Image processing device, inspection device, review apparatus, image processing method, inspection method, and review method

Abstract

To provide an image processing device, a review apparatus, an image processing method, and a review method, which can perform highly accurate image processing.SOLUTION: An image processing device 40 includes: an evaluation parameter acquisition section 41 that acquires an evaluation parameter as information about each pixel of a plurality of pixels included in a comparison image on the basis of information about each pixel of a plurality of pixels included in a reference image 100 and information about each pixel of a plurality of pixels included in an evaluation image 200; a statistical value acquisition section 42 that acquires a statistical value of the evaluation parameters for a plurality of predetermined specific pixels in the comparison image; a standardized evaluation parameter acquisition section 43 that acquires a standardized evaluation parameter as information about each pixel of a plurality of pixels included in a standardized image 400 by standardizing the evaluation parameter of each pixel of the plurality of pixels included in the comparison image on the basis of the statistical value; and an evaluation section 45 that evaluates the standardized evaluation parameter.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to an image processing apparatus, an inspection apparatus, a review apparatus, an image processing method, an inspection method, and a review method.

Background Art

[0002] Patent Document 1 describes an example in which a defect of a sample is detected by comparing a reference image and an inspection image of the sample.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] For example, highly accurate image processing in which noise is flattened is desired by inspection or the like that takes into account the magnitude of inspection noise due to differences in luminance and patterns.

[0005] The present disclosure has been made in view of such problems, and provides an image processing apparatus, an inspection apparatus, a review apparatus, an image processing method, an inspection method, and a review method capable of performing highly accurate image processing.

Means for Solving the Problems

[0006] An image processing apparatus according to an aspect of the present embodiment acquires, as information of each pixel among a plurality of pixels included in a comparison image corresponding to the reference image and the evaluation image, an evaluation parameter based on information of each pixel among a plurality of pixels included in the reference image and information of each pixel among a plurality of pixels included in an evaluation image corresponding to the reference image, an evaluation parameter acquisition unit that acquires an evaluation parameter, a statistical value acquisition unit that acquires a statistical value of the evaluation parameter for a predetermined plurality of specific pixels in the comparison image, and based on the statistical value, by normalizing the evaluation parameter of each pixel among the plurality of pixels included in the comparison image, a normalized evaluation parameter acquisition unit that acquires, as information of each pixel among the plurality of pixels included in a normalized image corresponding to the comparison image, a normalized evaluation parameter, and an evaluation unit that evaluates the normalized evaluation parameter.

[0007] An image processing apparatus according to an aspect of the present embodiment acquires, as information of each pixel among a plurality of pixels included in the evaluation image, an evaluation parameter for evaluating the information of each pixel among the plurality of pixels included in the evaluation image based on a comparison between the reference image and the evaluation image, based on information of each pixel among a plurality of pixels included in the reference image and information of each pixel among a plurality of pixels included in an evaluation image corresponding to the reference image, an evaluation parameter acquisition unit that acquires an evaluation parameter, a statistical value acquisition unit that acquires a statistical value of the evaluation parameter for a predetermined plurality of specific pixels in the evaluation image, and based on the statistical value, by normalizing the evaluation parameter, a normalized evaluation parameter acquisition unit that acquires a normalized evaluation parameter for a plurality of the pixels included in the evaluation image, and an evaluation unit that evaluates the normalized evaluation parameter.

[0008] In the above image processing apparatus, the evaluation parameter acquisition unit may acquire, as the evaluation parameter of each pixel, a difference value obtained by comparing the luminance of each pixel in the reference image with the luminance of each pixel in the evaluation image.

[0009] In the above-described image processing apparatus, the specific pixel may include at least any one of the pixels whose luminance of the pixels included in the reference image belongs to a predetermined luminance range and the pixels whose luminance of the pixels included in the evaluation image belongs to the predetermined luminance range.

[0010] In the above-described image processing apparatus, the statistical value acquisition unit acquires a specific statistical value of the evaluation parameter for the specific pixel including the pixels belonging to the luminance range, and the standardized evaluation parameter acquisition unit may acquire the standardized evaluation parameter by standardizing the evaluation parameter of the specific pixel based on the specific statistical value.

[0011] In the above-described image processing apparatus, the statistical value acquisition unit divides the luminance range into a plurality of luminance range portions, divides the specific pixel into a plurality of specific pixel portions belonging to each luminance range portion, acquires a specific statistical value portion which is a statistical value of the evaluation parameter for each specific pixel portion for each specific pixel portion, and the standardized evaluation parameter acquisition unit may acquire the standardized evaluation parameter for each pixel corresponding to the specific pixel portion by standardizing the evaluation parameter of each specific pixel portion based on each specific statistical value portion.

[0012] In the above-described image processing apparatus, at least any one of the reference image and the evaluation image includes an image of a sample having a pattern, and the specific pixel may include at least any one of the pixels belonging to a specific region classified based on the pattern of the reference image and the pixels belonging to the specific region classified based on the pattern of the evaluation image.

[0013] In the above-described image processing apparatus, the statistical value acquisition unit acquires a specific statistical value of the evaluation parameter for the specific pixel including the pixels belonging to the specific region, and the standardized evaluation parameter acquisition unit may acquire the standardized evaluation parameter by standardizing the evaluation parameter of the pixel based on the specific statistical value.

[0014] In the above-described image processing apparatus, the statistical value acquisition unit divides the specific region into a plurality of specific region portions, divides the specific pixel into a plurality of specific pixel portions belonging to each specific region portion, and acquires a specific statistical value portion, which is a statistical value of the evaluation parameter for each specific pixel portion, for each specific pixel portion. The standardized evaluation parameter acquisition unit may acquire the standardized evaluation parameter for each pixel corresponding to the specific pixel portion by standardizing the evaluation parameter of each specific pixel portion based on each specific statistical value portion.

[0015] In the above-described image processing apparatus, the evaluation unit may perform at least any one of the following: evaluation of the presence or absence of an error in the subject sample of the evaluation image by comparing the standardized evaluation parameter with a predetermined threshold value; evaluation of the pixel of the correction mode to be performed on the evaluation image based on the standardized evaluation parameter; and evaluation of the presence or absence of an error in the corrected image obtained by correcting the evaluation image based on the standardized evaluation parameter.

[0016] The above-described image processing apparatus further includes a processing unit that acquires a processing value by arithmetic processing based on the standardized evaluation parameter and the evaluation parameter, and the evaluation unit may evaluate the processing value.

[0017] An inspection apparatus according to one aspect of the present embodiment includes the above-described image processing apparatus and inspects the subject sample of the evaluation image.

[0018] A review apparatus according to one aspect of the present embodiment includes the above-described image processing apparatus and a monitor that reviews the reference image, the evaluation image, the comparison image, and the standardized image.

[0019] An image processing method according to an aspect of the present embodiment includes: obtaining evaluation parameters as information of each pixel in a plurality of pixels included in a comparison image corresponding to the reference image and the evaluation image, based on information of each pixel in a plurality of pixels included in the reference image and information of each pixel in a plurality of pixels included in an evaluation image corresponding to the reference image; obtaining a statistical value of the evaluation parameters for a predetermined plurality of specific pixels in the comparison image; obtaining standardized evaluation parameters as information of each pixel in a plurality of pixels included in a standardized image corresponding to the comparison image by standardizing the evaluation parameters for each pixel in the plurality of pixels included in the comparison image based on the statistical value; and evaluating the standardized evaluation parameters.

[0020] An image processing method according to an aspect of the present embodiment includes: obtaining evaluation parameters for evaluating information of each pixel in a plurality of pixels included in the evaluation image based on a comparison between the reference image and the evaluation image, based on information of each pixel in a plurality of pixels included in the reference image and information of each pixel in a plurality of pixels included in an evaluation image corresponding to the reference image; obtaining a statistical value of the evaluation parameters for a predetermined plurality of specific pixels in the evaluation image; obtaining standardized evaluation parameters for a plurality of pixels included in the evaluation image by standardizing the evaluation parameters based on the statistical value; and evaluating the standardized evaluation parameters.

[0021] An inspection method according to an aspect of the present embodiment includes the above-described image processing method and inspecting a subject sample of the evaluation image.

[0022] A review method according to an aspect of the present embodiment includes the above-described image processing method and reviewing the reference image, the evaluation image, the comparison image, and the standardized image on a monitor.

Advantages of the Invention

[0023] According to the present disclosure, it is possible to provide an image processing apparatus, an inspection apparatus, a review apparatus, an image processing method, an inspection method, and a review method capable of performing high-precision image processing.

Brief Description of the Drawings

[0024]

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Mode for Carrying Out the Invention

[0025] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. The following description shows preferred embodiments of the present disclosure, and the scope of the present disclosure is not limited to the following embodiments. In the following description, those denoted by the same reference numerals indicate substantially the same content.

[0026] (Embodiment 1) An image processing apparatus and an image processing method according to Embodiment 1 will be described. The image processing apparatus of the present embodiment may be applied to a review apparatus that displays an image of a sample, in addition to an inspection apparatus that inspects defects and the like in a sample. That is, the image processing apparatus of the present embodiment may be connected to the inspection apparatus or incorporated into the inspection apparatus. Further, the image processing apparatus of the present embodiment may be connected to the review apparatus or incorporated into the review apparatus. The inspection apparatus may include an image correction apparatus, a review apparatus, a storage apparatus, and the like. The review apparatus may include an image correction apparatus, an inspection apparatus, a storage apparatus, and the like. The image processing apparatus 40 performs image processing on images such as an image input to the image correction apparatus, an image detected by the inspection apparatus, and an image stored in the storage apparatus.

[0027] FIGS. 1 to 3 are plan views illustrating images processed in the image processing apparatus according to Embodiment 1. FIG. 1 shows a reference image 100, FIG. 2 shows an evaluation image 200, and FIG. 3 shows a difference image 300 as a comparison image. As shown in FIGS. 1 to 3, the image processing apparatus performs image processing on the reference image 100, the evaluation image 200, and the difference image 300 as the comparison image.

[0028] <Reference Image> The reference image 100 includes an image that serves as a reference when inspecting a sample to be imaged from an image and when correcting an image. The reference image 100 may be, for example, an image of a reference sample, or an image of a predetermined region of a sample that is the subject of review and inspection. Further, the reference image 100 may be a reference image based on design data. For example, when inspecting by comparing one mask with another mask, the reference image 100 may be an image of one mask. Also, for example, when inspecting by comparing the k-th die and the (k + 1)-th die of a mask in which a plurality of dies including the first die to the n-th die are formed, the reference image 100 may be an image including the k-th die.

[0029] The reference image 100 may be a partial region of an image larger than the reference image 100 including the reference image 100. For example, the portion of the k-th die of a mask image obtained by imaging a plurality of dies including the first die to the n-th die may be the reference image 100.

[0030] The reference image 100 includes, for example, a plurality of pixels arranged in a matrix. In the reference image 100, one direction in which the pixels are arranged is defined as the α-axis direction, and a direction intersecting the α direction is defined as the β-axis direction. The reference image 100 may include a plurality of pixels arranged in L rows in the α-axis direction and M columns in the β-axis direction. Each pixel of the reference image 100 has pixel information. The information of each pixel in the reference image 100 includes, for example, the luminance of the pixel. Note that the term "each pixel" may mean all the pixels constituting the image, or may mean a part of all the pixels (but a plurality of pixels) constituting the image. In this specification, unless otherwise specified, the expression "each pixel" is used in either meaning.

[0031] <Evaluation Image> The evaluation image 200 includes an image that is the target when inspecting a sample to be imaged from an image and when correcting an image, etc. The evaluation image 200 may be, for example, an image obtained by imaging a target sample, or an image obtained by imaging a predetermined region of a sample that is the target of review and inspection, etc. Further, the evaluation image 200 may be an image that is the target based on design data. For example, when inspecting by comparing one mask with another mask, the evaluation image 200 may be an image of the other mask. Also, for example, when inspecting by comparing the k-th die and the (k + 1)-th die of a mask in which a plurality of dies including the first die to the n-th die are formed, the evaluation image 200 may be an image including the (k + 1)-th die.

[0032] The evaluation image 200 may be a partial region of an image larger than the evaluation image 200 that includes the evaluation image 200. For example, the portion of the (k + 1)-th die of a mask image obtained by imaging a plurality of dies including the first die to the n-th die may be the evaluation image 200.

[0033] The evaluation image 200 includes, for example, a plurality of pixels arranged in a matrix. The evaluation image 200 may include a plurality of pixels arranged in L rows in the α-axis direction and M columns in the β-axis direction. Each pixel of the evaluation image 200 has pixel information. The information of each pixel in the evaluation image 200 includes, for example, the luminance of the pixel.

[0034] The evaluation image 200 corresponds to the reference image 100. For example, each pixel in the evaluation image 200 including a plurality of pixels corresponds to each pixel in the reference image 100 including a plurality of pixels. That is, each pixel in the evaluation image 200 can be compared with each pixel in the reference image 100. Specifically, for example, both the reference image 100 and the evaluation image 200 include a plurality of pixels arranged in a matrix with L rows in the α-axis direction and M rows in the β-axis direction. Note that, for associating each pixel in the evaluation image 200 with each pixel in the reference image 100, known means such as alignment using an alignment mark can be used. A plurality of pixels in a predetermined region of the reference image 100 may correspond to a plurality of pixels in a predetermined region of the evaluation image 200. Information of each pixel in the evaluation image 200 can be calculated with information of each pixel in the reference image 100. For example, a difference value between the luminance of each pixel in the evaluation image 200 and the luminance of each pixel in the reference image 100 can be calculated.

[0035] <Comparison Image (Difference Image)> The comparison image includes, for example, a plurality of pixels arranged in a matrix. The comparison image corresponds to the reference image 100 and the evaluation image 200. The comparison image may include a plurality of pixels arranged with L rows in the α-axis direction and M columns in the β-axis direction, similar to the reference image 100 and the evaluation image 200. Each pixel in the comparison image has pixel information. For example, each pixel in the comparison image may have an evaluation parameter as the pixel information. The evaluation parameter is obtained based on the information of each pixel in the evaluation image 200 and the information of each pixel in the reference image 100.

[0036] The comparison image corresponds to the reference image 100 and the evaluation image 200. For example, each pixel in the comparison image including a plurality of pixels corresponds to each pixel in the reference image 100 including a plurality of pixels and each pixel in the evaluation image 200 including a plurality of pixels.

[0037] The comparison image may include a difference image 300. The difference image 300 includes a plurality of pixels arranged in a matrix. The difference image 300 corresponds to the reference image 100 and the evaluation image 200. Similar to the comparison image, the difference image 300 may include a plurality of pixels arranged in L rows in the α-axis direction and M columns in the β-axis direction. Each pixel in the difference image 300 has pixel information. Each pixel in the difference image 300 has a difference value as the pixel information. The difference value is the difference between the luminance of each pixel in the evaluation image 200 and the luminance of each pixel in the reference image 100. Therefore, the evaluation parameter of each pixel in the difference image 300 (comparison image) includes the difference value between the luminance of each pixel in the evaluation image 200 and the luminance of each pixel in the reference image 100.

[0038] Note that the comparison image is not limited to the difference image 300, and the evaluation parameter is not limited to the difference value. For example, the evaluation parameter of each pixel in the comparison image may include the ratio between the luminance of each pixel in the evaluation image 200 and the luminance of each pixel in the reference image 100. Note that the comparison image may be virtual for the convenience of explaining the process for inspecting the sample to be imaged from the evaluation image 200 or the process for correcting the evaluation image 200. It is not essential for the comparison image to be displayed on a display device or the like, nor is it essential for the comparison image to be generated as an image. Therefore, the evaluation parameter of the pixels included in the comparison image may simply be referred to as "the evaluation parameter for a plurality of pixels included in the evaluation image 200 based on the comparison between the reference image 100 and the evaluation image 200 (the evaluation parameter for evaluating the information of a plurality of pixels included in the evaluation image 200)" or simply "the evaluation parameter for a plurality of pixels included in the evaluation image 200". Also, "the evaluation parameter for a plurality of pixels included in the evaluation image 200 based on the comparison between the reference image 100 and the evaluation image 200" or "the evaluation parameter for a plurality of pixels included in the evaluation image 200" may be described as the information (evaluation parameter) of the pixels included in the comparison image for convenience.

[0039] <Image Processing Apparatus> FIG. 4 is a block diagram illustrating the image processing apparatus 40 according to Embodiment 1. As shown in FIG. 1, the image processing apparatus 40 includes an evaluation parameter acquisition unit 41, a statistical value acquisition unit 42, a standardized evaluation parameter acquisition unit 43, and an evaluation unit 45. The evaluation parameter acquisition unit 41, the statistical value acquisition unit 42, the standardized evaluation parameter acquisition unit 43, and the evaluation unit 45 each have functions as an evaluation parameter acquisition means, a statistical value acquisition means, a standardized evaluation parameter acquisition means, and an evaluation means, respectively.

[0040] FIG. 5 is a block diagram illustrating an image processing apparatus 40a according to another example of Embodiment 1. As shown in FIG. 5, the image processing apparatus 40a may further include a processing unit 44 as compared with the image processing apparatus 40. The processing unit 44 has a function as a processing means.

[0041] <Evaluation parameter acquisition unit> The evaluation parameter acquisition unit 41 acquires evaluation parameters as information on each pixel in a plurality of pixels included in a comparison image based on information on each pixel in a plurality of pixels included in the reference image 100 and information on each pixel in a plurality of pixels included in the evaluation image 200. Here, the evaluation image 200 corresponds to the reference image 100, and the comparison image corresponds to the reference image 100 and the evaluation image 200. That is, the evaluation image 200 includes a plurality of pixels corresponding to a plurality of pixels of the reference image 100. The comparison image includes a plurality of pixels corresponding to a plurality of pixels of the reference image 100 and the evaluation image 200.

[0042] In other words, the evaluation parameter acquisition unit 41 acquires evaluation parameters for a plurality of pixels included in the comparison image obtained based on the reference image 100 and the evaluation image 200 corresponding to the reference image 100, based on the information on the pixels in the reference image 100 and the information on the pixels in the evaluation image 200.

[0043] Specifically, the evaluation parameter acquisition unit 41 obtains a difference value as the evaluation parameter of each pixel in the difference image 300 as a comparison image by comparing the luminance of each pixel in the reference image 100 with the luminance of each pixel in the evaluation image 200. In this way, the evaluation parameter acquisition unit 41 obtains the difference image 300 based on the reference image 100 and the evaluation image 200.

[0044] <Statistical value acquisition unit> The statistical value acquisition unit 42 obtains the statistical value of the evaluation parameters (the evaluation parameters for the information of a plurality of predetermined specific pixels in the evaluation image 200) for a plurality of predetermined specific pixels in the comparison image. Specifically, the statistical value acquisition unit 42 obtains the statistical value of the difference values for a plurality of predetermined specific pixels in the difference image 300 (obtains the statistical value of the difference values for evaluating the luminance of a plurality of predetermined specific pixels in the evaluation image 200).

[0045] The specific pixels in the comparison image (evaluation image 200) include at least any one of the pixels whose luminance of the pixels included in the reference image 100 belongs to a predetermined luminance range and the pixels whose luminance of the pixels included in the evaluation image 200 belongs to a predetermined luminance range. For example, the predetermined luminance range may include a range where the luminance of the pixel is 30 to 150. In this case, the specific pixels include pixels having a luminance in the range of 30 to 150. The statistical value acquisition unit 42 obtains the statistical value of the evaluation parameters for the specific pixels including the pixels belonging to the luminance range of 30 to 150. The statistical value of the evaluation parameters for the specific pixels may be referred to as a specific statistical value.

[0046] Further, the statistical value acquisition unit 42 may divide a predetermined luminance range into a plurality of luminance range portions. Then, the statistical value acquisition unit 42 may divide a specific pixel into a plurality of specific pixel portions belonging to each luminance range portion. The statistical value acquisition unit 42 acquires a specific statistical value portion, which is a statistical value of an evaluation parameter for a specific pixel portion, for each specific pixel portion. Specifically, for example, the statistical value acquisition unit 42 may divide a luminance range of 30 to 150 into a plurality of luminance range portions each including a range of ±1 of each luminance. Thus, in this case, the predetermined luminance range includes a luminance range of 30 to 150. The luminance range portions include 120 luminance range portions with each luminance in the range of 30 to 150 as the central luminance. Each luminance range portion has a luminance width of 3 (central luminance - 1, central luminance, central luminance + 1). Note that the luminance range and the luminance range portions are not limited to the above luminance and may be appropriately selected.

[0047] The statistical value of the evaluation parameter includes the average value of the evaluation parameter and the standard deviation of the evaluation parameter. The average value of the evaluation parameter may include the average value of the evaluation parameter of a specific pixel or the average value of the evaluation parameter of a specific pixel portion. The standard deviation of the evaluation parameter may include the standard deviation of the evaluation parameter of a specific pixel or the standard deviation of the evaluation parameter of a specific pixel portion. The statistical value of the difference value includes the average value of the difference value and the standard deviation of the difference value. The average value of the difference value may include the average value of the difference value of a specific pixel or the average value of the difference value of a specific pixel portion. The standard deviation of the difference value may include the standard deviation of the difference value of a specific pixel or the standard deviation of the difference value of a specific pixel portion.

[0048] FIG. 6 is a graph illustrating the relationship between pixel information and evaluation parameters in the image processing apparatus 40 according to Embodiment 1. The horizontal axis represents the luminance of pixels in the reference image 100, and the vertical axis represents the difference value as the evaluation parameter. Note that the horizontal axis may represent the luminance of pixels in the evaluation image 200. Whether to represent the luminance on the horizontal axis by the luminance of pixels in the reference image 100 or by the luminance of pixels in the evaluation image 200 may be determined based on predetermined conditions. For example, the one in which the difference value such as a defect is more visible may be used between the case where it is represented by the luminance of pixels in the reference image 100 and the case where it is represented by the luminance of pixels in the evaluation image 200. As shown in FIG. 6, the statistical value acquisition unit 42 acquires, for example, the average value Avg of the difference values and the standard deviation σ of the difference values for each luminance range portion where the width of the pixel luminance is in the range of ±1.

[0049] In FIG. 6, threshold values SL1 and SL2 may be set for the evaluation parameter. The range of the threshold values SL1 to SL2 for the evaluation parameter may be regarded as normal, and values smaller than the threshold value SL1 and values larger than the threshold value SL2 for the evaluation parameter may be regarded as abnormal. In this case, in FIG. 6, for example, the point E indicates an abnormality.

[0050] In FIG. 6, there are points smaller than the threshold value SL1 in the region where the luminance of pixels in the reference image 100 is large. For example, as the pattern of the photomask is miniaturized, highly sensitive inspection is required. Due to such an increase in the sensitivity of the inspection, the threshold value for defect detection is approaching the noise level. The noise level is lower in the region where the pixel luminance is small than in the region where the pixel luminance is large. On the other hand, the noise level in the region where the pixel luminance is large becomes larger than that in the region where the luminance is small. Therefore, due to the variation in the noise level, points smaller than the threshold value SL1 are detected as pseudo-defects in the region where the pixel luminance is large. For example, with an increase in the sensitivity of the review apparatus that captures the evaluation image 200, slight defocusing caused by the review apparatus may be detected as a pseudo-defect.

[0051] In order to suppress the detection of such pseudo defects, it is conceivable to set a threshold value for each luminance range portion. For example, the threshold value for the region where the luminance of the pixel is small may be made larger than the threshold value SL1 (the absolute value is made smaller), and the threshold value for the region where the luminance of the pixel is large may be made smaller than the threshold value SL1 (the absolute value is made larger). However, setting a threshold value for each luminance range portion is complicated and it is difficult to make fine settings.

[0052] <Normalized image> FIG. 7 is a plan view illustrating a normalized image 400 processed in the image processing apparatus 40 according to Embodiment 1. As shown in FIG. 7, the normalized image 400 includes, for example, a plurality of pixels arranged in a matrix. The normalized image 400 corresponds to the reference image 100, the evaluation image 200, the comparison image, and the difference image 300. Similar to the reference image 100, the evaluation image 200, the comparison image, and the difference image 300, the normalized image 400 may include a plurality of pixels arranged in L rows in the α-axis direction and M columns in the β-axis direction. Each pixel in the normalized image 400 includes pixel information. Each pixel in the normalized image 400 has a normalized evaluation parameter as the pixel information. The normalized evaluation parameter is obtained by normalizing the evaluation parameter of each pixel in the comparison image. Specifically, the normalized evaluation parameter of each pixel in the normalized image 400 has a normalized difference value obtained by normalizing the difference value of each pixel in the difference image 300.

[0053] Since the standardized image 400 corresponds to the reference image 100, the evaluation image 200, and the comparison image, for example, each pixel in the standardized image 400 including a plurality of pixels may correspond to each pixel in the reference image 100 including a plurality of pixels, each pixel in the evaluation image 200 including a plurality of pixels, and each pixel in the comparison image including a plurality of pixels. Note that the standardized image 400 may be virtual for the convenience of explaining the process for inspecting the sample to be imaged from the evaluation image 200 and the process for image-correcting the evaluation image 200. The standardized image 400 does not necessarily have to be displayed on a display device or the like, nor does it necessarily have to be generated as an image. Therefore, the standardized evaluation parameter for the pixels included in the standardized image 400 may sometimes be simply referred to as "the standardized evaluation parameter obtained by standardizing the evaluation parameter", or sometimes as "the standardized evaluation parameter (the standardized evaluation parameter obtained by standardizing the evaluation parameter for evaluating the information of a plurality of pixels included in the evaluation image 200) obtained by standardizing the evaluation parameter for a plurality of pixels included in the evaluation image 200". Also, "the standardized evaluation parameter obtained by standardizing the evaluation parameter" and "the standardized evaluation parameter obtained by standardizing the evaluation parameter for a plurality of pixels included in the evaluation image 200" can be explained as the information of the pixels included in the standardized image 400 (the standardized evaluation parameter of the pixels included in the standardized image 400) for convenience.

[0054] <Standardized Evaluation Parameter Acquisition Unit> The standardized evaluation parameter acquisition unit 43 acquires standardized evaluation parameters as information on each pixel among the plurality of pixels included in the standardized image 400 by standardizing the evaluation parameters of each pixel among the plurality of pixels included in the comparison image based on statistical values (acquires standardized evaluation parameters by standardizing the evaluation parameters for evaluating the information of each pixel among the plurality of pixels included in the evaluation image 200). That is, the standardized evaluation parameter acquisition unit 43 acquires standardized evaluation parameters obtained by standardizing the evaluation parameters for a plurality of pixels in the comparison image based on statistical values (acquires standardized evaluation parameters obtained by standardizing the evaluation parameters for evaluating the information of a plurality of pixels in the evaluation image 200). Specifically, the standardized evaluation parameter acquisition unit 43 acquires standardized difference values obtained by standardizing the difference values of each pixel in the difference image 300 based on statistical values (acquires standardized difference values obtained by standardizing the difference values for evaluating the luminance of each pixel in the evaluation image 200).

[0055] FIG. 8 is a graph illustrating the relationship between pixel information and standardized evaluation parameters in the image processing apparatus 40 according to Embodiment 1. The horizontal axis represents the luminance of the pixels of the reference image 100, and the vertical axis represents the standardized difference value. As shown in FIG. 8, the standardized evaluation parameter acquisition unit 43 acquires standardized evaluation parameters by standardizing the evaluation parameters of a specific pixel based on a specific statistical value for the specific pixel. Then, the standardized evaluation parameter acquisition unit 43 acquires standardized evaluation parameters as information on each pixel corresponding to the specific partial pixels in the standardized image 400 by standardizing the evaluation parameters of each specific partial pixel based on each specific partial statistical value. Specifically, the standardized evaluation parameter acquisition unit 43 acquires standardized evaluation parameters by the following (1) using, for example, the average value Ave of the evaluation parameters and the standard deviation σ of the evaluation parameters acquired for each specific pixel portion belonging to each luminance range portion with a luminance range of ±1.

[0056] StdPara i =(Para i -Avg i ) / σ i (1)

[0057] Here, StdPara represents the standardized evaluation parameter, Para represents the evaluation parameter, Avg represents the average value of the evaluation parameter, σ represents the standard deviation of the evaluation parameter, and i represents each specific pixel portion. When the evaluation parameter is a difference value, the following formula (2) is obtained.

[0058] StdDiff i =(Diff i -Avg i ) / σ i (2)

[0059] Here, StdDiff represents the standardized difference value, and Diff represents the difference value.

[0060] As shown in FIG. 8, the standardized evaluation parameter acquisition unit 43 expands the range of the difference value in the region where the luminance of the pixel is small by standardization. Therefore, the noise levels with respect to the thresholds SL3 and SL4 in the region where the luminance of the pixel is small and the region where the luminance of the pixel is large can be made equal. Also, by standardization, the more characteristic points that deviate from the average value such as the luminance indicating a defect are emphasized. Therefore, the accuracy of detecting defects and the like can be improved.

[0061] <Processed Image> FIG. 9 is a plan view illustrating a processed image 500 processed in the image processing apparatus 40 according to Embodiment 1. As shown in FIG. 9, the processed image 500 includes, for example, a plurality of pixels arranged in a matrix. The processed image 500 may correspond to the reference image 100, the evaluation image 200, the comparison image, the difference image 300, and the standardized image 400. Similar to the reference image 100 and the evaluation image 200, etc., the processed image 500 may include a plurality of pixels arranged in L rows in the α-axis direction and M columns in the β-axis direction. Each pixel in the processed image 500 has pixel information. Each pixel in the processed image 500 has a processed value obtained based on the standardized evaluation parameter of each pixel in the standardized image 400 and the evaluation parameter of each pixel in the comparison image as the pixel information.

[0062] Since the processed image 500 corresponds to the reference image 100, the evaluation image 200, the comparison image, and the standardized image 400, for example, each pixel in the processed image 500 including a plurality of pixels may correspond to each pixel in the reference image 100, the evaluation image 200, the comparison image, and the standardized image 400 including a plurality of pixels. Note that the processed image may be a virtual one for the convenience of explaining the process for inspecting the sample to be imaged from the evaluation image 200 or the process for image correction of the evaluation image 200. It is not essential for the processed image to be displayed on a display device or the like, nor is it essential for the processed image to be generated as an image. Therefore, the information (processing value) of the pixels included in the processed image may sometimes be simply referred to as the meaning of "the processing value obtained by performing arithmetic processing on the evaluation parameter based on the standardized evaluation parameter", or may be referred to as the meaning of "the processing value obtained by performing arithmetic processing on the evaluation parameter for a plurality of pixels included in the evaluation image 200 using the standardized evaluation parameter (the processing value obtained by performing arithmetic processing on the evaluation parameter for evaluating the information of a plurality of pixels included in the evaluation image 200 using the standardized evaluation parameter)".

[0063] <Processing unit> Based on the standardized evaluation parameter of each pixel in the standardized image 400 and the evaluation parameter of each pixel in the comparison image, the processing unit 44 acquires, as the information of each pixel in the plurality of pixels included in the processed image 500 corresponding to the standardized image 400 and the comparison image, a processing value. Specifically, the processing unit 44 acquires the processing value of each pixel in the processed image 500 based on the standardized difference value of each pixel in the standardized image 400 and the difference value of each pixel in the difference image 300.

[0064] FIG. 10 is a graph illustrating the relationship between the pixel information and the processing value in the image processing apparatus 40 according to Embodiment 1. The horizontal axis represents the luminance of the pixels of the reference image 100, and the vertical axis represents the processing value. As shown in FIG. 10, the processing unit 44 acquires the processing value by the following formula (3). Note that SL5 and SL6 are predetermined threshold values.

[0065] CX i =Para i ×|ParaStdi | (3)

[0066] Here, CX indicates a processing value. When the evaluation parameter is a difference value, the following equation (4) is obtained.

[0067] CX i =Diff i ×|StdDiff i | (4)

[0068] As shown in FIG. 10, the processing unit 44 can reduce the processing value of the luminance range where the noise level is high and the evaluation parameter varies. In addition, characteristic points away from the average value, such as the luminance indicating a defect, can be further emphasized.

[0069] <Evaluation unit> The evaluation unit 45 evaluates the standardized evaluation parameter of each pixel in the standardized image 400. That is, the evaluation unit 45 may evaluate the standardized evaluation parameter that evaluates the information of each pixel in the evaluation image 200. In addition, the evaluation unit 45 evaluates the processing value of each pixel in the processed image 500. That is, the evaluation unit 45 may evaluate the processing value that evaluates the information of each pixel in the evaluation image 200. Note that the evaluation unit 45 performs at least any one of the evaluation of the presence or absence of an error for the standardized image 400 (evaluation image 200) by comparing the standardized evaluation parameter with a predetermined threshold value, the evaluation of each pixel of the correction mode to be performed on the evaluation image 200 based on the standardized evaluation parameter, and the evaluation of the presence or absence of an error for the corrected image obtained by correcting the evaluation image 200 based on the standardized evaluation parameter. Here, the error includes, for example, a defect of the sample.

[0070] <Review device> Next, a review apparatus to which the image processing apparatus 40 is applied will be described. FIG. 11 is a configuration diagram illustrating a review apparatus RE to which the image processing apparatus 40 according to Embodiment 1 is applied. As shown in FIG. 11, the review apparatus RE includes the image processing apparatus 40 or 40a and a monitor MO. The monitor MO reviews the reference image 100, the evaluation image 200, the comparison image, the standardized image 400, and the processed image 500. The review apparatus RE includes an image correction apparatus, an inspection apparatus, a storage apparatus, etc., and performs image correction, acquisition of a sample image, inspection of a sample, retention of an image, etc. As described above, it is not essential that the comparison image, the standardized image 400, and the processed image 500 are actually generated as images. Therefore, the review apparatus RE may not review the comparison image, the standardized image 400, and the processed image 500.

[0071] <Image processing method> Next, an image processing method using the image processing apparatus 40 in the present embodiment will be described. FIG. 12 is a flowchart diagram illustrating an image processing method using the image processing apparatus 40 according to Embodiment 1. FIG. 13 is a flowchart diagram illustrating an image processing method using the image processing apparatus 40a according to another example of Embodiment 1.

[0072] As shown in step S11 of FIGS. 12 and 13, the evaluation parameter acquisition unit 41 acquires evaluation parameters. Specifically, the evaluation parameter acquisition unit 41 acquires evaluation parameters as information of each pixel included in a plurality of pixels included in the comparison image, based on information of each pixel in a plurality of pixels included in the reference image 100 and information of each pixel in a plurality of pixels included in the evaluation image 200. Note that the comparison image may be the difference image 300, and the evaluation parameter may be a difference value. Therefore, in step S11, the evaluation parameter acquisition unit 41 may acquire a difference value as an evaluation parameter of each pixel in the difference image 300 as the comparison image, by comparing the luminance of each pixel in the reference image 100 and the luminance of each pixel in the evaluation image 200.

[0073] Next, as shown in step S12, the statistical value acquisition unit 42 acquires a statistical value. Specifically, the statistical value acquisition unit 42 acquires the statistical value of the evaluation parameters for a plurality of predetermined specific pixels in the comparison image. Here, the specific pixels in the comparison image include at least any one of the pixels whose luminance of the pixels included in the reference image 100 belongs to a predetermined luminance range and the pixels whose luminance of the pixels included in the evaluation image 200 belongs to a predetermined luminance range. Note that the statistical value acquisition unit 42 may acquire the statistical value of the difference values for a plurality of predetermined pixels in the difference image 300.

[0074] In step S12, the statistical value acquisition unit 42 may acquire a specific statistical value that is the statistical value of the evaluation parameters for specific pixels including pixels belonging to a predetermined luminance range. Further, the statistical value acquisition unit 42 may divide the predetermined luminance range into a plurality of luminance range portions, and divide the specific pixels into a plurality of specific pixel portions belonging to each luminance range portion. Thereby, the statistical value acquisition unit 42 may acquire a specific statistical value portion that is the statistical value of the evaluation parameters for each specific pixel portion for each specific pixel portion.

[0075] Next, as shown in step S13, the standardized evaluation parameter acquisition unit 43 acquires a standardized evaluation parameter. Specifically, the standardized evaluation parameter acquisition unit 43 acquires the standardized evaluation parameter as the information of each pixel in the plurality of pixels included in the standardized image 400 by standardizing the evaluation parameter of each pixel in the plurality of pixels included in the comparison image based on the statistical value.

[0076] In step S13, the standardized evaluation parameter acquisition unit 43 may acquire the standardized evaluation parameter by standardizing the evaluation parameter of the specific pixel based on the specific statistical value belonging to the predetermined luminance range. Further, the standardized evaluation parameter acquisition unit 43 may acquire the standardized evaluation parameter as the information of each pixel corresponding to the specific pixel portion by standardizing the evaluation parameter of each specific pixel portion based on each specific statistical value portion of the specific pixel portion in each divided luminance range portion.

[0077] Next, as shown in step S14 of FIG. 13, the processing unit 44 may obtain a processing value as information for each of a plurality of pixels included in the processed image 500 based on the standardized evaluation parameter for each pixel in the standardized image 400 and the evaluation parameter for each pixel in the comparison image.

[0078] Next, as shown in step S15, the evaluation unit 45 may evaluate the standardized evaluation parameter for each pixel in the standardized image 400, or may evaluate the processing value for each pixel in the processed image 500.

[0079] In step S15, the evaluation unit 45 may perform at least any one of an evaluation of the presence or absence of an error for the standardized image 400 by comparing the standardized evaluation parameter with a predetermined threshold value, an evaluation for each pixel of the correction mode to be performed on the evaluation image 200 based on the standardized evaluation parameter, and an evaluation of the presence or absence of an error for the corrected image obtained by correcting the evaluation image 200 based on the standardized evaluation parameter.

[0080] <Review method> Next, a review method to which the image processing method of the present embodiment is applied will be described. FIG. 14 is a flowchart diagram illustrating a review method using the image processing method according to Embodiment 1. As shown in FIG. 14, the review method includes a step SS20 of reviewing the reference image 100, the evaluation image 200, the comparison image, and the standardized image 400 on the monitor MO in addition to the above-described image processing method. Note that the review method may include an inspection method of inspecting the subject sample of the evaluation image 200 and may include the image processing method.

[0081] Next, the effects of this embodiment will be described. In the image processing apparatus 40 of this embodiment, the normalization evaluation parameter acquisition unit 43 obtains the normalization evaluation parameter as information of each pixel in the normalized image 400 by dividing the evaluation parameter of the pixel of the comparison image by the standard deviation for each luminance such as the reference image 100. Therefore, the smaller the noise level area, the more the normalization evaluation parameter is amplified, and the variation of the normalization evaluation parameter for each luminance can be flattened. As a result, high-precision image processing can be performed.

[0082] For example, the difference value that becomes a pseudo defect due to defocus is approximately the same as the noise level at each luminance of the reference image 100. Therefore, in the normalized image 400, the difference value of the pseudo defect becomes small, making it difficult to detect. For the same reason, it is also possible to make it difficult to detect pseudo defects due to film surface changes (reflectance changes).

[0083] By applying a threshold value to the processed image 500, both the normalized image 400 and the comparison image can be evaluated with a single threshold value, and the evaluation system for actual defects and pseudo defects can be improved.

[0084] (Embodiment 2) Next, the image processing apparatus 40 according to Embodiment 2 will be described. In this embodiment, the sample to be imaged has a pattern. For example, in a sample having a pattern including an absorber and a multilayer, as the reflectance of the absorber changes, the luminance of the absorber changes. As a result, the difference values of the pixels corresponding to the absorber in the difference image 300 may be different. On the other hand, if the luminance of the pixels corresponding to the absorber is adjusted to be constant, the luminance of the multilayer changes. In any case, in the difference image 300, pseudo defects due to changes in the luminance of the absorber or the multilayer may be detected. Also, if there are patterns of absorbers and patterns of multilayers with different difference values in the same luminance range, normalization may not be successfully reflected. Therefore, in this embodiment, normalization is performed separately for regions divided based on patterns such as absorbers and multilayers.

[0085] FIG. 15 is a diagram illustrating an evaluation image 200a processed by the image processing apparatus 40 according to Embodiment 2. As shown in FIG. 15, the evaluation image 200a includes an image of a sample having a pattern PT including an absorber region AB and a region CD other than the absorber. Note that the absorber region AB and the region CD other than the absorber are an example of the pattern PT. The pattern PT may be classified according to other properties, and the upper limit of the number of types of the pattern PT classified for the sample is not limited. Therefore, for each type of the pattern PT and according to the number of divisions corresponding to the number of types, the specific region portions described later may be determined. In FIG. 15, some reference numerals are omitted so that the figure does not become complicated. The region AB in the pattern PT includes, for example, a solid film of an absorber formed on the sample. Note that the pattern PT is not limited to the solid film of the absorber formed on the sample, and may include a corner portion of the solid film. Note that the reference image 100 may also include an image of a sample having a pattern PT including the absorber region AB and the region CD other than the absorber. That is, at least one of the reference image 100 and the evaluation image 200a may include an image of a sample having the pattern PT.

[0086] FIG. 16 is a diagram illustrating a difference image 300a as a comparison image processed by the image processing apparatus 40 according to Embodiment 2. As shown in FIG. 16, the difference image 300 includes an image reflecting the pattern PT including the absorber region AB and the region CD other than the absorber.

[0087] FIG. 17 is a graph illustrating the relationship between pixel information and an evaluation parameter in the image processing apparatus 40 according to Embodiment 2. The horizontal axis represents the luminance of the reference image 100, and the vertical axis represents the difference value. FIG. 18 is a graph illustrating the relationship between pixel information and a standardized evaluation parameter in the image processing apparatus 40 according to Embodiment 2. The horizontal axis represents the luminance of the pixels of the reference image 100, and the vertical axis represents the standardized difference value. In FIGS. 17 and 18, SL7 to SL10 represent predetermined threshold values.

[0088] As shown in FIG. 18, in the present embodiment, a pseudo defect F has occurred in the standardized image 400. As described above, this is presumably because the standardization cannot be properly reflected due to the patterns PT in the regions AB and CD having different difference values in the same luminance range.

[0089] Therefore, the statistical value acquisition unit 42 acquires the statistical values of the specific regions divided based on the pattern PT. In the present embodiment, the specific pixels in the comparison image include at least any one of the pixels belonging to the specific regions divided based on the pattern PT of the reference image 100 and the pixels belonging to the specific regions divided based on the pattern PT of the evaluation image 200. The specific regions include the pattern PT in the reference image 100, the evaluation image 200, and the difference image 300. The specific pixels include the pixels belonging to the pattern PT.

[0090] In this way, the statistical value acquisition unit 42 acquires the specific statistical values of the evaluation parameters for the specific pixels including the pixels belonging to the specific regions. The standardized evaluation parameter acquisition unit 43 acquires the standardized evaluation parameters by standardizing the evaluation parameters of the plurality of pixels included in the comparison image based on the specific statistical values.

[0091] Further, the statistical value acquisition unit 42 divides the specific region including the pattern PT into a plurality of specific region portions. The specific region portions may be, for example, the region AB of the absorber or the region CD other than the absorber. Also, the specific region portions may be the corner portions of the absorber or the portions other than the corner portions of the absorber.

[0092] Further, the statistical value acquisition unit 42 divides a specific pixel into a plurality of specific pixel portions belonging to each specific region portion. The specific pixel portion may be a pixel belonging to the region AB of the absorber and having a predetermined luminance range, or a pixel belonging to the region CD other than the absorber and having a predetermined luminance range. Further, the specific pixel portion may be a pixel belonging to a corner portion of the absorber and having a predetermined luminance range, or a pixel belonging to a portion other than the corner portion of the absorber and having a predetermined luminance range. Note that the predetermined luminance range may be divided into a plurality of luminance range portions as described above.

[0093] In this way, the statistical value acquisition unit 42 acquires a specific statistical value portion, which is a statistical value of the evaluation parameter for the specific pixel portion, for each specific pixel portion. The standardized evaluation parameter acquisition unit 43 acquires a standardized evaluation parameter as information on each pixel corresponding to the specific pixel portion in the standardized image 400 by standardizing the evaluation parameter of each specific pixel portion based on each specific statistical value portion.

[0094] Hereinafter, a specific example will be described. The specific region in the difference image 300a is a region having a pattern PT including the region AB of the absorber and the region CD other than the absorber. The specific pixel includes pixels belonging to a region having a pattern PT including the region AB of the absorber and the region CD other than the absorber. The specific region portion obtained by dividing the specific region includes the region AB of the absorber and the region CD other than the absorber. The specific pixel portion includes pixels belonging to the region AB of the absorber and pixels belonging to the region CD other than the absorber.

[0095] FIGS. 19 and 20 are graphs illustrating the relationship between the pixel information and the evaluation parameter for the specific pixel portion in the image processing apparatus 40 according to the second embodiment. The horizontal axis represents the luminance of the pixels in the reference image 100, and the vertical axis represents the difference value. FIG. 19 shows the difference value of the pixels when the pixels belonging to the region AB of the absorber are used as the specific pixel portion, and FIG. 20 shows the difference value of the pixels when the pixels belonging to the region CD other than the absorber are used as the specific pixel portion.

[0096] FIG. 21 and FIG. 22 are graphs illustrating the relationship between pixel information and a normalization evaluation parameter for a specific pixel portion in the image processing apparatus 40 according to Embodiment 2. The horizontal axis represents the luminance of the pixels of the reference image, and the vertical axis represents the normalization difference value. FIG. 21 shows the normalization difference value when the pixels belonging to the absorber region AB are used as the specific pixel portion, and FIG. 22 shows the normalization difference value when the pixels belonging to the region CD other than the absorber are used as the specific pixel portion. FIG. 23 is a graph illustrating the relationship between pixel information and a normalization evaluation parameter for a specific pixel in the image processing apparatus 40 according to Embodiment 1. The horizontal axis represents the luminance of the pixels of the reference image 100, and the vertical axis represents the normalization difference value. FIG. 23 combines the results of FIG. 21 and FIG. 22. In FIGS. 19 to 23, SL11 to SL20 indicate predetermined threshold values.

[0097] As shown in FIGS. 21 to 23, in the present embodiment, generation of pseudo defects F is suppressed in the standardized image 400. This is because the standardized evaluation parameters are obtained by dividing the pixels belonging to the absorber region AB into a specific pixel portion that is a specific pixel portion and the pixels belonging to the region CD other than the absorber into a specific pixel portion that is a specific pixel portion. Thus, when there are regions (pattern PT of region AB and region CD) where the degree of variation in the difference value is different in the same luminance range, standardization is performed for each region. Thereby, the evaluation image 200 can be more suitably evaluated, such as suppressing the generation of pseudo defects. In addition, the first embodiment and the second embodiment can be appropriately combined. That is, a specific pixel portion that is a pixel belonging to the first specific region portion (absorber region AB) and within a predetermined luminance range and a specific pixel portion that is a pixel belonging to the second specific region portion (region CD other than the absorber) and within a predetermined luminance range may be divided to obtain the standardized evaluation parameters. Further, the predetermined luminance range may be divided into a plurality of luminance range portions. That is, a specific pixel portion that is a pixel belonging to the first specific region portion (absorber region AB) and belonging to the first luminance range portion and a specific pixel portion that is a pixel belonging to the first specific region portion (absorber region AB) and belonging to the second luminance range portion and a specific pixel portion that is a pixel belonging to the second specific region portion (region CD other than the absorber) and belonging to the first luminance range portion and a specific pixel portion that is a pixel belonging to the second specific region portion (region CD other than the absorber) and belonging to the second luminance range portion may be divided to obtain the standardized evaluation parameters. Thereby, the evaluation image 200 can be more suitably evaluated, such as suppressing the generation of pseudo defects.

[0098] According to the image processing apparatus 40 of the present embodiment, since standardization is performed on the evaluation parameters for each specific region portion, the accuracy of the processing can be improved. In addition, unwanted differences (nuisances) that occur in a specific pattern PT can also be reduced by standardizing for each specific region portion.

[0099] The embodiments of the present disclosure have been described above. However, the present disclosure includes appropriate modifications that do not impair its objectives and advantages, and furthermore, is not limited by the above embodiments. Also, combinations of the configurations of Embodiments 1 to 2 are also within the scope of the technical idea of the present disclosure. Also, the following configurations are also within the scope of the technical idea of the embodiments.

[0100] (Appendix 1) Based on the information of each pixel among a plurality of pixels included in a reference image and the information of each pixel among a plurality of pixels included in an evaluation image corresponding to the reference image, as the information of each pixel among a plurality of pixels included in a comparison image corresponding to the reference image and the evaluation image, a step of obtaining an evaluation parameter; A step of obtaining a statistical value of the evaluation parameter for a predetermined plurality of specific pixels in the comparison image; Based on the statistical value, by normalizing the evaluation parameter of each pixel among the plurality of pixels included in the comparison image, as the information of each pixel among the plurality of pixels included in a normalized image corresponding to the comparison image, a step of obtaining a normalized evaluation parameter; A step of evaluating the normalized evaluation parameter; An image processing method comprising the above. (Appendix 2) Based on the information of each pixel among a plurality of pixels included in a reference image and the information of each pixel among a plurality of pixels included in an evaluation image corresponding to the reference image, a step of obtaining an evaluation parameter for evaluating the information of each pixel among the plurality of pixels included in the evaluation image based on a comparison between the reference image and the evaluation image; A step of obtaining a statistical value of the evaluation parameter for a predetermined plurality of specific pixels in the evaluation image; Based on the statistical value, by normalizing the evaluation parameter, a step of obtaining a normalized evaluation parameter for a plurality of the pixels included in the evaluation image; A step of evaluating the normalized evaluation parameter; An image processing method comprising (Appendix 3) In the step of obtaining the evaluation parameter, A difference value obtained by comparing the luminance of each pixel in the reference image with the luminance of each pixel in the evaluation image is obtained as the evaluation parameter of each pixel. The image processing method according to Appendix 1 or 2. (Appendix 4) The specific pixel includes at least one of the pixels whose luminance in the reference image belongs to a predetermined luminance range and the pixels whose luminance in the evaluation image belongs to the predetermined luminance range. The image processing method according to Appendix 1 or 2. (Appendix 5) In the step of obtaining the statistical value, A specific statistical value of the evaluation parameter for the specific pixel including the pixels belonging to the luminance range is obtained. In the step of obtaining the standardized evaluation parameter, Based on the specific statistical value, the evaluation parameter of the specific pixel is standardized to obtain the standardized evaluation parameter. The image processing method according to Appendix 4. (Appendix 6) In the step of obtaining the statistical value, The luminance range is divided into a plurality of luminance range portions. The specific pixel is divided into a plurality of specific pixel portions belonging to each luminance range portion. A specific statistical value portion, which is the statistical value of the evaluation parameter for the specific pixel portion, is obtained for each specific pixel portion. In the step of obtaining the standardized evaluation parameter, Based on each specific statistical value portion, the evaluation parameter of each specific pixel portion is standardized to obtain the standardized evaluation parameter for each pixel corresponding to the specific pixel portion. The image processing method according to Appendix 4. (Appendix 7) At least one of the reference image and the evaluation image includes an image of a sample having a pattern. The specific pixels include at least any one of the pixels belonging to a specific region classified based on the pattern of the reference image and the pixels belonging to the specific region classified based on the pattern of the evaluation image. The image processing method according to Appendix 1 or 2. (Appendix 8) In the step of obtaining the statistical value, Obtain a specific statistical value of the evaluation parameter for the specific pixels corresponding to the pixels belonging to the specific region. In the step of obtaining the standardized evaluation parameter, Based on the specific statistical value, standardize the evaluation parameter of the pixel to obtain the standardized evaluation parameter. The image processing method according to Appendix 7. (Appendix 9) In the step of obtaining the statistical value, Divide the specific region into a plurality of specific region parts. Divide the specific pixels into a plurality of specific pixel parts belonging to each specific region part. Obtain a specific statistical value part, which is a statistical value of the evaluation parameter for the specific pixel part, for each specific pixel part. In the step of obtaining the standardized evaluation parameter, Based on each specific statistical value part, standardize the evaluation parameter of each specific pixel part to obtain the standardized evaluation parameter for each pixel corresponding to the specific pixel part. The image processing method according to Appendix 7. (Appendix 10) In the step of evaluating, Evaluating the presence or absence of an error for the subject sample of the evaluation image by comparing the standardized evaluation parameter with a predetermined threshold value, evaluating the pixel of the correction mode to be performed on the evaluation image based on the standardized evaluation parameter, and evaluating the presence or absence of an error for the corrected image obtained by correcting the evaluation image based on the standardized evaluation parameter, at least any one of them is performed. The image processing method according to Appendix 1 or 2. (Appendix 11) The apparatus further includes a processing unit that obtains a processing value by arithmetic processing based on the standardized evaluation parameter and the evaluation parameter. The evaluation unit evaluates the processing value. The image processing method according to Appendix 1.

Explanation of Signs

[0101] 40, 40a Image processing apparatus 41 Evaluation parameter acquisition unit 42 Statistical value acquisition unit 43 Standardized evaluation parameter acquisition unit 44 Processing unit 45 Evaluation unit 100 Reference image 200 Evaluation image 300 Difference image 400 Standardized image 500 Processed image AB, CD regions MO Monitor PT Pattern RE Review apparatus SL1, SL2 Threshold values

Claims

1. An evaluation parameter acquisition unit that acquires an evaluation parameter as the information of each pixel in a plurality of pixels included in a comparison image corresponding to the reference image and the evaluation image, based on the information of each pixel in a plurality of pixels included in the reference image and the information of each pixel in a plurality of pixels included in the evaluation image corresponding to the reference image; A statistical value acquisition unit that acquires a statistical value of the evaluation parameter for a plurality of predetermined specific pixels in the comparison image; A standardized evaluation parameter acquisition unit that acquires a standardized evaluation parameter as the information of each pixel in a plurality of pixels included in a standardized image corresponding to the comparison image by standardizing the evaluation parameter of each pixel in a plurality of pixels included in the comparison image based on the statistical value; An evaluation unit that evaluates the standardized evaluation parameter; An image processing apparatus comprising the above.

2. An evaluation parameter acquisition unit that acquires an evaluation parameter for evaluating the information of each pixel in a plurality of pixels included in the evaluation image based on a comparison between the reference image and the evaluation image, based on the information of each pixel in a plurality of pixels included in the reference image and the information of each pixel in a plurality of pixels included in the evaluation image corresponding to the reference image; A statistical value acquisition unit that acquires a statistical value of the evaluation parameter for a plurality of predetermined specific pixels in the evaluation image; A standardized evaluation parameter acquisition unit that acquires a standardized evaluation parameter for a plurality of pixels included in the evaluation image by standardizing the evaluation parameter based on the statistical value; An evaluation unit that evaluates the standardized evaluation parameter; An image processing apparatus comprising the above.

3. The evaluation parameter acquisition unit acquires, as the evaluation parameter of each pixel, a difference value obtained by comparing the luminance of each pixel in the reference image with the luminance of each pixel in the evaluation image. The image processing apparatus according to Claim 1 or 2.

4. The specific pixel includes at least any one of the pixels whose luminance in the reference image belongs to a predetermined luminance range and the pixels whose luminance in the evaluation image belongs to the predetermined luminance range. The image processing apparatus according to Claim 1 or 2.

5. The statistical value acquisition unit acquires a specific statistical value of the evaluation parameter for the specific pixel including the pixel belonging to the luminance range. The standardized evaluation parameter acquisition unit acquires the standardized evaluation parameter by standardizing the evaluation parameter of the specific pixel based on the specific statistical value. The image processing apparatus according to claim 4.

6. The statistical value acquisition unit divides the luminance range into a plurality of luminance range portions, divides the specific pixel into a plurality of specific pixel portions belonging to each luminance range portion, acquires a specific statistical value portion, which is a statistical value of the evaluation parameter for the specific pixel portion, for each specific pixel portion, The standardized evaluation parameter acquisition unit acquires the standardized evaluation parameter for each pixel corresponding to the specific pixel portion by standardizing the evaluation parameter of each specific pixel portion based on each specific statistical value portion. The image processing apparatus according to claim 4.

7. At least one of the reference image and the evaluation image includes an image of a sample having a pattern, The specific pixel includes at least one of the pixels belonging to a specific region classified based on the pattern of the reference image and the pixels belonging to the specific region classified based on the pattern of the evaluation image. The image processing apparatus according to claim 1 or 2.

8. The statistical value acquisition unit acquires a specific statistical value of the evaluation parameter for the specific pixel including the pixels belonging to the specific region, The standardized evaluation parameter acquisition unit acquires the standardized evaluation parameter by standardizing the evaluation parameter of the pixel based on the specific statistical value. The image processing apparatus according to claim 7.

9. The statistical value acquisition unit divides the specific region into a plurality of specific region portions, divides the specific pixel into a plurality of specific pixel portions belonging to each specific region portion, acquires a specific statistical value portion, which is a statistical value of the evaluation parameter for the specific pixel portion, for each specific pixel portion, The standardized evaluation parameter acquisition unit acquires the standardized evaluation parameter for each pixel corresponding to the specific pixel portion by standardizing the evaluation parameter of each specific pixel portion based on each specific statistical value portion. The image processing apparatus according to claim 7.

10. The evaluation unit performs at least one of: evaluating the presence or absence of an error in the subject sample of the evaluation image by comparing the standardized evaluation parameter with a predetermined threshold value; evaluating the pixels of the correction mode to be performed on the evaluation image based on the standardized evaluation parameter; and evaluating the presence or absence of an error in the corrected image obtained by correcting the evaluation image based on the standardized evaluation parameter. The image processing apparatus according to claim 1 or 2.

11. The apparatus further includes a processing unit that obtains a processing value by arithmetic processing based on the standardized evaluation parameter and the evaluation parameter. The evaluation unit evaluates the processing value. The image processing apparatus according to claim 1 or 2.

12. An inspection apparatus for a subject sample of the evaluation image, comprising the image processing apparatus according to claim 1 or 2.

13. The image processing apparatus according to claim 1, and a monitor for reviewing the reference image, the evaluation image, the comparison image, and the standardized image. A review apparatus comprising the same.

14. A step of obtaining an evaluation parameter as the information of each pixel in a plurality of pixels included in a comparison image corresponding to the reference image and the evaluation image, based on the information of each pixel in a plurality of pixels included in the reference image and the information of each pixel in a plurality of pixels included in the evaluation image corresponding to the reference image; A step of obtaining a statistical value of the evaluation parameter for a plurality of predetermined specific pixels in the comparison image; A step of obtaining a standardized evaluation parameter as the information of each pixel in a plurality of pixels included in a standardized image corresponding to the comparison image, by standardizing the evaluation parameter of each pixel in a plurality of pixels included in the comparison image based on the statistical value; A step of evaluating the standardized evaluation parameter; An image processing method comprising the same.

15. A step of obtaining an evaluation parameter for evaluating the information of each pixel in a plurality of pixels included in the evaluation image based on the comparison between the reference image and the evaluation image, based on the information of each pixel in a plurality of pixels included in the reference image and the information of each pixel in a plurality of pixels included in the evaluation image corresponding to the reference image; A step of obtaining a statistical value of the evaluation parameter for a plurality of predetermined specific pixels in the evaluation image; Based on the statistical value, by standardizing the evaluation parameter, obtaining a standardized evaluation parameter for a plurality of the pixels included in the evaluation image; evaluating the standardized evaluation parameter; An image processing method comprising the above.

16. An inspection method for inspecting a subject sample of the evaluation image, comprising the image processing method according to claim 14 or 15.

17. The image processing method according to claim 14, reviewing the reference image, the evaluation image, the comparison image, and the standardized image on a monitor; A review method comprising the above.

Images