JP2006275691A - Inspection method and inspection device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To easily and surely determine whether crystallization is progressed sufficiently or not when solid-phase-crystallizing amorphous silicon. <P>SOLUTION: In this inspection method, an image of an inspection area on a substrate 110 is imaged to prepare a gray scale image from the image on the basis of lightness, a threshold value of the lightness of the gray scale image is determined pursuant to Expression : threshold value (Th)=minimum value+(maximum value-minimum value)×A (A=0-1), a binary image is prepared from the image on the basis of the threshold value of the lightness, and the inspection area is determined to be nondefective or defective on the basis of the binary image. A degree of the progress in the solid phase crystallization is determined, in particular, when an amorphous area and a polycrystal area exist in the inspection area on the substrate by the solid phase crystallization. A determination result is reflected to other manufacturing process through a network to enhance the quality of a product. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to an inspection method and an inspection apparatus for a crystalline thin film.

  In recent years, thin film transistors (TFTs) using polycrystalline silicon instead of amorphous silicon are often employed in liquid crystal panels and organic EL panels that require high definition. Polycrystalline silicon can be manufactured by solid-phase crystallization of amorphous silicon, which is relatively easy to manufacture. In order to manufacture high-quality panels, it is inspected whether crystallization of amorphous silicon has progressed sufficiently. There is a need to. Conventionally, the inspection after the solid-phase crystallization has been performed visually by an inspector.

  However, since the inspection by the inspector as described above takes a long time, the manufacturing time of the product increases. In addition, there are problems such as placing a great burden on the inspector and causing individual differences in determination. Therefore, in recent years, a method has been proposed in which inspection after solid-phase crystallization is performed by image processing (for example, see Patent Document 1).

  In the image processing method described in Patent Document 1, the progress of crystallization is determined by classifying an optical microscope image obtained by imaging a test substrate into either an amorphous region or a polycrystalline region and binarizing it. This binarization method is difficult to understand intuitively and it is difficult to reflect the experience of the inspector. However, it is desirable to easily reflect the empirical judgment of the inspector in the image processing because a determination result close to visual inspection can be obtained.

Patent Publication No. 2004-179190

  Therefore, in the present invention, it is possible to easily and surely determine whether crystallization has sufficiently progressed when solid-phase crystallization of amorphous silicon, and the determination result is close to that of visual inspection reflecting the experience of the inspector. It is an object of the present invention to provide an inspection method and an inspection apparatus capable of producing

The configuration of the present invention for solving the above problems is to capture an image of an inspection region on a substrate, create a grayscale image based on the brightness from the image, and set the brightness threshold of the grayscale image to the following formula: Determined by
Threshold (Th) = Minimum value + (Maximum value−Minimum value) × A
(However, A = 0 to 1)
A binary image is created from the grayscale image based on the lightness threshold value, and the quality of the inspection area is determined based on the binary image. In particular, when an amorphous region and a polycrystalline region are present by solid phase crystallization in the inspection region on the substrate, the progress of solid phase crystallization can be determined.

  Further, in the present invention, an image of an inspection region is taken, a grayscale image is created from the lightness based on the image, a histogram is created based on the grayscale image, a skewness is calculated from the histogram, Whether the inspection area is good or bad is determined from the skewness. In particular, in the case where an amorphous region and a polycrystalline region exist due to solid-phase crystallization in the inspection region on the substrate, solid-phase crystallization in the inspection region is not sufficiently advanced when the skewness is smaller than a certain reference value. If the degree of distortion is greater than a certain reference value, it can be determined that solid-phase crystallization in the inspection region is sufficiently advanced. It should be noted that the substrate determined that the solid-phase crystallization is not sufficiently advanced due to the skewness may be removed from the inspection without proceeding to the next inspection step.

  Further, the present invention captures an image of an inspection region including an amorphous region and a polycrystalline region, creates a grayscale image from the image based on brightness, creates a histogram based on the grayscale image, And calculating the degree of crystallization from amorphous to polycrystalline in the inspection region based on the degree of distortion to determine whether the inspection region is good or bad.

Further, in the present invention, an image of an inspection region including an amorphous region and a polycrystalline region is taken, a gray scale image is created from the lightness based on the image, and a lightness threshold value of the gray scale image is determined by the following equation: ,
Threshold (Th) = Minimum value + (Maximum value−Minimum value) × A
(However, A = 0 to 1)
A binary image is created from the gray scale image based on a threshold value of brightness, a connected region in the binary image is detected, and the ratio of the connected region to the total pixels of the binary image It is characterized by determining a defect. In this case, it can be determined whether the crystallization from amorphous to polycrystalline has sufficiently progressed based on the ratio of the amorphous region to the total pixels of the binary image.

Further, in the present invention, an image of an inspection region including an amorphous region and a polycrystalline region is taken, a gray scale image is created from the lightness based on the image, and a lightness threshold value of the gray scale image is determined by the following equation: ,
Threshold (Th) = Minimum value + (Maximum value−Minimum value) × A
(However, A = 0 to 1)
A binary image is created from the grayscale image based on a lightness threshold value, a connected region in the binary image is detected, and whether the inspection region is good or bad is determined based on the number or size of the connected regions. It is characterized by. In this case, whether or not the solid-phase crystallization state is in a desired state can be determined based on the number or size of the connected regions obtained from the binary image.

In the present invention, an image of an inspection region including an amorphous region and a polycrystalline region is captured, a grayscale image is created from the image based on brightness, a histogram is created based on the grayscale image, and the histogram The degree of distortion is calculated from the degree of crystallization from amorphous to polycrystalline in the inspection area based on the degree of distortion to determine whether the inspection area is good or bad. Determined by the formula
Threshold (Th) = Minimum value + (Maximum value−Minimum value) × A
(However, A = 0 to 1)
A binary image is created from the gray scale image based on a threshold value of brightness, a connected region in the binary image is detected, and the ratio of the connected region to the total pixels of the binary image A defect is determined, and whether the inspection area is good or bad is determined from the number or size of the connected areas.

  In the present invention, when measuring the maximum value / minimum value of the brightness of the gray scale image, the influence of noise is reduced by using a histogram.

  Further, the present invention is characterized in that the determination result is transmitted to another computer such as a server through a network, and the determination result is reflected in another manufacturing process. As a result, the quality of the product can be increased.

  Furthermore, the present invention is an inspection apparatus comprising the inspection method as a means.

  As described above, the inspection after solid-phase crystallization has been performed by visual inspection by an inspector. However, the inspection method provided by the present invention enables the inspection to be automated, shortening the manufacturing time, saving labor, and cost. It leads to reduction. In addition, the burden on the inspector can be reduced, and variations in determination due to individual differences among the inspectors can be suppressed. In the present invention, since the experience of the inspector is easily reflected in the image processing, it is possible to obtain a determination result close to the case where the determination is made visually.

  In this embodiment mode, an inspection method after solid-phase crystallization of amorphous silicon to which the present invention is applied will be described.

  In the inspection method of the present invention, when the amorphous silicon formed on the substrate is solid-phase crystallized into polycrystalline silicon, it is possible to inspect the degree of crystallization, but in the case of the present embodiment, A case will be described in which not only polycrystalline silicon but also amorphous silicon is automatically selected within a certain reference value range. Note that the embodiment described below is one specific example of the present invention, and thus various restrictions are given. However, the present invention is not limited to these embodiments.

  In addition, although the semiconductor film in this embodiment is silicon, even after the semiconductor film is germanium or a silicon germanium compound, inspection after solid-phase crystallization can be performed by the same procedure.

  FIG. 1 is a schematic diagram of an inspection apparatus according to the present embodiment. The inspection substrate 110 is irradiated with the white light source 103, and the transmitted light is imaged by the optical microscope 102 and the digital camera 101. Note that the white light source 103 at the time of photographing may be arranged on the inspection substrate 110 and irradiated to image the reflected light. The captured image is transferred to a personal computer (PC) 104 and image processing is performed in the PC 104. Various images can be confirmed on the monitor 105. The PC 104 is connected to another computer via the network 106.

  The monitor 105 is for displaying an imaged image and the like for an inspector to visually confirm, but since the image processing is performed in the PC 104, the monitor 105 is only used as an auxiliary by the inspector.

  Next, the inspection method of this embodiment will be described with reference to the flowcharts of FIGS.

  In this embodiment, a substrate obtained by solid-phase crystallization of amorphous silicon on a substrate is used as an inspection substrate, the inspection substrate is irradiated with a white light source 103, and an inspection region on the inspection substrate is imaged by the optical microscope 102 and the digital camera 101. (Step 1). Note that when the inspection substrate is irradiated with white light and the transmitted light is imaged, the region where amorphous silicon remains without being solid-phase crystallized (hereinafter referred to as amorphous region) is yellow, and solid-phase crystallization progresses frequently. A region where the crystalline silicon is formed (hereinafter referred to as a polycrystalline region) is white. This color difference is due to the fact that polycrystalline silicon transmits white light almost as it is, while amorphous silicon absorbs blue light of white light.

  Next, a lightness grayscale image is created from the image (optical microscope image) captured in step 1 (step 2). Lightness is an element representing brightness. In this embodiment, the brightness is 256 gradations from 0 to 255. Since amorphous silicon absorbs blue light, the brightness in the amorphous region is lower than that in the polycrystalline region, so the amorphous region appears dark in the grayscale image.

  Next, noise is removed from the grayscale image created in step 2 using a known method, and a histogram of the grayscale image from which noise has been removed is created (step 3). By creating a histogram, the distribution of amorphous regions and polycrystalline regions existing on each inspection substrate can be expressed by skewness. The skewness is a value representing the bias of the histogram, and takes a positive value when the shape of the histogram is to the left, and a negative value when the shape of the histogram is to the right. In the case of the present embodiment, since the horizontal axis of the histogram is represented by brightness, when the shape of the histogram is to the left (distortion is a positive value), solid-phase crystallization does not progress so much and there are many amorphous regions. When the shape of the histogram is to the right (strain value is negative), solid-phase crystallization proceeds and there are many polycrystalline regions.

  3A to 3F show gray scale images at each stage of solid-phase crystallization in this embodiment. Solid phase crystallization proceeds in the order of (A) → (B) → (C) → (D) → (E) → (F). FIG. 4 shows a histogram obtained from the gray scale images of FIGS. Note that (A) to (F) shown in FIG. 4 correspond to (A) to (F) in FIG. 3.

  In FIG. 4, the shape of the histogram changes from left to right as solid-phase crystallization proceeds in the order of (A) → (B) → (C) → (D) → (E) → (F). I understand. This is because there are many pixels with low brightness when crystallization is not progressing much and there are many amorphous regions, and there are many pixels with high brightness when crystallization progresses and the number of polycrystalline regions increases.

  In addition, Table 1 shows the skewness calculated from each histogram in FIG. 4 ((A) to (F)).

  The skewness of the histogram changes from a positive value to a negative value as solid-phase crystallization progresses (as solid-phase crystallization progresses from (A) to (F)). Therefore, in this embodiment, the skewness is used as a parameter for evaluating the progress of solid-phase crystallization. That is, it is possible to determine whether solid-phase crystallization on the inspection substrate has reached the inspection standard based on the standard value of the skewness. If the crystal state on the inspection substrate shows a skewness larger than the reference value, it is determined that the inspection substrate does not reach the inspection standard and is judged as defective (NG), and the inspection substrate is removed from the inspection process. If the degree of distortion is smaller than that, it is determined that the inspection standard has been reached, and the product is determined to be non-defective (OK), and the inspection substrate proceeds to the inspection process (step 4).

  When the skewness shown in Table 1 is obtained from each test substrate and, for example, 0.2 is set as the standard value of the skewness, solid-phase crystallization hardly progresses (A) or (B) (C), (D), (E), and (F) are determined to be defective products (NG) because they have not reached the inspection standard, are removed from the inspection process, and solid phase crystallization is in progress. As a result, it is determined that the product is non-defective (OK), and the process proceeds to the next inspection process.

  Next, each pixel of the grayscale image used when creating the histogram in step 3 is classified as either a polycrystalline region or an amorphous region, and a binary image in which the polycrystalline region is represented by white and the amorphous region is represented by black. Is created (step 5).

  In this embodiment, a lightness threshold value is determined in advance. If the lightness value of each pixel of the grayscale image is larger than the lightness threshold value, it is determined as a polycrystalline region, and if it is small, it is determined as an amorphous region. This is expressed as black to create a binary image.

The brightness threshold used in this embodiment is determined by the following method. For example, when the brightness in the straight line XX ′ portion in the gray scale image shown in FIG. 5A is measured, data as shown in FIG. 5B is obtained. The gray scale image in FIG. 5A is an image having 400 pixels in the horizontal direction and 300 pixels in the vertical direction, and the Y coordinate of the straight line XX ′ is 215. As described above, when the value of lightness is small, it indicates that the region is an amorphous region, and when the value of lightness is large, it indicates that the region is a polycrystalline region. By using the maximum value (H) and the minimum value (L) of the lightness in FIG. 5B, the lightness threshold (Th) is obtained by the following equation (1).
Threshold (Th) = Minimum value + (Maximum value−Minimum value) × A (Formula 1)
(However, A = 0 to 1)

  When the test substrates in the same solid-state crystallization state are evaluated, when the value of A in Equation (1) is close to 1, more regions are considered as amorphous regions, whereas the value of A is close to 0. It can be seen that the number of regions regarded as polycrystalline regions increases. Therefore, by adjusting the value of A so as to meet the inspection standard when the inspector visually inspects, the polycrystalline region and the amorphous region can be distinguished by the same inspection standard as when visually inspected. . Note that the maximum and minimum values in equation (1) change in response to changes in the inspection environment (such as when the illumination changes and the entire image becomes brighter or darker), so the effect of changes in the inspection environment It is possible to reduce the inspection.

  However, since the maximum value (H) and the minimum value (L) of the brightness obtained by the measurement may change due to noise in the image, using the values as they are in the expression (1), the expression (1 The threshold value (Th) of brightness obtained in (1) may also be affected by noise. If such noise is included, the polycrystalline region and the amorphous region cannot be sufficiently distinguished, so the maximum value (H ′) and the minimum value (L ′) obtained by reducing the influence of noise are set. It is preferable to obtain the lightness threshold (Th ′) using the equation (1).

  In this embodiment, the lightness threshold (Th ′) is obtained by using the maximum value (H ′) and the minimum value (L ′) obtained by reducing the influence of noise in the equation (1).

  In the case of the gray scale image of FIG. 5A, the histogram shown in FIG. 6A is obtained, and the maximum value (H) and the minimum value (L) of brightness are obtained by measurement. Here, what is detected when the frequency of the histogram is F or less is regarded as noise. That is, in the histogram, when the frequency is F, the value that maximizes the brightness is the maximum value (H ′), and when the frequency is F, the value that minimizes the brightness is the minimum value (L ′). A lightness threshold (Th ′) that further reduces the influence of noise is obtained.

  FIG. 6B shows a binary image created based on the brightness threshold (Th ′) obtained in this embodiment. In equation (1), the value of A was 0.40, and the value of H for noise reduction was 5. Since the binary image shown in FIG. 6B reduces the influence of noise, the amorphous region included in the inspection substrate is detected more accurately. In the following description, when Expression (1) is used, it is assumed that A is 0.40 and F is 5, and the threshold value is obtained.

  Next, a black pixel region (hereinafter referred to as a connected region) representing an amorphous region is detected in the created binary image, and the number and size thereof are stored. (Step 6).

  By examining how much the measured connected region is present in the binary image, it is possible to examine the progress of solid-phase crystallization on the inspection substrate. Therefore, in the present embodiment, the sum of the number of pixels in all the connected areas detected in step 6 is obtained, and the ratio of the connected areas in the binary image is calculated. Compare the ratio of connected areas in this binary image with the reference value of the ratio of connected areas set in advance, and if the ratio of connected areas is lower than the reference value, solid-phase crystallization will proceed within the inspection standard range. Therefore, it is regarded as non-defective (OK) and proceeds to the next inspection process. If it is high, solid-phase crystallization has not progressed within the inspection standard range, so it is determined as defective (NG) and removed from the inspection process. The (Step 7).

  Table 2 shows the ratio of connected regions in the case where the reference value of the skewness in step 4 is 0.2 and the images in FIGS. 3C to 3F are binarized by equation (1).

  The image shown in FIG. 3C is an image that is not sufficiently crystallized and should be determined as NG. However, if the reference value of the ratio of the amorphous region is set to about 0.25 in step 6, the image shown in FIG. NG can be determined for the image shown in 3 (C).

  In addition, there are a plurality of connected regions detected in step 6, and the shapes thereof are various. Even in the case of a test substrate that is determined to be non-defective because solid phase crystallization has progressed within the range of the inspection standard from the ratio of the connected regions in step 7, there may be a problem in the state of solid phase crystallization. For example, when a large number of connected regions larger than a certain standard are included in a plurality of connected regions representing an amorphous region, the TFT (thin film transistor) using these substrates is completed through a later process. Problems such as reduced performance may occur.

  Therefore, the size and number of connected regions detected in step 6 are compared with a preset size and number reference value (step 8). If the number of connected regions whose shape is larger than the reference value is larger than the reference value, the crystallized state is regarded as bad, and it is determined as a defective product (NG) and removed from the inspection process. If not, it is determined that the crystallized state is good and that the product is OK.

  For example, when the reference value of the size of the connected region is 300 pixels, each of the binary images when the images shown in FIGS. 3D, 3E, and 3F are binarized by Expression (1) The number of connected regions of 300 pixels or more is the number shown in Table 3.

  Therefore, if it is determined that the image shown in FIG. 3D is defective, the reference value for the size of the connected region may be set to 300 pixels, and the reference value for the number may be set to 1.

  Note that since the inspection substrate inspection process described in this embodiment is completed as described above, the substrate determined to be a non-defective product (OK) in Step 8 can proceed to a subsequent manufacturing process.

  As described above, by applying the present invention to the inspection after solid-phase crystallization, the inspection can be automated and speeded up, and the manufacturing cost of the TFT substrate can be reduced. In addition, the burden on the inspector can be reduced, and variations in determination due to individual differences can be suppressed. In the present invention, since the experience of the inspector is easily reflected in the image processing, a determination result close to that in the case of visual inspection can be obtained.

The schematic diagram of the inspection device in the present invention. The flowchart which shows the operation | movement procedure of the inspection method in this invention. The gray scale image of each step of crystallization used in the present invention. The histogram of each gray scale image in FIG. (A) A grayscale image used for threshold value calculation. FIG. 5B is a graph showing lightness on the straight line X-X ′ in FIG. (A) Histogram of the image shown in FIG. (B) An image obtained by binarizing FIG.

Explanation of symbols

101 Digital Camera 102 Optical Microscope 103 White Light Source 104 Personal Computer (PC)
105 Monitor 106 Network 110 Inspection board

Claims (14)

Take an image of the inspection area,
Create a grayscale image based on brightness from the image,
Measure the maximum and minimum values of brightness in the grayscale image,
The threshold value of the brightness of the grayscale image is determined by the following formula:
Threshold (Th) = Minimum value + (Maximum value−Minimum value) × A
(However, A = 0 to 1)
Create a binary image from the grayscale image using the brightness threshold,
An inspection method comprising: determining whether the inspection area is good or bad based on the binary image. Take an image of the inspection area,
Create a grayscale image based on brightness from the image,
Create a histogram based on the grayscale image,
Calculating skewness from the histogram,
An inspection method comprising: determining whether the inspection area is good or bad from the skewness. Take an image of the inspection area including the amorphous and polycrystalline areas,
Create a grayscale image based on brightness from the image,
Create a histogram based on the grayscale image,
Calculating skewness from the histogram,
An inspection method, wherein the degree of progress of crystallization from amorphous to polycrystalline is determined based on the degree of distortion to determine whether the inspection region is good or bad. Take an image of the inspection area including the amorphous and polycrystalline areas,
Create a grayscale image based on brightness from the image,
Measure the maximum and minimum values of brightness in the grayscale image,
The threshold value of the brightness of the grayscale image is determined by the following formula:
Threshold (Th) = Minimum value + (Maximum value−Minimum value) × A
(However, A = 0 to 1)
Create a binary image based on the brightness threshold from the grayscale image,
Detecting a connected region in the binary image;
An inspection method comprising: determining whether the inspection area is good or bad from a ratio of the connected area to all pixels of the binary image. Take an image of the inspection area including the amorphous and polycrystalline areas,
Create a grayscale image based on brightness from the image,
Measure the maximum and minimum values of brightness in the grayscale image,
The threshold value of the brightness of the grayscale image is determined by the following formula:
Threshold (Th) = Minimum value + (Maximum value−Minimum value) × A
(However, A = 0 to 1)
Create a binary image based on the brightness threshold from the grayscale image,
Detecting a connected region in the binary image;
An inspection method comprising: determining whether the inspection area is good or bad from the number or size of the connection areas. Take an image of the inspection area including the amorphous and polycrystalline areas,
Create a grayscale image based on brightness from the image,
Create a histogram based on the grayscale image,
Calculating skewness from the histogram,
A first step of judging the degree of progress of crystallization from amorphous to polycrystalline according to the degree of distortion and judging whether the inspection area is good or bad;
The threshold value of the brightness of the grayscale image is determined by the following formula:
Measure the maximum and minimum values of brightness in the grayscale image,
Threshold (Th) = Minimum value + (Maximum value−Minimum value) × A
(However, A = 0 to 1)
Create a binary image based on the brightness threshold from the grayscale image,
Detecting a connected region in the binary image;
A second step of calculating a ratio of the connected area with respect to all pixels of the binary image and determining whether the inspection area is good or bad;
An inspection method comprising a third step of determining whether the inspection area is good or bad from the number or size of the connection areas. In claim 1, or any one of claims 4 to 6,
The maximum value and the minimum value are values obtained by removing noise from the maximum value and the minimum value obtained from the histogram. Means for capturing an image of the inspection area;
Means for creating a grayscale image from the image based on brightness;
Means for measuring maximum and minimum values of brightness in the grayscale image;
Means for determining a lightness threshold of the grayscale image by the following equation:
Threshold (Th) = Minimum value + (Maximum value−Minimum value) × A
(However, A = 0 to 1)
Means for creating a binary image from the grayscale image using the brightness threshold;
An inspection apparatus comprising: means for determining whether the inspection area is good or defective based on the binary image. Means for capturing an image of the inspection area;
Means for creating a grayscale image from the image based on brightness;
Means for creating a histogram based on the grayscale image;
Means for calculating skewness from the histogram;
An inspection apparatus comprising: means for determining whether the inspection area is good or bad from the skewness. Means for capturing an image of an inspection region including an amorphous region and a polycrystalline region;
Means for creating a grayscale image from the image based on brightness;
Means for creating a histogram based on the grayscale image;
Means for calculating skewness from the histogram;
An inspection apparatus comprising: means for determining a progress degree of crystallization from amorphous to polycrystal according to the degree of distortion and determining whether the inspection area is good or bad. Means for capturing an image of an inspection region including an amorphous region and a polycrystalline region;
Means for creating a grayscale image from the image based on brightness;
Means for measuring maximum and minimum values of brightness in the grayscale image;
Means for determining a lightness threshold of the grayscale image by the following equation:
Threshold (Th) = Minimum value + (Maximum value−Minimum value) × A
(However, A = 0 to 1)
Means for creating a binary image from the grayscale image based on the brightness threshold;
Means for detecting a connected region in the binary image;
An inspection apparatus comprising: means for determining whether the inspection area is good or bad from a ratio of the connection area to all pixels of the binary image. Means for capturing an image of an inspection region including an amorphous region and a polycrystalline region;
Means for creating a grayscale image from the image based on brightness;
Means for measuring maximum and minimum values of brightness in the grayscale image;
Means for determining a lightness threshold of the grayscale image by the following equation:
Threshold (Th) = Minimum value + (Maximum value−Minimum value) × A
(However, A = 0 to 1)
Means for creating a binary image from the grayscale image based on the brightness threshold;
Means for detecting a connected region in the binary image;
An inspection apparatus comprising: means for determining whether the inspection area is good or bad from the number or size of the connection areas. Means for capturing an image of an inspection region including an amorphous region and a polycrystalline region;
Means for creating a grayscale image from the image based on brightness;
Means for creating a histogram based on the grayscale image;
Means for calculating skewness from the histogram;
Means for determining the progress of crystallization from amorphous to polycrystalline according to the degree of distortion;
Means for measuring maximum and minimum values of brightness in the grayscale image;
Means for determining a lightness threshold of the grayscale image by the following equation:
Threshold (Th) = Minimum value + (Maximum value−Minimum value) × A
(However, A = 0 to 1)
Means for creating a binary image from the grayscale image based on the brightness threshold;
Means for detecting a connected region in the binary image;
Means for calculating a ratio of the connected region to all pixels of the binary image;
An inspection apparatus comprising: means for calculating the number or size of the connection regions. In claim 8, or any one of claims 11 to 13,
The inspection apparatus according to claim 1, wherein the maximum value and the minimum value are values obtained by removing noise from the maximum value / minimum value obtained from the histogram.