image 36, the array data of the luminance values (gray values) of the tenth image 37, the array data of the luminance values (gray values) of the thirtieth image 38, and the array data of the luminance values (gray values) of the fortieth image 39, respectively. In FIGS. 14 to 17, the horizontal position corresponds to the horizontal position i, and the vertical position corresponds to the vertical position j. The positional relationship between the image position and the array data is also applicable to the array data of the luminance values described later.

[0065] Next, the edge evaluation value E(i,j) of each image is calculated using Equation 1. Then, the distribution of the edge evaluation value E(i,j) in the area where 20 pixels are arranged in the vertical direction and 20 pixels are arranged in the horizontal direction of the flexible organic EL (1 pixel size) of the inspection object is shown in FIGS. 18 to 21. FIGS. 18 to 21 show the edge evaluation value E(i,j) of the first image 36, the edge evaluation value E(i,j) of the tenth image 37, the edge evaluation value E(i,j) of the thirtieth image 38, and the edge evaluation value E(i,j) of the fortieth image 39, respectively. Similarly, the foreign matter evaluation value F(i,j) of each image can be calculated using Equation 3.

[0066] After the edge evaluation values E(i,j) corresponding to all the image numbers (N = 1 to 40) from the first to the fortieth are calculated, FIGS. 22 to 25 show how the edge evaluation stored value EM(i,j) is updated in the evaluation value storage unit 44. FIGS. 22 to 25 show how the edge evaluation stored value EM(i,j) is updated using the edge evaluation value E(i,j) of the first image 36, how the edge evaluation stored value EM(i,j) is updated using the edge evaluation value E(i,j) of the tenth image 37, how the edge evaluation stored value EM(i,j) is updated using the edge evaluation value E(i,j) and the foreign matter evaluation value F(i,j) of the thirtieth image 38, and finally how the edge evaluation stored value EM(i,j) is updated using the edge evaluation value E(i,j) and the foreign matter evaluation value F(i,j) of the fortieth image 39.

[0067] After the update of the edge evaluation stored value EM(i,j) using the edge evaluation value E(i,j) (hereinafter referred to as "evaluation value update") is completed in this way, FIGS. 28 to 31 show how the image number N is updated in the edge image number storage unit 45.

[0068] In the process of detecting foreign matter, Fig. 26 shows how the foreign matter evaluation memory value FM(i, j) is updated using the foreign matter evaluation value F(i, j) of the 30th image. Fig. 27 shows how the foreign matter evaluation memory value FM(i, j) is updated using the foreign matter evaluation value F(i, j) of the 40th image. Since the foreign matter evaluation values F(i, j) of the 1st and 10th images are below the foreign matter threshold, they are set to 0 (zero).

[0069] Next, in the process of detecting edges, after the evaluation value update for the 1st image is completed, Fig. 28 shows how the image number N in the image number storage unit 45 is updated. Similarly, after the evaluation value update for the 10th image is completed, Fig. 29 shows how the image number N in the image number storage unit 45 is updated. After the evaluation value update for the 30th image is completed, Fig. 30 shows how the image number N in the image number storage unit 45 is updated. After the evaluation value update for the 40th image is completed, Fig. 31 shows how the image number N in the image number storage unit 45 is updated.

[0070] Next, in the process of detecting foreign matter, after the evaluation value update for the 30th image is completed, Fig. 32 shows how the image number in the foreign matter image number storage unit 55 is updated. After the evaluation value update for the 40th image is completed, Fig. 33 shows how the image number in the foreign matter image number storage unit 55 is updated. The foreign matter three-dimensional information extraction unit 56 determines that the pixels where the foreign matter is displayed exist at the positions (5, 14) in image number 30 (the 30th image) and (15, 4) in image number 40 (the 40th image) as shown in Fig. 33. Since the foreign matter evaluation values F(i, j) of the 1st and 10th images are below the foreign matter threshold and are set to 0, no evaluation value update is performed for the 1st and 10th images, and no pixels displaying foreign matter are detected.

[0071] In this way, when the process of updating the image numbers for which edges and foreign objects have been detected is completed, finally, based on the array data of the image numbers shown in FIG. 31, the apparatus creates a contour graph for the object to be inspected. Then, as shown in FIG. 34, the three-dimensional information (height information) of the sample can be analyzed from the contour graph. Based on FIG. 34, the three-dimensional information about the object to be inspected is determined in detail.

[0072] Since the array of the edge evaluation values E(i, j) of the tenth image shown in FIG. 19 can be said to have the highest density, it can be determined that the tenth image is an image in focus on the thin film transistor (TFT) circuit portion existing at the bottom of the object, which is the standard for the three-dimensional information (structure) of the object. Therefore, the height of the tenth image is set as the reference height (0 μm). The image number corresponding to the reference height of the three-dimensional information is 10. That is, it can be set with reference to the height of the tenth image.

[0073] Since the image number of the image in focus of the first minute foreign object 101 is 30, the apparatus can determine that the first minute foreign object 101 exists at a height 2.0 μm above the reference. Since the image number of the image in focus of the second minute foreign object 102 is 40, the apparatus can determine that the second minute foreign object 102 exists at a height 3.0 μm above the reference. Then, the apparatus can determine that the first minute foreign object 101 exists 2 μm above the electrode pattern 103. Furthermore, the apparatus can determine that the second minute foreign object 102 exists 3 μm above the first foreign object, that is, 5 μm above the electrode pattern 103. Also, the height of the peak of the contour graph shown in FIG. 34 indicates the three-dimensional information of the minute foreign objects in the object to be inspected.

[0074] When defects such as minute foreign matters or pinholes are detected, the pass / fail determination unit 60 analyzes the three-dimensional information of the defects. From the analysis result, the apparatus determines whether the defects exist on the same encapsulation layer. If the defects exist on the same encapsulation layer, the organic EL display device as the object is determined to be a non-defective product. Then, the apparatus determines whether a plurality of defects exist at different heights (in the thickness direction). If a plurality of defects exist at different heights (in the thickness direction), the object is determined to be a defective product. As described above, over time, oxygen and water that have penetrated from the defects existing on the organic film penetrate into the organic film. Further, when oxygen and water reach the defects under the organic film, the measurement object (for example, an EL display device) directly below the defects is destroyed. As a result, the life of the measurement object is shortened.

[0075] Furthermore, means for repairing defects may be added to the defect pass / fail determination apparatus of the present invention. For example, in the manufacture of a thin-film multilayer device such as an organic EL display device, the defect pass / fail determination apparatus of the present invention can identify a layer having a defect with a diameter of 1 μm or less (for example, a pinhole or a foreign matter), which cannot be determined by the conventional technique. Therefore, by using the defect pass / fail determination apparatus of the present invention, an optimal repair means can be selected according to the material of the encapsulation film forming the layer where the defect exists. When the defect is a foreign matter in the organic film, the foreign matter can be removed by using a laser, and then the film can be repaired. Further, by selecting the wavelength [nm] and energy density [J / cm 2 of the laser light, optimal repair can be performed. When a laser beam cannot be used, a repair method of pushing the foreign matter downward can be selected. When the defect is a pinhole, the following method can be employed. The method is to apply a minute amount of film material to the defective pinhole using a cartridge loaded with a fine tip processing tube (micro dispenser), and then cure it by firing or ultraviolet irradiation.

[0076] In this embodiment, it has been shown that one image number with the largest number of edge pixels of the pattern is detected, and the image number with the largest number of foreign object pixels and the image number with the second largest number of foreign object pixels (including the case where the number of foreign object pixels is the same as the largest) are detected. However, the present invention is not limited to the above embodiment. For example, appropriate changes can be made according to the organic film, inorganic film, and electrode pattern constituting the sample, or according to the size, density, generation position, and the like of foreign objects and defects.

[0077] The edge evaluation value E(i, j) and the foreign object evaluation value F(i, j) can be stored in the edge evaluation value storage value EM(i, j). Which of the edge evaluation value E(i, j) and the foreign object evaluation value F(i, j) is prioritized may be determined based on, for example, the sizes of the edge evaluation value E(i, j) and the foreign object evaluation value F(i, j) and the sharpness of the partial image.

[0078] In the defect quality determination method and apparatus according to the present invention, in the imaging process, a horizontal position error occurs between the images acquired due to the vibration of the apparatus itself or the floor. By detecting the image number with the highest sharpness, it is possible to accurately measure the height information where minute defects occur without being affected by the horizontal position error. Then, the vibration resistance characteristics of the defect quality determination apparatus can be improved, and the above processing greatly contributes to the cost reduction of the apparatus.

[0079] In the above-described embodiment, an example in which the height measurement of the edge pixels of the pattern and the height measurement of the minute foreign matter are independently performed has been shown. However, when the edge evaluation value of the pattern and the minute foreign matter evaluation value are converted to values of the same order of magnitude, the processing from the edge evaluation value comparison unit 43 to the edge three-dimensional information extraction unit 46 and the processing from the foreign matter evaluation value comparison unit 53 to the foreign matter three-dimensional information extraction unit 56 can be treated as common processing. By such sharing of the processing units, the defect determination device of the present invention can be simplified. As such an example, a feature extraction unit integrating the pattern edge extraction unit 41 and the minute foreign matter extraction unit 51, an evaluation value calculation unit integrating the edge evaluation value calculation unit 42 and the foreign matter evaluation value calculation unit 52, an evaluation value comparison unit integrating the edge evaluation value comparison unit 43 and the foreign matter evaluation value comparison unit 53, an evaluation value storage unit integrating the edge evaluation value storage unit 44 and the foreign matter evaluation value storage unit 54, an image number storage unit integrating the edge image number storage unit 45 and the foreign matter image number storage unit 55, and a three-dimensional information extraction unit integrating the edge three-dimensional information extraction unit 46 and the foreign matter three-dimensional information extraction unit 56 are configured, whereby the configuration of the defect determination device of the present invention can be simplified. The edge processing unit 40, the foreign matter processing unit 50, and the pass / fail determination unit 60 can be executed by software processing except for the storage unit.

[0080] [Alternative Embodiment of the Present Invention] FIG. 35 is a schematic side view of an inspection apparatus 500 that maps features within a sample 502 according to an embodiment of the present invention. The apparatus 500 operates based on the same principle as the above-described embodiment with the following additional and modified features. As described in the foregoing embodiments, for example, as shown in FIGS. 1 and 3, the sample 502 typically includes a plurality of thin film layers including a transparent layer, which are overlaid on the surface of the sample.

[0081] Apparatus 500 includes a video camera 506 that captures an electronic image of sample 502 through a lens 508, typically a microscope lens having high magnification, high numerical aperture, and shallow depth of field. While camera 506 captures an image, illumination source 504 illuminates sample 502. In this embodiment, illumination source 504 emits monochromatic light, i.e., light having a bandwidth (full width at half maximum) of 40 nm or less. Such enhanced monochromatic illumination is advantageous for eliminating the effect of chromatic aberration in the image captured by camera 506. To enhance the contrast of image features, it is also advantageous for illumination source 504 to illuminate sample 506 in dark field mode. However, alternatively, or additionally, illumination source 504 may emit white light or other broadband light and may provide bright field illumination.

[0082] Motor 510 scans the front focal plane of camera 506 in a direction perpendicular to the surface of sample 502. The scanning may be continuous or stepped. In the illustrated embodiment, motor 510 translates camera 506 and lens 508 vertically in parallel. Alternatively, or additionally, the motor can shift the vertical position of sample 502 or adjust the focus setting of lens 508 to scan the focal plane. In the coarse scan, camera 506 captures a series of images of the thin film layer on sample 502 at different depths of focus within the sample. As a result, features located at different depths within the sample are in focus in a series of different images, where the sharpest focus occurs when the front focal plane of the camera coincides with the location of the feature. For features that extend over a range in the depth dimension (i.e., the dimension perpendicular to the surface of sample 502), the upper end of the feature is in sharp focus in one image and the lower end is in sharp focus in another image.

[0083] Processor 512 processes a series of images captured by camera 506 over the scanning course of motor 510 in order to identify features of interest within the images. Such features can include, for example, defects within the thin film layer as described above. Processor 512 generally includes a general-purpose computer processor, has a suitable interface for receiving the electronic images from camera 506 and signals from other components of apparatus 500, is programmed with software, and executes the functions described herein. Alternatively or additionally, at least some of the functions of processor 512 may be implemented with programmable or hard-wired logic. Once a feature of interest is identified, processor 512 calculates the optimal optical depth of focus of that feature within the series of images, and then estimates the position of that feature within the thin film layer, particularly the position in the depth (vertical) dimension. For this purpose, as previously described in detail, processor 512 calculates a measure of the sharpness of the edges of the feature within the image and finds the depth that maximizes the sharpness.

[0084] In this embodiment, apparatus 500 includes a rangefinder including laser 514 and detector 516 for measuring the distance between camera 506 and sample 502. The illustrated rangefinder operates by sensing the shift in the position of the laser spot reflected from sample 502 onto detector 516 as the distance between the camera and the sample changes. Alternatively, other types of rangefinders known in the art, such as ultrasonic rangefinders or interferometric rangefinders, can be used. Processor 512 applies the distance measured by the rangefinder when estimating the position of the feature of interest, and more particularly corrects for variations in the position of the front focal plane of camera 506 within the thin film layer on sample 502 that can occur due to, for example, vibrations of the sample. Processor 512 is capable of detecting this vibration based on periodic changes in the distance measured by the rangefinder over time, and then, within the captured images, is capable of correcting the depth measurements to compensate for this vibration, and thus estimate the position of the features of sample 502 with higher accuracy.

[0085] FIG. 36 is a plot schematically showing vibrations measured in an apparatus 500 according to an embodiment of the present invention. A plurality of data points 520 within the plot show the relative height (in microns) with respect to the reference line height of the camera 506 above the sample 502 as a function of time (in seconds), assuming no vibration. Each data point 520 corresponds to a reading taken by the rangefinder detector 516. The processor 512 fits a periodic function to the data points 520 to generate a curve 522, which gives the estimated amplitude of vibration at any given point in time. The camera 506 captures an image at the times indicated by a plurality of marks 524 on the curve 522. At each such time, the processor 512 reads the value of the curve 522 to provide a height correction and adds (or subtracts) this correction value to the nominal depth provided by the scanning of the motor 510 to calculate the corrected depth of focus. Thus, the processor 512 can compensate for vibrations of the sample 502 and more accurately estimate the position of features appearing in the image.

[0086] FIG. 37 is a schematic plot showing measurement values of focus quality obtained by an apparatus 500 according to an embodiment of the present invention. The data points 530 correspond to focus scores calculated for a given feature and are plotted as a function of the depth of focus of the camera 506 within the thin film layer on the sample 502. The nominal depth of focus may be corrected for vibrations of the sample as described above. The focus score measures the sharpness of the edges of the feature of interest, for example based on a derived image. The Z position of the data points 530 is corrected for the measured vibrations and may thus be unevenly spaced within the plot. The focus score exhibits an inverted parabolic shape as a function of the depth of the front focal plane of the camera 506. Thus, the processor 512 fits a suitable curve 532 to the data points 530 and finds the peak of the curve 532, which indicates the depth of the feature within the sample 502.

[0087] FIG. 38 is a flowchart schematically showing a method of mapping features in a sample according to an embodiment of the present invention. For convenience and clarity, the method will be described below with reference to the features of the apparatus 500 (see FIG. 35). Alternatively, the method will be apparent to those skilled in the art after reading this specification and, with the necessary modifications, may be implemented using the apparatus of the foregoing embodiment or any other suitable inspection system.

[0088] In distance measurement step 540, the processor 512 uses a distance meter such as the laser 514 or the sensor 516 to measure the distance from the sample 502 to the camera 506 and the lens 508. Generally, in scanning step 542, the apparatus 500 is configured such that this distance remains substantially constant (except for slight movements due to vibrations) while the motor 510 scans the depth of the front focal plane of the camera through the thin film layer on the sample 502. Alternatively, in this step, the distance meter may measure the shift caused by the operation of the motor 510. As the motor 510 scans the focus of the camera in the depth dimension, the processor 512 acquires an image of the sample 502 from the camera 506.

[0089] Based on the distance measurement performed in step 540, as shown in FIG. 36, for example, in vibration reconstruction step 544, the processor 512 reconstructs the vibration pattern of the sample 502. Next, in depth correction step 546, the processor 512 can correct the nominal focus depth of the image acquired in step 542 to compensate for the error caused by vibrations. The processor 512 identifies one or more features of interest in the image, such as potential defects, and in focus scoring step 548, calculates a focus score for these features as a function of the corrected depth. In position calculation step 550, for each such feature, the processor 512 fits a curve to the calculated focus score and then finds the three-dimensional coordinates of the feature.

[0090] It should be understood that the above-described embodiments are cited as examples, and the present invention is not limited to those specifically identified and described above. Rather, the scope of the present invention includes various combinations and partial combinations of the functions described above, as well as these variations and modifications that will be found by those skilled in the art upon reading the above description and that are not disclosed in the prior art.

Explanation of Reference Numerals

[0091] 1 Electric circuit pattern 2 First sealing layer 3 Second sealing layer 4 Third sealing layer 5 First base material 6 Second base material 10 Flexible organic EL display device 20 Microscope (barrel part) 21 Objective lens 22 Camera 23 Height direction drive motor 30 Sequence control unit 31 Image acquisition unit 32 Image memory 40 Edge processing unit 41 Pattern edge extraction unit 42 Edge evaluation value calculation unit 43 Edge evaluation value comparison unit 44 Edge evaluation value storage unit 45 Edge image number storage unit 46 Edge three-dimensional information extraction unit 50 Foreign object processing unit 51 Micro foreign object extraction unit 52 Foreign object value calculation unit 53 Foreign object value comparison unit 54 Foreign object value storage unit 55 Foreign object image number storage unit 56 Foreign object three-dimensional information extraction unit 60 Pass / fail determination unit 101 First micro foreign object 102 Second micro foreign object 103 Electrode pattern 104 Cathode electrode 105 Organic film 106 Transparent film 107 Light-emitting layer 500 Inspection device 502 Sample 504 Illumination light source 506 Camera 508 Microscope lens 510 Focus adjustment motor 512 Processor 514 Distance measuring laser 516 Distance measuring detector

Claims

1. For an object to be inspected including a multilayer transparent thin film, acquiring, by optical imaging means, a plurality of images with a predetermined step width in the height direction; Calculating, for each pixel of the plurality of images, the sharpness of a partial image from the luminance difference with an adjacent pixel, wherein the partial image is composed of each pixel of the plurality of images, pixels adjacent to each pixel in the horizontal direction, and pixels adjacent to each pixel in the vertical direction; Calculating the height information of the partial image from the image number with the maximum calculation result of the sharpness at the same pixel position among all images of the plurality of images; Obtaining three-dimensional information of the entire image from the calculation of the height information, wherein the three-dimensional information includes at least one of edge three-dimensional information of the entire image and foreign object three-dimensional information of the entire image; and Including determining the defect acceptability of the object to be inspected based on the edge three-dimensional information and the foreign object three-dimensional information, The object to be inspected is an organic EL device. The edge of the electrode pattern of the organic EL device is used as a reference for the height of the three-dimensional information. When the edge of the electrode pattern is difficult to detect, the edge of the light-emitting part of the organic EL device is used as a reference for the height of the three-dimensional information. Defect acceptability determination method.

2. Detecting a pattern defect of the image with the maximum sharpness; Extracting, from the plurality of images, an image with the maximum density of partial images with high sharpness; Setting the image as a reference position 1 in the height direction of the three-dimensional pattern structure; and Further including measuring the height of the generated pattern defect in the three-dimensional pattern structure from the relationship between the height information of the pattern defect and the reference position 1. The defect acceptability determination method according to Claim 1.

3. Detecting a pattern defect of the image with the maximum sharpness; Extracting, from the plurality of images, an image in which the interference image of interference fringes generated at the edge of the transparent thin film is the sharpest; Setting the image as a reference position 2 in the height direction of the three-dimensional pattern structure; and Further including measuring the height of the generated pattern defect in the three-dimensional pattern structure from the relationship between the height information of the pattern defect and the reference position 2. The defect acceptability determination method according to Claim 1.

4. The defect pass / fail determination method according to claim 2 or 3, further comprising the step of repairing the pattern defect using the height information of the pattern defect.

5. An imaging means for acquiring a plurality of image data of an inspection object having a multilayer transparent thin film with an image number attached by an optical imaging means capable of moving up and down with a predetermined step width, An extraction unit for extracting features of the image data, An evaluation value calculation unit for calculating an evaluation value based on the features, wherein the evaluation value represents a luminance difference between a target pixel and an adjacent pixel adjacent to the target pixel, An evaluation value comparison unit that compares the previous evaluation value whose positional relationship matches with the evaluation value in the image data and generates a comparison result, An evaluation value storage unit for storing the evaluation value based on the comparison result, An image number storage unit for storing the image number based on the comparison result, An edge three-dimensional information extraction unit for extracting the edge three-dimensional information of the inspection object based on the image number stored in the image number storage unit, A foreign matter three-dimensional information extraction unit for extracting the height information of a defect existing in the inspection object based on the edge three-dimensional information, and A pass / fail determination unit for determining the pass / fail of the inspection object based on the difference in the height information between the defects when a plurality of the defects exist, A defect pass / fail determination device comprising: the inspection object is an organic EL device, and the edge of the electrode pattern of the organic EL device is used as a reference for the height of the edge three-dimensional information, and when the edge of the electrode pattern is difficult to detect, the edge of the light-emitting portion of the organic EL device is used as a reference for the height of the edge three-dimensional information.

6. The defect pass / fail determination device according to claim 5, wherein the edge three-dimensional information extraction unit extracts the edge three-dimensional information based on the image number with the highest evaluation value.

7. The defect pass / fail determination device according to claim 5 or 6, wherein the evaluation value is calculated based on a luminance difference between a target pixel and an adjacent pixel adjacent to the target pixel.

8. The defect pass / fail determination device according to any one of claims 5 to 7, wherein the foreign matter three-dimensional information extraction unit determines a reference for the height information based on the evaluation value of the image data in which interference fringes of the electrode pattern of the inspection object and the sealing layer of the inspection object are photographed.

9. The defect pass / fail determination device according to any one of claims 5 to 8, wherein the defect is a pattern defect, a pinhole, or a foreign matter.

10. The defect pass / fail determination device according to any one of claims 7 to 9, wherein the evaluation value is sharpness calculated based on a difference between the luminance value of the target pixel and the luminance values of the adjacent pixels.

11. The defect pass / fail determination device according to any one of claims 5 to 10, wherein the object to be inspected is an organic EL display device.

12. The defect pass / fail determination device according to any one of claims 5 to 10, wherein the object to be inspected is a flexible organic EL display device formed on a flexible substrate.

13. The defect pass / fail determination device according to any one of claims 5 to 12, further comprising at least one function of repairing the defect based on the height information calculated by the foreign object three-dimensional information extraction unit.

14. The defect pass / fail determination device according to claim 13, further including a function of selecting the function according to the height information.

15. A camera configured to capture an image of a sample including a plurality of thin film layers overlaid on its surface, a motor coupled to scan the front focal plane of the camera in a direction perpendicular to the surface of the sample, thereby capturing a series of images of the thin film layers at different focal depths within the sample, and a processor configured to process the series of images to process the images within the series, identify features of interest in the images, calculate the optimal focal depth of the features of interest in the series of images, and estimate the position of the features of interest, comprising: The processor is further configured to include calculating the sharpness of the edges of the feature of interest in calculating the optimal focal depth of the feature of interest and finding the depth that maximizes the sharpness. The sample is an organic EL device, and the edge of the electrode pattern of the organic EL device is used as a reference for the height of the three-dimensional information. When the edge of the electrode pattern is difficult to detect, the edge of the light-emitting portion of the organic EL device is used as a reference for the height of the feature of interest. Inspection device.

16. The device according to claim 15, comprising an illumination source configured to illuminate the sample with monochromatic light while the camera captures the image.

17. The device according to claim 16, wherein the illumination source is configured to illuminate the sample in a dark field mode.

18. The apparatus according to claim 15, comprising a distance meter configured to measure the distance between the camera and the sample, wherein the processor is configured to use the measured distance when estimating the position of the feature of interest.

19. The apparatus according to claim 18, wherein the processor is configured to detect vibrations of the sample with respect to the camera based on periodic changes in the measured distance over time, and to correct the depth of focus of the captured image to compensate for the detected vibrations.

20. Capturing a series of images of a sample, including a plurality of thin film layers overlaid on the surface of the sample at different respective depths of focus within the sample, identifying a feature of interest in the image, calculating an optimal depth of focus of the feature of interest in the series of images, and estimating the position of the feature of interest in the thin film layer based on the optimal depth of focus, including the calculation of the optimal depth of focus includes calculating the sharpness of the edge of the feature of interest and finding the depth that maximizes the sharpness, the sample is an organic EL device, the edge of the electrode pattern of the organic EL device is used as a reference for the height of the three-dimensional information, and when the edge of the electrode pattern is difficult to detect, the edge of the light-emitting portion of the organic EL device is used as a reference for the height of the feature of interest, Inspection method.

21. The method according to claim 20, wherein capturing the series of images includes illuminating the sample with monochromatic light while the image is being captured.

22. The method according to claim 21, wherein illuminating the sample includes directing light at the sample in a dark field mode.

23. The method according to claim 20, wherein capturing the series of images includes scanning the front focal plane of the camera in a direction perpendicular to the surface of the sample, whereby the camera captures the series of images of the thin film layer at the different depths of focus.

24. The method according to claim 23, wherein calculating the optimal depth of focus includes measuring the distance between the camera and the sample and applying the measured distance when estimating the position of the feature of interest.

25. Applying the measured distance includes detecting vibration of the sample with respect to the camera based on periodic changes in the measured distance over time, and correcting the depth of focus of the captured image to compensate for the detected vibration, the method according to claim 24.

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