JP5257063B2 - Defect detection method and defect detection apparatus - Google Patents

Description

  The present invention relates to a defect detection method for accurately and automatically detecting a defect displayed at a low contrast on the captured image due to scratches or spots on the inspection object by processing an image obtained by imaging the inspection object. And an apparatus.

  In the defect detection of foreign matters and scratches on the wiring sealing surface of the flexible printed circuit board, the defect detection is performed using a filter process which is one of image processing methods. That is, when an object to be inspected with a foreign object or a flaw is imaged, the brightness of the part of the foreign object or the flaw in the captured image is different from the surrounding area. However, since the luminance difference is small, that is, the contrast is low, defect detection is performed by emphasizing the luminance difference using a filter.

  As such a defect detection method, two comparison pixels that are equidistant from the target pixel are selected, and the presence / absence of a defect is determined by comparing the inner brightness difference, the outer brightness difference, and the target pixel brightness data. (Patent Document 1).

JP 2003-14580 A

  By the way, in the method described in Patent Document 1, there is a particular problem when the defective pixel includes only a bright defective pixel that becomes brighter than the normal pixel or only a dark defective pixel that becomes darker than the normal pixel. Does not occur. However, when dark defects and bright defects are mixed, for example, when detecting a bright defect, even if the inspection target pixel is normal, any of the comparison pixels set to surround the periphery of the inspection target pixel. If there is a dark defect, the luminance of the pixel group becomes darker than the pixel to be inspected, so that the luminance of the pixel to be inspected is determined to be relatively bright and erroneously detected as a bright defect. There's a problem.

  The present invention has been made in view of the above-described problems, and a defect detection method and defect detection that can improve the accuracy of defect detection of an inspection object even when bright defects and dark defects are mixed. An object is to provide an apparatus.

  According to the defect detection method of the present invention, a defect enhancement processing step for performing defect enhancement processing on a captured image obtained by imaging an inspection object, and a defect based on a defect enhancement value of each pixel obtained in the defect enhancement processing step. A defect detection step for detecting, and the defect enhancement processing step includes: a defect mask processing step for performing mask processing on a region of one of the defect components of the bright defect and the dark defect of the captured image; and the captured image In the inspection target pixel selection step of sequentially selecting the inspection target pixels, a plurality of comparison target pixels that are separated from the center of the selected inspection target pixels by a predetermined distance are arranged around the inspection target pixels, and the comparison target pixels are a plurality of the comparison target pixels. The comparison target pixel group setting step that is set separately for the comparison target pixel group, and the comparison that is not masked in the defect mask processing step among the comparison target pixels included in the comparison target pixel group The luminance difference data that is the difference between the luminance value of the elephant pixel and the luminance value of the inspection target pixel is obtained, and the minimum luminance difference that minimizes the value among the luminance difference data is obtained for each comparison target pixel group. A luminance difference calculation step, and a defect enhancement value calculation step in which a minimum luminance difference having a maximum value among the minimum luminance differences calculated for each comparison target pixel group is used as a defect enhancement value of the inspection target pixel. It is characterized by.

In addition, what is necessary is just to set an inspection object pixel and a comparison object pixel in the imaging pixel unit of the CCD camera which imaged the to-be-inspected object, for example.
According to the present invention, in the defect emphasis processing step, first, a defect mask processing step is performed in which mask processing is performed on one of the defect component regions of the bright defect and the dark defect. Here, the area to be masked is a bright defect when a dark defect is detected by the defect detection method, and a dark defect when a bright defect is detected by the defect detection method. In the minimum luminance difference calculation step, the luminance value of the inspection target pixel selected in the inspection target pixel selection step and the luminance value of each comparison target pixel of the comparison target pixel group set in the comparison target pixel group setting step Calculate the difference. At this time, in the minimum luminance difference calculation step, when any one of the inspection target pixel and the comparison target pixel is included in the area masked in the defect mask processing step, that is, the inspection target pixel or the comparison target pixel is When the pixel is a mask pixel, the luminance difference is not calculated. Accordingly, in the minimum luminance difference calculation step, the luminance difference is calculated for pixels other than the mask pixel, and the minimum value is obtained from the luminance differences in each comparison target pixel group.
In the defect enhancement value calculation step, the maximum value is selected from the minimum luminance differences calculated for each of the comparison target pixel groups, and is set as the defect enhancement value of the inspection target pixel.
In such a defect detection method of the present invention, one of the bright defect and the dark defect is masked with respect to the captured image in which the bright defect and the dark defect are mixed, so that the other defect that is not masked. Can be detected with high accuracy. That is, when the defect mask processing step is not performed, for example, in the detection of a bright defect, when the comparison target pixel has a dark defect and the inspection target pixel has a normal luminance value, the luminance of the comparison target pixel and the inspection target pixel Since the difference between the values becomes large, there is a disadvantage that the pixel to be inspected is detected as a bright defect. On the other hand, in the present invention, as described above, since the dark defect is masked by the defect mask processing step, the brightness difference between the inspection target pixel and the comparison target pixel is ignored in the comparison target pixel group. Data can be calculated. Therefore, it is possible to avoid a decrease in accuracy due to the presence of dark defects, and it is possible to carry out accurate defect detection. The same applies to the case of detecting a dark defect. By performing a mask process on a bright defect, a dark defect detection process ignoring the bright defect can be performed, and the measurement accuracy can be improved.

Further, by calculating the defect emphasis value, it is possible to detect a defect including the inspection target pixel and not including the comparison target pixel, for example, a planar spot defect.
That is, when there is no defect in the inspection target pixel portion and there is no luminance difference with the surrounding pixels, the minimum luminance difference is a very small value. Further, even if the inspection target pixel has a defect, if the defect extends to any of the comparison target pixel portions, there is almost no luminance difference between the inspection target pixel and the comparison target pixel included in the defective portion. The minimum luminance difference is also a very small value.
On the other hand, if the inspection target pixel has a defect and the surrounding comparison target pixel has no defect, that is, if the spot defect is within the area surrounded by the comparison target pixel, the luminance of the inspection target pixel Since the value is different from the luminance value of any comparison target pixel, the minimum luminance difference is also a relatively large value. As a result, when there is a spot defect having a size that can be accommodated in the area surrounded by the comparison target pixels, the minimum luminance difference becomes a relatively large value, and the defect is emphasized.
In the present invention, since the plurality of comparison target pixels are divided into a plurality of comparison target pixel groups, a line defect can be detected in addition to a spot defect. That is, when there is a line defect that overlaps at least one of the plurality of comparison target pixels and the detection target pixel, the difference in luminance value of each pixel on the line defect is small. It cannot be detected.
On the other hand, when the plurality of comparison target pixels are divided into a plurality of comparison target pixel groups and the minimum luminance difference is calculated for each comparison target pixel group as in the present invention, the position of each comparison target pixel is different. Even if the line defect overlaps the comparison target pixel of one comparison target pixel group and the line defect cannot be detected, the comparison target pixel of the other comparison target pixel group does not overlap the line defect, and the line defect is not detected. It can be detected.
For this reason, if the maximum value of each minimum luminance difference calculated in the plurality of comparison target pixel groups is set as the defect enhancement value of the inspection target pixel, both the spot defect and the line defect can be enhanced. In particular, even if a line defect cannot be detected by any of the comparison target pixel groups, it can be detected by other comparison target pixel groups, so that detection sensitivity unevenness due to the angle of the line defect does not occur and detection sensitivity is improved. Can do.
In the present invention, since both a spot defect and a line defect can be detected simultaneously, the defect detection time can be shortened as compared with the case where a spot defect detection filter and a line defect detection filter are separately prepared and detected.

  In the defect detection method of the present invention, the number of mask pixels masked in the defect mask processing step among the comparison target pixels included in each comparison target pixel group set in the comparison target pixel group setting step is counted. The minimum luminance difference calculating step may calculate the minimum luminance difference for the comparison target pixel group when the number of the mask pixels counted by the counting step is less than a predetermined threshold value. preferable.

  According to the present invention, when the number of mask pixels counted in the counting step is equal to or greater than a predetermined threshold, the minimum value is not calculated for the comparison target pixel group by the minimum value calculating step. That is, in the minimum value calculating step, the luminance difference between the plurality of comparison target pixels that are not masked and the inspection target pixel is calculated, and the minimum value is selected from the calculated plurality of luminance values. At this time, when the comparison target pixel includes a mask pixel that is equal to or greater than the threshold value, the number of calculated luminance difference data decreases and the measurement accuracy decreases. For example, when detecting a bright defect, if three of the four comparison target pixels included in the comparison target pixel group are mask pixels, the defect is determined only by the remaining one comparison target pixel, and measurement is performed. The reliability is lowered, and in some cases, it may cause a false detection. On the other hand, in the present invention, in the minimum value calculating step, when the number of mask pixels in the comparison target pixel group is equal to or larger than the predetermined threshold, the minimum value is not calculated and the number of mask pixels is smaller than the predetermined threshold. Only calculate the minimum value. By adopting such a method, it is possible to avoid a decrease in measurement accuracy as described above, and it is possible to output only a reliable minimum value. Therefore, erroneous detection in defect detection can be prevented and measurement accuracy can be improved.

  In the defect detection method of the present invention, when the number of mask pixels for all the comparison target pixel groups is equal to or greater than the threshold value, the comparison target pixel group set in the comparison target pixel setting step is different. It is preferable to provide a comparison target pixel resetting step for setting a comparison target pixel group in which a plurality of comparison target pixels at pixel positions are arranged.

According to this invention, when the minimum luminance difference is not calculated for all the comparison target pixel groups, the comparison target pixel including a pixel different from the comparison target pixel previously set in the comparison target pixel group setting step A new group is set, and the minimum value calculation step is performed again based on the set comparison target pixel group to calculate the minimum luminance.
As the comparison target pixel included in the comparison pixel group, for example, a pixel whose distance from the inspection target pixel is located at a different distance from the comparison target pixel set in the comparison target pixel setting step may be selected. Good. For this, for example, a plurality of filters for setting the position of the comparison target pixel with respect to the inspection target pixel are prepared, and a filter other than the filter selected in the comparison target pixel setting step is selected in the comparison target pixel resetting step. Thus, the comparison target pixel group can be easily set.
In such a defect detection method, as described above, even when the number of masks in each comparison target pixel group is equal to or greater than a predetermined threshold, by setting different comparison target pixels, inconvenience such as a decrease in measurement accuracy can be prevented. Therefore, the measurement accuracy can be further improved.

  In the defect detection method of this invention, it is preferable that the said defect mask process process implements a mask process with respect to any one of the bright defect detected by the said defect detection method, and a dark defect.

In the defect mask processing step described above, preliminary detection processing for detecting in advance the target pixel for mask processing is required. As such preliminary detection processing, since it is not necessary to carry out detection with particularly high accuracy and it is only necessary to know the approximate defect position, for example, a conventional defect detection method can be used.
On the other hand, in the defect detection method, when both a bright defect and a dark defect are detected, one of the bright defect and the dark defect is detected first, and then the other is detected. At this time, in the present invention, when the detection of either one of the dark defect and the bright defect is completed first, the defect mask processing step is performed based on the detection result previously issued during the other defect detection process.
For example, when a bright defect is detected after detecting a dark defect by the defect detection method of the present invention, first, a bright defect is preliminarily detected using a conventional method, and in the defect mask processing step, the detected bright defect is detected. A mask process is performed on the above, and dark defects are detected with high accuracy by the above-described steps. Thereafter, when detecting a bright defect, in the defect mask processing step, mask processing is performed on the previously detected dark defect, and the bright defect is detected with high accuracy by the above-described steps. In such a defect detection method, since the detection result of the defect detection performed earlier can be used in the defect detection process (in the above example, during the bright defect detection process) performed later, the preliminary defect detection process is performed. It becomes unnecessary. Therefore, the processing time can be shortened and the processing load can be reduced.

  In the defect detection method of the present invention, the comparison target pixel group setting step selects 4 × n (n is an integer of 2 or more) comparison target pixels as the plurality of comparison target pixels, and these comparison target pixels Is preferably selected for each of four comparison target pixels arranged at intervals of 90 degrees in the circumferential direction centering on the inspection target pixel, and each comparison target pixel group is set.

  Note that the specific number of the 4 × n comparison target pixels may be set based on the distance between the inspection target pixel and the comparison target pixel, and is usually any of 8, 12, or 16 You only have to set it.

Furthermore, it is preferable that the comparison target pixels are arranged on the circumference and at equal intervals around the inspection target pixel. That is, an angle formed by a line segment connecting each comparison target pixel and the inspection target pixel and a line segment connecting another comparison target pixel and the inspection target pixel adjacent to the comparison target pixel is the same in each comparison target pixel. It is preferable.
Therefore, for example, when eight comparison target pixels are provided, they may be arranged at intervals of 45 degrees in the circumferential direction centering on the inspection target pixel. In this case, a first comparison target pixel group is configured by four inspection target pixels selected every other circumferential direction around the inspection target pixel, that is, four inspection target pixels arranged at intervals of 90 degrees. What is necessary is just to comprise a 2nd comparison object pixel group by the other four test object pixels which are not contained in this 1st comparison object pixel group.
Similarly, when 12 comparison target pixels are provided, they may be arranged at intervals of 30 degrees in the circumferential direction centering on the inspection target pixel. In this case, the first to third comparison target pixel groups are respectively formed by four inspection target pixels selected every two in the circumferential direction centering on the inspection target pixel, that is, four inspection target pixels arranged at intervals of 90 degrees. What is necessary is just to comprise.
Similarly, when 16 comparison target pixels are provided, they may be arranged at intervals of 22.5 degrees in the circumferential direction centering on the inspection target pixel. In this case, each of the first to fourth comparison target pixel groups is composed of four inspection target pixels selected every third in the circumferential direction around the inspection target pixel, that is, four inspection target pixels arranged at intervals of 90 degrees. What is necessary is just to comprise.

  Since each comparison target pixel group is composed of four comparison target pixels arranged at intervals of 90 degrees in the circumferential direction centering on the inspection target pixel, the presence or absence of a spot defect is confirmed with a minimum number of comparison target pixels. Even if there is a line defect that passes through two comparison target pixels arranged at point-symmetrical positions with respect to the inspection target pixel in a certain comparison target pixel group and the line defect cannot be detected, another comparison target pixel group Thus, the line defect can be reliably detected.

  In the defect detection method of the present invention, the comparison target pixel group setting step includes, as the plurality of comparison target pixels, eight comparison target pixels arranged at intervals of 45 degrees in a circumferential direction centering on the inspection target pixel. These eight comparison target pixels are selected for each of four comparison target pixels arranged at intervals of 90 degrees in the circumferential direction centering on the inspection target pixel, and the first comparison target pixel group and the first comparison target pixel group Two comparison target pixel groups may be set.

For example, a first comparison target pixel group is configured by four comparison target pixels provided on the upper and lower sides and the left and right sides of the inspection target pixel, and the upper right side, the lower right side, the upper left side, and the left side of the inspection target pixel. The second comparison target pixel group may be configured by four comparison target pixels provided obliquely below.
If two comparison target pixel groups each including four comparison target pixels are set, it is possible to detect a spot defect and a line defect with a minimum comparison target pixel. For this reason, the defect detection process by a filter can also be performed in a short time.

  In the defect detection method of the present invention, the minimum luminance difference calculating step detects a bright defect in which the luminance of the defective portion is higher than the surrounding luminance, from the luminance value of the inspection target pixel. When the luminance difference data is obtained by subtracting the luminance value, the minimum luminance difference of the luminance difference data is obtained, and a dark defect in which the luminance of the defective portion is lower than the surrounding luminance is detected, the comparison target pixel It is preferable to obtain luminance difference data by subtracting the luminance value of the pixel to be inspected from the luminance value, and obtain the minimum luminance difference of the luminance difference data.

  In the present invention, the minimum value of each luminance difference data obtained by subtracting the luminance value of the comparison target pixel from the luminance value of the inspection target pixel is used as the defect enhancement value for the bright defect of the inspection target pixel, and the luminance of the comparison target pixel Since the minimum value of each luminance difference data obtained by subtracting the luminance value of the inspection target pixel from the value is used as the defect enhancement value for the dark defect of the inspection target pixel, the defect enhancement value is obtained when the inspection target pixel is defective. It becomes a positive value, and bright defects and dark defects can be detected with high accuracy.

  In the defect detection method of the present invention, the defect detection step extracts a defect candidate pixel by comparing a defect emphasis value in the inspection target pixel with a predetermined threshold value, and extracts a defect candidate region constituted by the defect candidate pixel. It is characterized in that the defect content is discriminated from the feature amount.

  In the present invention, the defect contents are evaluated and discriminated based on the feature area such as the area of the defect portion, the average luminance, the maximum luminance, etc., so that the defect can be objectively evaluated, for example, the defect can be easily ranked. And defective products can be easily determined.

In the defect detection apparatus of the present invention, defect enhancement processing means for performing defect enhancement processing on a captured image obtained by imaging the inspection object, and defects based on the defect enhancement value of each pixel obtained by the defect enhancement processing means. Defect detection means for detecting, and the defect enhancement processing means performs defect mask processing means for performing mask processing on a region of one of the defect components of the bright defect and dark defect of the captured image, and the captured image. And a plurality of comparison target pixels that are separated from the center of the selected inspection target pixel by a predetermined distance around the inspection target pixel, and the comparison target pixels are a plurality of comparison target pixels. Comparison target pixel group setting means for setting the comparison target pixel group separately, and of the comparison target pixels included in the comparison target pixel group, the mask processing is not performed in the defect mask processing step. Luminance difference data, which is the difference between the luminance value of the comparison target pixel and the luminance value of the inspection target pixel, is obtained, and the minimum luminance difference that minimizes the value is obtained for each comparison target pixel group. A minimum luminance difference calculating unit; and a defect enhancement value calculating unit that uses a minimum luminance difference having a maximum value among the minimum luminance differences calculated for each comparison target pixel group as a defect enhancement value of the inspection target pixel. It is characterized by that.
This defect detection apparatus can also provide the same effects as the defect detection method.

FIG. 1 is a configuration diagram of a computer apparatus which is a defect detection apparatus according to an embodiment of the present invention.
The defect detection apparatus according to the present embodiment detects a defect of an inspection object 1 such as a flexible substrate, a liquid crystal panel (TFT panel), or a semiconductor wafer. The inspection object 1 is placed on the XY stage 2 and configured to be movable in a plane.
It can be said that the defect detection apparatus includes a microscope 4, a CCD camera 5, a computer device 6, and a display device 7.

The microscope 4 is provided for enlarging the inspection object 1 and photographing it with the CCD camera 5, and a microscope having a sufficient magnification for detecting a defect of the inspection object 1 is used.
The CCD camera 5 is an imaging unit that images the inspection object 1 through the microscope 4.
The computer device 6 is an image processing means that controls the CCD camera 5 and detects the inspection object 1. The display device 7 is a display device such as a liquid crystal display connected to the computer device 6.

  The computer device 6 includes an image input unit 60, a defect enhancement processing unit 61, a defect extraction unit 62, and a defect determination unit 63. The defect extraction means 62 and the defect determination means 63 constitute the defect detection means of the present invention.

  Image data of a captured image captured by the CCD camera 5 is input to the image input means 60 of the computer device 6. The captured image is stored in a storage means (not shown). Accordingly, an image acquisition process (imaging process) is performed in which the image input means 60 images the inspection object using the CCD camera 5.

  The defect enhancement processing unit 61 performs a defect enhancement processing step of performing defect enhancement processing on the acquired image, and includes a defect mask region setting unit 615 that is a defect mask processing unit, an inspection target pixel selection unit 611, and the like. A comparison target pixel group setting unit 612, a counting unit 616, a minimum luminance difference calculation unit 613, and a defect enhancement value calculation unit 614.

The defect mask area setting means 615 performs a defect mask area setting process for performing a mask process on a defective area in the captured image.
In other words, the defect mask area setting unit 615 sets a mask so as not to perform filter processing on pixels that satisfy a predetermined condition in order to reduce false detection due to filter processing in a captured image in which bright and dark defects are mixed. .
Specifically, when detecting a bright defect in the captured image, the defect mask region setting unit 615 replaces the luminance data in the region where the dark defect component exists from the captured image with a mask value, and sets it as a mask pixel. The mask value is not particularly limited as long as it is a value recognizable as a mask pixel, but “−1” is adopted in the embodiment. The mask value is not limited to “−1”, and may be set to any value as long as it can be easily recognized as a mask pixel. For example, in the case of a CCD camera, the brightness of an image is usually digitized from 0 to 255. However, since a value of 0 is not output depending on the camera, this “0” value may be used as a mask value. Further, since it is sufficient that the difference between the masked pixel and the non-masked pixel is clear, for example, a mask-dedicated pixel may be prepared and the mask position may be managed by the pixel.

  The inspection target pixel selection unit 611 performs an inspection target pixel selection step of sequentially selecting inspection target pixels in the captured image.

The comparison target pixel group setting unit 612 arranges a plurality of comparison target pixels that are separated by a predetermined distance from the center of the selected inspection target pixel, and divides these comparison target pixels into a plurality of comparison target pixel groups. The comparison target pixel group setting step is set.
Specifically, the comparison target pixel group setting unit 612 sets a comparison target pixel using a defect enhancement filter as shown in FIG. FIG. 5 is a diagram illustrating an example of a defect emphasis filter indicating the arrangement position of the comparison target pixel set with respect to the inspection target pixel in the captured image. Such a defect enhancement filter is stored in advance in a storage unit (not shown), and is appropriately read out by the comparison target pixel group setting unit 612 and applied to the imaging data.
As a defect enhancement filter, FIG. 5 shows an example of a defect enhancement filter in which the distance from the inspection target pixel to the comparison target pixel is 7 pixels, but the distance from the inspection target pixel to the comparison target pixel is 6 pixels. A plurality of defect emphasis filters, a defect emphasis filter in which the distance from the inspection target pixel to the comparison target pixel is 8 pixels or more are stored in a plurality of storage means. The comparison target pixel group setting unit 612 then selects the defect enhancement filter currently selected from the storage unit when the comparison target pixel group includes the mask pixels masked by the defect mask region setting unit 615 at a predetermined threshold value or more. The other defect enhancement filters different from the above are read out and applied to the imaging data to set a comparison target pixel group.

The counting means 616 counts the number of comparison pixels in the mask region in the comparison target pixel group, that is, the number of mask pixels.
The minimum luminance difference calculating unit 613 obtains luminance difference data that is a difference between the luminance value of each comparison target pixel included in the comparison target pixel group and the luminance value of the inspection target pixel, and among the luminance difference data, A minimum luminance difference calculating step for obtaining a minimum luminance difference having a minimum value for each comparison target pixel group is performed.
The defect emphasis value calculating means 614 performs a defect emphasis value calculating step in which the minimum luminance difference having the maximum value among the minimum luminance differences calculated for each comparison target pixel group is used as the defect emphasis value of the inspection target pixel. Is.

  The defect includes a bright defect having a higher luminance value than other pixel portions and a dark defect having a lower luminance value. For this reason, the defect enhancement value calculation means 614 of the present embodiment is configured to separately calculate the defect enhancement value for bright defects and the defect enhancement value for dark defects.

The defect extraction unit 62 extracts defect candidates by comparing the result processed by the defect enhancement processing unit 61 with a predetermined threshold. As the threshold values, a bright defect threshold value and a dark defect threshold value are set, a bright defect region is extracted by comparing the bright defect enhancement result with the bright defect threshold value, and the dark defect enhancement result is compared with the dark defect threshold value. Thus, the dark defect area is extracted.
Alternatively, the defect candidate extraction process by the defect extraction unit 62 may be executed after applying a median filter or the like to the image processed by the defect enhancement processing unit 61 to perform noise removal processing.

  The defect discriminating means 63 evaluates the rank of the defect based on the extracted defect area area, average luminance, maximum luminance, etc., and executes a defect discriminating process for classifying which defect rank the current inspection object corresponds to. To do.

[Operation of defect detection device]
Next, the operation of the defect detection apparatus according to the embodiment of the present invention will be described.
FIG. 2 is a flowchart for explaining the operation of the defect detection apparatus of this embodiment. The operation shown in FIG. 2 is realized by a program executed on the computer device 6.

First, when the inspection object 1 is set on the XY stage 2, the image input means 60 of the computer device 6 captures an image of the inspection object 1 with the CCD camera 5 and captures an image of the captured data. (Imaging step) is performed (ST1). At this time, the photographing data is taken into the computer apparatus 6 as digital data of 4096 gradations (12 bits) by an A / D converter (not shown).
When the inspection object 1 is a display panel such as a liquid crystal panel, a specific image pattern may be displayed on the display panel so that defects can be easily detected. For example, there are an all-white screen pattern that displays the entire screen in white so that dark defects can be easily detected, an all-black screen pattern that displays all screens in black so that bright defects can be easily detected, and a halftone screen pattern. What is necessary is just to set suitably according to the defect kind to want.

  Next, the defect emphasis processing means 61 performs a defect emphasis processing step for emphasizing the defect on the acquired imaging data (ST2). In this defect enhancement processing step ST2, since it is difficult to detect a low-contrast defect as it is, only the defect in the image is enhanced. The defect enhancement processing step ST2 is performed according to the processing flow shown in FIG.

That is, as shown in FIG. 3, the defect emphasis processing means 61 first performs a defect mask area setting step for masking the acquired defect in the imaging data by the defect mask area setting means 615 (ST21).
In this defect mask region setting step, a dark defect is masked in advance when a bright defect is detected, and a bright defect is masked in advance when a dark defect is detected. In this embodiment, first, a dark defect detection process for detecting a dark defect is performed, and a bright defect detection process is performed after the dark defect detection process. Therefore, in the defect mask region setting step, first, a process for masking a bright defect is performed.
In this defect mask region setting step, after performing a preliminary defect detection step for detecting pixels for performing mask processing, a defect mask processing step for performing mask processing on the detected defect is performed. Here, as the preliminary defect detection step, a conventional bright defect detection method can be used, but in this embodiment, the preliminary defect detection step according to ST211 to ST218 shown in FIG. 4 is performed.

That is, in the preliminary defect detection step, first, the defect enhancement processing means 61 of the computer device 6 selects an inspection target pixel to be inspected and executes an inspection target pixel selection step (ST211).
In the present embodiment, the target pixel is selected for each imaging pixel unit of the CCD camera 5.

Next, the defect enhancement processing means 61 performs a comparison target pixel group setting step (ST212).
That is, the defect enhancement processing means 61 applies a defect enhancement filter as shown in FIG. 5 to the imaging data, sets eight comparison target pixels S1 to S8 in the circumferential direction centering on the inspection target pixel O, and Further, these comparison target pixels S1 to S8 are set in two comparison target pixel groups.

In the present embodiment, the comparison target pixels S1 to S8 are arranged at intervals of 45 degrees in the circumferential direction centering on the inspection target pixel O.
Specifically, the comparison target pixels S1 and S5 are arranged vertically (vertical direction) with the inspection target pixel O interposed therebetween, and the comparison target pixels S7 and S3 are arranged left and right (horizontal direction) with the inspection target pixel O interposed therebetween. ing. Further, the comparison target pixels S2 and S6 are arranged in a diagonal direction (from the upper right to the lower left) with the inspection target pixel O in between, and in an oblique direction (from the upper left to the lower right direction with the inspection target pixel O in between). ) Are compared pixels S8 and S4.
The comparison target pixels S1 and S5, the comparison target pixels S2 and S6, the comparison target pixels S3 and S7, and the comparison target pixels S4 and S8 are set at point-symmetric positions with the inspection target pixel O as the center.

  Then, in the comparison target pixel group selection step ST212, these comparison target pixels S1 to S8 are divided into two comparison target pixel groups and set. That is, the first comparison target pixel group is set by the comparison target pixels S1, S3, S5, and S7 that are alternately arranged in the circumferential direction, and the second comparison is performed by the remaining comparison target pixels S2, S4, S6, and S8. A target pixel group is set.

  Next, the defect enhancement processing unit 61 selects the comparison target pixels S1 to S8 one by one for each comparison target pixel group by the minimum luminance difference calculation unit 613, and sets the luminance value of the selected pixel and the inspection target pixel O. Luminance difference data, which is a difference from the luminance value, is obtained, and a minimum luminance difference calculation step for obtaining a minimum luminance difference having a minimum value among the luminance difference data is executed (ST213).

  Specifically, in the minimum luminance difference calculation step ST213, the minimum luminance difference calculation means 613 first selects each pixel of the first comparison target pixel group one by one from the luminance value of the inspection target pixel O. The luminance difference data F is obtained by subtracting the luminance values of the comparison target pixels S1, S3, S5, and S7. That is, when the luminance value of the inspection target pixel O is “O” and the luminance values of the comparison target pixels S1, S3, S5, and S7 are “S1, S3, S5, and S7”, The luminance difference data F1, F3, F5, and F7 are calculated using this.

F1 = O-S1 (Formula 1)
F3 = O-S3 (Formula 2)
F5 = O-S5 (Formula 3)
F7 = O-S7 (Formula 4)

Next, the minimum luminance difference calculation unit 613 uses Equation 5 below to calculate the minimum luminance difference D1 having the smallest value among the luminance difference data F1, F3, F5, and F7 of the first comparison target pixel group. Ask for.
D1 = Min (F1, F3, F5, F7) (Formula 5)

Next, the minimum luminance difference calculation unit 613 sequentially selects each pixel of the second comparison target pixel group one by one, and calculates each comparison target pixel S2, S4, S6, S8 from the luminance value of the inspection target pixel O. The luminance difference data F is obtained by subtracting the luminance value. That is, when the luminance value of the inspection target pixel O is “O” and the luminance values of the comparison target pixels S2, S4, S6, and S8 are “S2, S4, S6, and S8”, The luminance difference data F2, F4, F6, and F8 are calculated using this.
Further, the minimum luminance difference calculation means 613 uses the following equation 10 to calculate the minimum luminance difference D2 having the minimum value among the luminance difference data F2, F4, F6, and F8 of the second comparison target pixel group. Ask.

F2 = O-S2 (Formula 6)
F4 = O-S4 (Formula 7)
F6 = O-S6 (Formula 8)
F8 = O-S8 (Formula 9)
D2 = Min (F2, F4, F6, F8) (Formula 10)

  Next, the defect emphasis processing means 61 uses the defect emphasis value calculation means 614 to calculate the smallest luminance difference D1, D2 calculated for each of the first and second comparison target pixel groups as the inspection target pixel O. A defect emphasis value calculation step for setting the defect emphasis value at the position of is performed (ST214).

  In this embodiment, in order to detect the dark defect first, in the preliminary defect detection step, the comparison target pixel S1 is calculated from the luminance value of the inspection target pixel O using the above formulas 1 to 4 and formulas 6 to 9. The bright defect candidates are detected by subtracting the luminance values S1 to S8 of .about.S8 to obtain the luminance difference data F1 to F8. For example, when detecting a bright defect first, the dark defect candidate is detected in the preliminary defect detection step. In this case, the luminance difference data F1 to F8 may be obtained by subtracting the luminance value of the inspection target pixel O from the luminance values S1 to S8 of the comparison target pixels S1 to S8. Specifically, the luminance difference data F1 to F8 may be obtained using the following formulas 11 to 18.

F1 = S1-O (Formula 11)
F3 = S3-O (Formula 12)
F5 = S5-O (Formula 13)
F7 = S7-O (Formula 14)
F2 = S2-O (Formula 15)
F4 = S4-O (Formula 16)
F6 = S6-O (Formula 17)
F8 = S8-O (Formula 18)

  Thereafter, as shown in FIG. 4, the defect enhancement processing means 61 determines whether or not the defect enhancement processing has been completed over the entire acquired image (ST215). Another inspection target pixel O is selected by moving (ST211), and a comparison target pixel group setting step ST212, a minimum luminance difference calculation step ST213, and a defect enhancement value calculation step ST214 are performed. That is, since the inspection target pixel O is set for each imaging pixel of the CCD camera 5, the inspection target pixels O may be sequentially moved for each imaging pixel, and the steps ST211 to ST214 may be sequentially performed.

  On the other hand, if the processing has been completed in ST215, the defect enhancement processing means 61 generates a defect enhanced image using the defect enhancement value calculated for each pixel (ST216). In the present embodiment, a bright defect-enhanced image is generated with a defect-enhancement value for bright defects. However, as described above, when a dark defect is first detected in the preliminary defect detection step, a defect for dark defect is detected. A dark defect enhanced image is generated based on the enhanced value.

Thereafter, the defect extraction means 62 of the defect enhancement processing means 61 sets a threshold for extracting the defect in the defect enhancement image, and executes a defect candidate extraction process for extracting defect candidates (ST217).
That is, the defect extraction means 62 sets a threshold value for extracting a bright defect in the bright defect enhanced image, and extracts a bright defect candidate region. At this time, the defect extraction means 62 detects a region above the bright defect threshold as a bright defect region for the bright defect enhancement result.
Here, each threshold value may be set to an optimum value according to the situation of the image. For example, the average value of the defect-enhanced image and its standard deviation may be obtained, and the threshold value may be set using the following equation.

  Bright defect threshold wslevel = avr (bright) + α1 · σ (bright) + β1

  Here, avr (bright) is the average value of each defect-enhanced image, σ (bright) is the standard deviation of each defect-enhanced image, and α1, α2, β1, and β2 are arbitrary numbers depending on the situation of the image to be inspected. It is determined.

  Thereafter, the defect mask area setting unit 615 performs a mask process on each pixel of the detected bright defect candidate area to generate a mask image (ST218). That is, the defect mask area setting means 615 generates a mask image in which “−1” that is a mask value is set as the luminance value of each pixel in the bright defect candidate area detected in ST217. Here, the defect mask region setting means 615 shows an example in which −1 is set as the mask value. However, as described above, for example, the defect mask region setting unit 615 may be set to “0”, which clearly indicates that the mask value is set. Any value that can be recognized may be set.

  After the defect mask region setting process in ST211 to ST218, as shown in FIG. 3, the defect emphasis processing unit 61 uses the inspection target pixel selection unit 611 to select the inspection target pixel to be inspected. Is executed (ST22). In this inspection target pixel selection step ST22, the inspection target pixel is selected by the same processing as in ST211 described above.

  Next, similarly to ST212, the defect enhancement processing unit 61 executes the comparison target pixel group setting step by the comparison target pixel group setting unit 612, and every other comparison target pixel S1, S3, S5 in the circumferential direction. , S7, the first comparison target pixel group is set, and the remaining comparison target pixels S2, S4, S6, S8 are set as the second comparison target pixel group (ST23).

Next, the defect emphasis processing means 61 performs a counting process in which the counting means 616 counts the number of mask pixels set in ST218 from the comparison pixels included in the first and second comparison pixel groups (ST24). ).
Here, the counting unit 616 includes a first counting step for counting the number of mask pixels (first mask number) included in the first comparison target pixel group, and a mask pixel included in the second comparison target pixel group. A second counting step is performed to count the number of (second mask number).

  Thereafter, the defect enhancement processing means 61 determines whether or not the first mask number counted in the counting step ST24 is equal to or greater than a predetermined threshold value (2 in the present embodiment) (ST25). When it is determined in the process of ST25 that the first mask number is 2 or more, the defect enhancement processing means 61 sets 0 as the first minimum luminance difference D1 (ST26).

  On the other hand, when it is determined in ST25 that the number of first masks is less than 2, the defect enhancement processing unit 61 uses the minimum luminance difference calculation unit 613 to compare pixels S1, S3, S5 for each comparison target pixel group. , S7 are selected pixel by pixel, luminance difference data that is the difference between the luminance value of the selected pixel and the luminance value of the inspection target pixel O is obtained, and the minimum luminance difference that minimizes the value among these luminance difference data The minimum luminance difference calculation step for obtaining is performed (ST27).

  Specifically, similarly to ST213, the minimum luminance difference calculation unit 613 sequentially selects each pixel of the first comparison target pixel group one by one, and then compares each comparison target pixel S1 from the luminance value of the inspection target pixel O. , S3, S5, and S7 are subtracted to obtain the luminance difference data F. That is, when the luminance value of the inspection target pixel O is “O” and the luminance values of the comparison target pixels S1, S3, S5, and S7 are “S1, S3, S5, and S7”, The luminance difference data F1, F3, F5, and F7 are calculated using this.

F1 = S1-O (Formula 19)
F3 = S3-O (Formula 20)
F5 = S5-O (Formula 21)
F7 = S7-O (Formula 22)

  Here, when “−1” indicating that one of the luminance values of the inspection target pixel O and the comparison target pixels S1, S3, S5, and S7 is a mask pixel is set, the expression Do not calculate the brightness difference by. For example, in Equation 19, when any one of the inspection target pixel O and the comparison target pixel S1 is a mask pixel, the difference value F1 is not calculated.

Next, the minimum luminance difference calculation unit 613 uses the following Expression 23 to calculate the first minimum luminance that has the minimum value among the luminance difference data F1, F3, F5, and F7 of the first comparison target pixel group. The difference D1 is obtained.
D1 = Min (F1, F3, F5, F7) (Formula 23)

  Here, if the inspection target pixel O is a mask pixel, none of the difference values F1, F3, F5, and F7 is calculated, so 0 is set as the first minimum luminance difference D1.

  Next, after ST26 or ST27, the defect enhancement processing means 61 determines whether or not the second mask number counted in the counting step ST24 is greater than or equal to a predetermined threshold (2 in the present embodiment) (ST28). ). When it is determined in ST28 that the second mask number is less than 2, the defect enhancement processing unit 61 uses the minimum luminance difference calculation unit 613 to compare the comparison target pixels S2, S4, S6 and S6 for each comparison target pixel group. S8 is selected for each pixel, luminance difference data that is the difference between the luminance value of the selected pixel and the luminance value of the inspection target pixel O is obtained, and the minimum luminance difference that minimizes the value among the luminance difference data is determined. The minimum luminance difference calculation step to be obtained is executed (ST29).

  Specifically, it is substantially the same as ST27, and the minimum luminance difference calculation means 613 sequentially selects each pixel of the second comparison target pixel group one by one, and then compares each comparison target from the luminance value of the inspection target pixel O. The luminance difference data F is obtained by subtracting the luminance values of the pixels S2, S4, S6, and S8. That is, when the luminance value of the inspection target pixel O is “O” and the luminance values of the comparison target pixels S2, S4, S6, and S8 are “S2, S4, S6, and S8”, The luminance difference data F1, F3, F5, and F7 are calculated using this.

F2 = S2-O (Formula 24)
F4 = S4-O (Formula 25)
F6 = S6-O (Formula 26)
F8 = S8-O (Formula 27)

  Here, as in ST27, if any one of the luminance values of the inspection target pixel O and the comparison target pixels S2, S4, S6, and S8 is “−1” that is a mask pixel, Do not calculate the brightness difference using the formula. For example, in Expression 24, when any one of the inspection target pixel O and the comparison target pixel S2 is a mask pixel, the difference value F2 is not calculated.

Next, the minimum luminance difference calculation unit 613 uses the following expression 28 to calculate the second minimum luminance that has the minimum value among the luminance difference data F2, F4, F6, and F8 of the first comparison target pixel group. The difference D2 is obtained.
D2 = Min (F2, F4, F6, F8) (Formula 28)

  Here, when the inspection target pixel O is a mask pixel, since the difference values F2, F4, F6, and F8 are not calculated, 0 is set as the second minimum luminance difference D2.

  On the other hand, when it is determined in ST28 that the second mask number is 2 or more, the defect enhancement processing means 61 determines whether or not the first minimum luminance difference D1 obtained in the processing of ST25 to ST27 is 0. Judgment is made (ST30). If it is determined in ST30 that the first minimum luminance difference D1 = 0, the defect enhancement processing means 61 sets 0 as the second minimum luminance difference D2 (ST31).

In ST30, when it is determined that the first minimum luminance difference D1 = 0, the defect enhancement processing unit 61 causes the comparison target pixel group setting unit 612 to set the comparison target pixel group again (comparison target pixel group reconstruction). The setting process is performed. That is, the comparison target pixel group setting unit 612 reads out another defect enhancement filter whose distance from the inspection target pixel to the comparison target pixel is different from the previously set defect enhancement filter from the storage unit, and this read defect The enhancement target pixel group is reset by applying the enhancement filter to the imaging data (ST32). In ST32, the defect enhancement processing unit 61 also determines whether or not the inspection target pixel O is a mask pixel. When the inspection target pixel O is a mask pixel, 0 is calculated as the minimum luminance values D1 and D2 even if the filter replacement is performed as described above. In this case, the minimum luminance values D1 and D2 are Set the value as 0. When the inspection target pixel O is a mask pixel, there is a high possibility that it is a bright defect. However, since a process for detecting a dark defect is performed here, no special process is performed.
Further, after this ST32, the defect emphasis processing means 61 carries out the minimum luminance difference calculation processing (ST25 to ST32) again from the counting step ST24.

  By the process of ST32, the distance between the inspection target pixel O and the comparison target pixels S1 to S8 is set according to the size of the spot defect to be detected. That is, since the defect is emphasized by the luminance difference between the inspection target pixel O and the comparison target pixels S1 to S8, the spot defect cannot be detected unless the size is within the area surrounded by the comparison target pixels S1 to S8. By appropriately changing the defect emphasis filter in ST32, it becomes possible to perform defect detection with accuracy using an appropriate defect emphasis filter.

  Next, the defect emphasis processing means 61 uses the defect emphasis value calculation means 614 to calculate the smallest luminance difference D1, D2 calculated for each of the first and second comparison target pixel groups as the inspection target pixel O. A defect emphasis process is performed with the defect emphasis value at the position (ST33).

  As shown in FIG. 3, the defect emphasis processing means 61 determines whether or not the defect emphasis processing has been completed over the entire acquired image (ST34). If not, the inspection target pixel O is moved. Another inspection target pixel O is selected (ST22), and a comparison target pixel group setting step ST23, a counting step ST24, a minimum luminance difference calculation step ST25 to ST32, and a defect enhancement value calculation step ST33 are performed. That is, since the inspection target pixel O is set for each imaging pixel of the CCD camera 5, the inspection target pixel O may be sequentially moved for each imaging pixel and the steps ST22 to ST33 may be sequentially performed.

On the other hand, if the processing has been completed in ST34, the defect enhancement processing means 61 generates a defect enhanced image based on the defect enhancement value calculated for each pixel (ST35). That is, a dark defect emphasized image is generated based on the defect emphasis value for dark defects. In addition, although the case where a dark defect was detected was illustrated in the above, a bright defect emphasis image is produced | generated when a bright defect is detected.
Thus, the defect enhancement processing step ST2 is completed.

When the defect emphasis processing step ST2 is completed, as shown in FIG. 2, the defect extraction means 62 of the defect emphasis processing means 61 sets a threshold for cutting out the defect with respect to the defect enhancement image obtained in the defect enhancement processing step ST2. Then, a defect candidate extraction step for extracting defect candidates is executed (ST3).
In this defect candidate extraction step, defect candidates can be extracted by substantially the same processing as in ST217 described above.

  Dark defect threshold bslevel = avr (dark) + α2 · σ (dark) + β2

  Here, avr (dark) is an average value of each defect emphasized image, σ (dark) is a standard deviation of each defect emphasized image, and α1, α2, β1, and β2 are arbitrary numbers depending on the situation of the image to be inspected. It is determined.

Next, the defect discriminating means 63 performs a blob process on the dark defect extracted image extracted from the emphasized image, obtains the area of the region cut out as a defect candidate, the average luminance, and the maximum luminance, and uses these feature amounts. A defect determination step for classifying the defect rank is performed (ST4).
The defect rank obtained by the defect discriminating means 63 is displayed on the display device 7, and the inspector can easily grasp the defect rank of the inspection object 1.

Moreover, in the above, although the detection method of the dark defect was illustrated, it can detect with a method substantially the same as the above also about a bright defect. In the case where a bright defect is detected after detecting a dark defect in ST1 to ST4, the detection method can be further simplified.
That is, when dark defects are detected by ST1 to ST4, it is not necessary to perform the preliminary defect detection process of ST211 to 217 at the time of bright defect detection, and for dark defects detected by ST1 to ST4, The process of ST218, that is, the dark defect can be masked to form a mask pixel, and the processing can be speeded up.

[Effects of this embodiment]
According to this embodiment, there are the following effects.
(1) In the computer device 6, the defect mask area setting unit 615 of the defect enhancement processing unit 61 detects bright pixels and dark defects other than the defect to be inspected, for example, a dark defect. When detecting a light defect, the defect is masked to form a mask pixel. Then, the inspection target pixel selection unit 611 selects the inspection target pixel O. Further, the comparison target pixel group setting unit 612 arranges the plurality of comparison target pixels S1 to S8 around the inspection target pixel O, and sets the comparison target pixels S1 to S8 as two comparison target pixel groups. Set separately. Further, the minimum luminance difference calculation unit 613 obtains a difference between the luminance values of the comparison target pixels S1 to S8 that are not mask pixels and the luminance value of the inspection target pixel O in each comparison target pixel group, and compares the comparison target pixels. Minimum luminance differences D1 and D2 having the smallest luminance difference data are calculated for each group. Then, the defect enhancement value calculation means 614 uses the smallest luminance difference D1, D2 as the defect enhancement value of the inspection target pixel O, which has a larger value.
For this reason, even when bright and dark defects are mixed in the imaging data, it is possible to prevent erroneous detection, etc., in which normal pixels are regarded as defects by masking defects other than those to be inspected. Can be improved. In addition, since bright defects and dark defects do not coexist, bright defect candidates can be easily detected only by the absolute value of the defect enhancement value.
In addition, by obtaining a minimum luminance difference in each comparison target pixel group, each comparison target pixel group includes the inspection target pixel O and a stain defect in a region surrounded by the comparison target pixels, It is possible to emphasize line defects other than line defects passing through the inspection target pixel O and any comparison target pixel. In addition, since each comparison target pixel group is different in position of the comparison target pixel, a line defect that cannot be emphasized in one comparison target pixel group can be emphasized in the other comparison target pixel group. By using a luminance difference having a larger value as the defect enhancement value of the inspection target pixel O, it is possible to emphasize and detect a spot defect and a line defect.

(2) The defect enhancement processing unit 61 includes a counting unit 616, and the counting unit 616 counts the number of mask pixels included in each comparison target pixel group. Then, the minimum luminance difference calculation unit 613 calculates the minimum luminance differences D1 and D2 when the first mask number and the second mask number counted by the counting unit 616 are each equal to or greater than a predetermined threshold (2 in the present embodiment). The minimum luminance differences D1 and D2 are calculated only when the first mask number and the second mask number are smaller than the predetermined threshold value.
When there are many mask pixels in the comparison target pixels, the number of luminance difference data for obtaining the minimum luminance differences D1 and D2 decreases, and the measurement accuracy and reliability deteriorate. On the other hand, in the above embodiment, in such a case, it is possible to prevent the measurement accuracy and the reliability from being lowered by setting the minimum luminance differences D1 and D2 to 0 without obtaining them.

(3) The comparison target pixel group setting means 612 of the defect enhancement processing means 61 is when both of the minimum luminance differences D1 and D2 calculated for the first comparison target pixel group and the second comparison target pixel group are zero. Then, another defect enhancement filter having a different distance from the inspection target pixel to the defect target pixel is selected and applied to the imaging data, and a new comparison target pixel group is reset. Thereby, an appropriate comparison target pixel can be set for the inspection target pixel, and measurement accuracy can be improved.
Further, as described above, even when there is a spot defect, an appropriate defect enhancement filter corresponding to the spot defect area can be selected, and the measurement accuracy for the spot defect can be improved.

(4) In this embodiment, after detecting a dark defect by the process of ST1-ST4, when detecting a bright defect, in ST218, a mask process is implemented with respect to the dark defect detected by said ST1-ST4. .
For this reason, the preliminary defect detection process of ST211 to ST217 can be omitted, and the processing can be speeded up.

(5) In ST32, the defect enhancement processing means 61 determines whether or not the inspection target pixel O is a mask pixel. If the inspection target pixel O is a mask pixel, 0 is set as the minimum luminance difference D1 and D2. Set.
That is, when the inspection target pixel O is a mask pixel, the calculation according to the above equations 19 to 27 is not performed and 0 is output regardless of the filter. In such a case, changing the defect emphasis filter a plurality of times wastes processing time. On the other hand, in the present embodiment, when the inspection target pixel O is a mask pixel, the filter change process of ST32 is not performed, and 0 is output as the minimum luminance values D1 and D2. Can be achieved.
(6) In this embodiment, the defect emphasis value is calculated by the difference between the luminance value of the inspection target pixel O and the luminance values of the comparison target pixels S1 to S8, and between the inspection target pixel O and the comparison target pixels S1 to S8. The luminance value of the point at is not used. For this reason, if the spot defect is smaller than the area surrounded by the comparison target pixels S1 to S8 and includes the inspection target pixel O, even if the size of the defect changes to some extent, it is visually observed by an inspector. A defect can be detected in the same manner as the determination. For this reason, the distance size between the inspection target pixel O and the comparison target pixels S1 to S8 does not need to be set very fine and can be set easily.

(7) Since the minimum luminance difference D1, D2 in each comparison target pixel group is obtained and used as a defect enhancement value, and the defect enhancement value is equal to or greater than a predetermined threshold value, it is recognized as a defect candidate. Can be reduced.

(8) Furthermore, in the minimum luminance difference calculation means 613, the formula for calculating the bright defect enhancement value and the formula for calculating the dark defect are set separately, so that the defect portion of the bright defect or dark defect is determined. Each can be emphasized with high precision, and both the bright defect and the dark defect can be detected easily and accurately by extracting the light defect threshold value and the dark defect threshold value with the defect extraction means 62, respectively. Can do. For this reason, various defects can be efficiently detected by a simple process.

(9) Since the rank of the extracted defect can be classified by the defect discriminating means 63, the objective ranking of the defect can be performed in a short time, and the inspector can easily determine the degree of the defect, and whether it is a non-defective product The determination of whether or not can be facilitated in a short time.

The present invention is not limited to the above embodiment.
For example, the distance between the inspection target pixel O and the comparison target pixels S1 to S8 in the defect enhancement filter for emphasizing the defect may be set to any distance as described above. What is necessary is just to set according to a magnitude | size.

In addition, although the example in which the distance from the inspection target pixel O to each comparison target pixel is set to an equal distance (for example, 7 pixels) is shown as the defect enhancement filter, the present invention is not limited to this. For example, a configuration using a defect enhancement filter in which a plurality of comparison target pixels having a distance of 7 pixels from the inspection target pixel O and a plurality of comparison target pixels having a distance of 15 pixels from the inspection target pixel O may be used. .
Furthermore, although the example of the defect emphasis filter in which the comparison target pixel is arranged along the circumferential direction is shown, for example, the defect enhancement filter in which the comparison target pixel is arranged along the elliptical shape or the comparison target along the rectangular shape A defect enhancement filter in which pixels to be compared are randomly arranged, such as a defect enhancement filter in which pixels are arranged, may be used.

Furthermore, in the above embodiment, eight comparison target pixels S1 to S8 are provided, and these comparison target pixels S1 to S8 are divided into two comparison target pixel groups. It is not limited to that of the embodiment.
For example, as shown in FIG. 6, twelve comparison target pixels S11 to S14, S21 to S24, and S31 to S34 are provided, and each comparison is arranged at intervals of 90 degrees in the circumferential direction centering on the inspection target pixel O. A defect enhancement filter in which a comparison target pixel group is configured for each of the target pixels S11 to S14, S21 to S24, and S31 to S34 may be used.

  Further, as shown in FIG. 7, 16 comparison target pixels S11 to S14, S21 to S24, S31 to S34, and S41 to S44 are provided and arranged at intervals of 90 degrees in the circumferential direction centering on the inspection target pixel O. A defect enhancement filter in which a comparison target pixel group is configured for each of the comparison target pixels S11 to S14, S21 to S24, S31 to S34, and S41 to S44 may be used.

  In the embodiment and the modification shown in FIGS. 6 and 7, four comparison target pixels are arranged in each comparison target pixel group, and these comparison target pixels are arranged at intervals of 90 degrees in the circumferential direction. However, the interval between the pixels to be compared is not limited to 90 ° intervals. However, if four comparison target pixels are provided and arranged at intervals of 90 degrees, the respective comparison target pixels can be arranged at equal intervals, and defects can be detected efficiently.

Further, in the above-described embodiment and the modification shown in FIGS. 6 and 7, four comparison target pixels are arranged in each comparison target pixel group. However, as shown in FIG. S18, S21 to S28 are provided, and a comparison target pixel group is configured for each of the comparison target pixels S11 to S18 and S21 to S28 arranged every other one in the circumferential direction centering on the inspection target pixel O. Two comparison target pixel groups having one comparison target pixel may be set.
In this case, since the comparison target pixels S11 to S18 and S21 to S28 are arranged at intervals of 45 degrees in each comparison target pixel group, the line defects that can be detected are compared with the above-described embodiment arranged at intervals of 90 degrees. The width dimension becomes smaller. That is, when detecting a line defect, it is necessary for the line defect to pass through a gap between each comparison target pixel. For this reason, when the comparison target pixels are arranged at intervals of 45 degrees, the width dimension of the line defect passing through the gap is also reduced. Therefore, when limiting the width dimension of the detected line defect, a filter as shown in FIG. 8 may be used.

  In the above-described embodiment, the defect determination unit 63 determines the defect rank based on the area of the defect portion or the like, but the defect may be determined by another method / procedure. In short, the defect discriminating unit 63 only needs to be able to determine whether or not it corresponds to a defect based on the defect emphasized by the defect enhancement processing unit 61.

  The present invention can detect if the captured image of the object to be inspected 1 has a portion having a luminance difference from the surroundings. For this reason, the present invention can be widely used for detecting foreign object defects on flexible substrates, detecting scratches and dirt on the surface of an object to be inspected, detecting brightness spot defects and color spot defects in various display devices, and the like.

The block diagram of the computer apparatus which is a defect detection apparatus by embodiment of this invention. The flowchart which shows the defect detection process of a defect detection apparatus. The flowchart which shows the defect emphasis process process in a defect detection process. The figure which shows the defect mask area | region setting process in a defect emphasis processing process. The figure which shows an example of the defect emphasis filter which shows the arrangement position of the comparison object pixel set with respect to the inspection object pixel in a captured image. The figure which shows the modification of a defect emphasis filter. The figure which shows the other modification of a defect emphasis filter. The figure which shows the other modification of a defect emphasis filter.

Explanation of symbols

  6... Computer apparatus which is a defect detection device 61. Defect emphasis processing means 62. Defect extraction means constituting defect detection means 63. Defect discrimination means constituting defect detection means 611. Inspection object pixel selection means 612 ... comparison target pixel group setting means, 613 ... minimum luminance difference calculation means, 614 ... defect enhancement value calculation means, 615 ... defect mask area setting means, 616 ... counting means.

Claims (9)

A defect enhancement processing step of performing defect enhancement processing on a captured image obtained by imaging the inspection object; and
A defect detection step of detecting a defect based on the defect enhancement value of each pixel obtained in the defect enhancement processing step,
The defect enhancement processing step includes a defect mask processing step of performing mask processing on a region of one of the defect components of the bright defect and the dark defect of the captured image, and an inspection target pixel for sequentially selecting the inspection target pixel in the captured image A selection process;
A comparison target pixel group setting step in which a plurality of comparison target pixels that are separated from the center of the selected inspection target pixel by a predetermined distance are arranged around the inspection target pixels, and these comparison target pixels are divided into a plurality of comparison target pixel groups and set. When,
Among the comparison target pixels included in the comparison target pixel group, luminance difference data that is a difference between the luminance value of the comparison target pixel that is not masked in the defect mask processing step and the luminance value of the inspection target pixel is obtained. A minimum luminance difference calculation step for obtaining a minimum luminance difference having a minimum value among the luminance difference data for each comparison target pixel group;
A defect enhancement value calculating step in which a minimum luminance difference having a maximum value among the minimum luminance differences calculated for each comparison target pixel group is a defect enhancement value of the inspection target pixel; and
A defect detection method comprising: 2. The defect detection method according to claim 1, wherein among the comparison target pixels included in each comparison target pixel group set by the comparison target pixel group setting step, the number of mask pixels masked in the defect mask processing step A counting step for counting
The defect detection method characterized in that the minimum luminance difference calculation step calculates the minimum luminance difference for the comparison target pixel group when the number of the mask pixels counted by the counting step is less than a predetermined threshold value. . The defect detection method according to claim 2,
When the number of mask pixels is greater than or equal to the threshold for all the comparison target pixel groups, a plurality of comparison target pixels at different pixel positions from the comparison target pixel group set in the comparison target pixel setting step A defect detection method comprising: a comparison target pixel resetting step for setting the arranged comparison target pixel group. In the defect detection method in any one of Claims 1-3,
The defect detection method, wherein the defect mask processing step performs mask processing on any one of a bright defect and a dark defect detected by the defect detection method. In the defect detection method in any one of Claims 1-4,
The comparison target pixel group setting step includes:
As the plurality of comparison target pixels, 4 × n (n is an integer of 2 or more) comparison target pixels are selected,
These comparison target pixels are selected for each of four comparison target pixels arranged at intervals of 90 degrees in the circumferential direction centering on the inspection target pixel, and each comparison target pixel group is set. Detection method. The defect detection method according to claim 5,
The comparison target pixel group setting step includes:
As the plurality of comparison target pixels, eight comparison target pixels arranged at intervals of 45 degrees in a circumferential direction centering on the inspection target pixel are selected.
These eight comparison target pixels are selected for each of four comparison target pixels arranged at intervals of 90 degrees in the circumferential direction centering on the inspection target pixel, and the first comparison target pixel group and the second comparison target pixel A defect detection method characterized by setting a pixel group. In the defect detection method in any one of Claims 1-6,
The minimum luminance difference calculation step includes:
When detecting a bright defect in which the luminance of the defective portion is higher than the surrounding luminance, the luminance difference data is obtained by subtracting the luminance value of the comparison target pixel from the luminance value of the inspection target pixel. Find the minimum brightness difference of
When detecting a dark defect in which the luminance of the defective portion is lower than the surrounding luminance, the luminance difference data is obtained by subtracting the luminance value of the pixel to be inspected from the luminance value of the comparison target pixel. A defect detection method characterized in that a minimum luminance difference is obtained. In the defect detection method in any one of Claims 1-7,
The defect detection step extracts a defect candidate pixel by comparing a defect emphasis value at the inspection target pixel with a predetermined threshold, and determines a defect content from a feature amount of a defect candidate area constituted by the defect candidate pixel. A defect detection method characterized by the above. Defect enhancement processing means for performing defect enhancement processing on a captured image obtained by imaging the inspection object;
Defect detection means for detecting a defect based on the defect enhancement value of each pixel obtained by the defect enhancement processing means,
The defect enhancement processing means includes a defect mask processing means for performing mask processing on a defect component region of one of a light defect and a dark defect in the captured image, and an inspection target pixel for sequentially selecting inspection target pixels in the captured image. Selection means,
Comparison target pixel group setting means for arranging a plurality of comparison target pixels separated by a predetermined distance from the center of the selected inspection target pixel around the inspection target pixel and dividing the comparison target pixels into a plurality of comparison target pixel groups When,
Among the comparison target pixels included in the comparison target pixel group, luminance difference data that is a difference between the luminance value of the comparison target pixel that is not masked in the defect mask processing step and the luminance value of the inspection target pixel is obtained. A minimum luminance difference calculating means for determining, for each comparison target pixel group, a minimum luminance difference having a minimum value among the luminance difference data;
A defect enhancement value calculation means for setting a minimum luminance difference having a maximum value among the minimum luminance differences calculated for each comparison target pixel group as a defect enhancement value of the inspection target pixel;
A defect detection apparatus comprising:

Images