JP2001021502A - Flaw inspecting method and system therefor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a flaw inspecting method that employs a masking processing capable of reducing, as far as possible, a dead zone that causes overlooking of flaws, while preventing a positioning error and excessive detection due to product diversity. SOLUTION: A mask image is produced on the basis of each inspection target object itself during actual flaw inspecting process. That is, an inspection target image A is binarized at S2, and is reduced by a predetermined amount at S3, so that the reduced binary image A is used as the mask image for the inspection target image A. Accordingly, it is possible to eliminate a dead zone that causes overlooking of flaws, and to determine an inspection region on the basis of the inspection target image itself that is the actual flaw inspection target so as to cause no problems of positioning error and excessive detection due to product diversity.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a defect inspection method and apparatus for inspecting, for example, defects such as wiring patterns and bonding pads on a printed circuit board. The present invention relates to a defect inspection method and an apparatus for inspecting a defect of the inspection object only for an inspection region obtained by masking.

[0002]

2. Description of the Related Art As a method for detecting defects such as scratches, foreign matter, dents, and stains formed on the surface of a printed circuit board or the like, conventionally, a difference process between a picked-up image of an inspection object and a non-defective image is performed. Density difference between the two over the entire image (absolute density difference)
Is widely used, and a portion where the density difference exceeds a predetermined threshold value is determined to be a defect. In addition, the present applicant divides a captured image of an inspection object into local regions, and determines that a pixel is a defective pixel when density data in a pixel unit is far from the average density in the local region to which the pixel belongs. A defect inspection method using relative defect determination has been developed, and a patent application has already been completed (Japanese Patent Application No. 11-065297). According to the present invention, it is possible to solve the problem of the erroneous determination due to the variation in the density for each production lot in the above-described conventional determination method using good-quality images.

In the defect detection using the captured image as described above, masking processing is performed to narrow the inspection area to a specific area for the purpose of shortening the inspection time and eliminating a positioning error. ing. This masking process is performed by a logical AND operation of a captured image of the inspection object and a mask image obtained in advance. An example of a procedure for generating the mask image will be briefly described. First, a reference image B is captured using a sample of the same type as the inspection object (see FIG. 7). An alignment mark is registered on the image B (see FIG. 7). Using a plurality of samples of the same type, a plurality of images similar to the above are captured (A _{1 to An} ), and the position is corrected based on the alignment marks registered in. The images A _{1 to An} are binarized at a relatively low density level (for example, a density value between the density of the base material portion and the density of the pad top portion), and a logical product thereof is obtained. An image AT (A) is obtained. The image AT (A) is subjected to shrinkage processing, partial deletion, and the like in consideration of the positioning error of the transport system, product variations, the amount of deviation of the resist boundary, and the obtained image is used as the mask image M ( See FIG. 8).

The defect to be detected in this defect detection is, for example, a local abnormality or a large chip on the surface of a bonding pad or the like. Exists, it may be erroneously detected as a defect. Therefore, by performing the above-described processing, even if a positioning error or the like occurs in the inspection target, the resist boundary portion or the like does not enter the inspection area determined by the mask image M until the registration boundary or the like does not enter. The inspection area is narrowed.

[0005]

In the above-mentioned conventional masking method, each inspection object is imaged by using a common mask image (hereinafter, referred to as a fixed mask image) generated in advance. The image is masked. For this reason, the fixed mask image is required to be applicable to all inspection objects having positioning errors and product variations. As described above, the fixed mask image is an area of the greatest common divisor of the inspection area of all inspection objects. It needs to be.
For this reason, when performing the masking process using the fixed mask image, a dead zone (a region where a defect cannot be detected) always occurs in the inspection region of each inspection object. As a result, a relatively small defect existing in a portion close to the outer periphery is hidden by the dead zone, and may not be detected. In general, there is a large error in resist coating at the resist boundary (normally about ± 20 μm to 50 μm), and defects existing in this part are excluded from the inspection target. In the defect detection using the above-described conventional masking method, The likelihood of missing a defect is very high. The present invention has been made in view of the above circumstances, and has as its object to prevent over-detection due to positioning errors and product variations,
An object of the present invention is to provide a defect detection method using masking processing capable of minimizing a dead zone that causes a defect to be overlooked.

[0006]

In order to achieve the above object, the present invention performs an AND operation of a picked-up image of an inspection object and a predetermined mask image defining an inspection area of the inspection object. A defect inspection method for inspecting a defect of the inspection object based on the obtained inspection image; a binarization step of binarizing a captured image of the inspection object based on a predetermined density threshold; A mask image generating step of generating the mask image based on the binarized image obtained in the binarizing step or a contracted binarized image obtained by performing a predetermined contraction process on the binarized image. It is configured as a defect inspection method characterized by the following. According to the present defect inspection method, the inspection area is determined based on the inspection image itself to be actually subjected to the defect inspection, so that it is possible to eliminate a dead zone that may cause a defect to be missed, There is no problem of over-detection due to product variation. In addition, a fixed mask image generating step of generating binary images in advance from captured images of a plurality of samples of the same type as the inspection object and generating a fixed mask image by performing a logical product operation of the binary images is provided. The mask image generating step further includes the binarized image or the contracted image
If the image obtained by the logical sum of the binarized image and the fixed mask image obtained in the fixed mask image generation step is configured to be the mask image, not only the defect existing inside the pad or the like but also the pattern Defects can be accurately detected. In addition, the inspection area obtained from the generated mask image does not include a portion having a large density gradient between the pad and the base portion, and is substantially the same as a top portion such as a pad so as to be as wide as possible. Is desirable. Therefore, if the density threshold value used in the binarization step can be made to substantially coincide with, for example, the density of the outer periphery of the top portion of the pad, the contraction processing is unnecessary. However,
It is difficult to set such a threshold value for all inspection objects because the density of captured images varies from product to product. It is realistic to set a value between the density of the pad portion and the density of the pad portion, and to apply a predetermined amount of contraction processing to the binary image to generate the contracted binary image. And easy. Here, the contraction process may be performed, for example, so that a predetermined width or area of the predetermined portion of the pad portion is substantially equal to a corresponding width or area on the binary image. .

An apparatus capable of performing the above-described defect inspection method performs an AND operation on a captured image of an inspection object and a predetermined mask image defining an inspection area of the inspection object, and obtains an inspection result. A defect inspection apparatus for inspecting a defect of the inspection object based on an image; a binarization unit for binarizing a captured image of the inspection object based on a predetermined density threshold;
Mask image generating means for generating the mask image based on the binarized image obtained by the binarizing means, or a contracted binary image obtained by subjecting the binarized image to a predetermined contraction process; And a defect inspection apparatus characterized by comprising: Further, a fixed mask image generating means for generating binary images in advance from captured images of a plurality of samples of the same type as the inspection object and generating a fixed mask image by performing a logical product operation of the binary images is provided. And the mask image generating means converts an image obtained by the logical sum of the binary image or the contracted binary image and the fixed mask image obtained by the fixed mask image generating means into the mask image This makes it possible to accurately detect not only a defect existing inside a pad or the like but also a pattern defect.

[0008]

Embodiments and examples of the present invention will be described below with reference to the accompanying drawings to provide an understanding of the present invention. The following embodiments and examples are mere examples embodying the present invention, and do not limit the technical scope of the present invention. FIG. 1 is a flowchart showing an example of a processing procedure of a mask image generating method according to an embodiment of the present invention, and FIG. 2 is a flowchart showing an example of a processing procedure of a mask image generating method according to an embodiment of the present invention. FIGS. 3 and 4 are block diagrams showing a schematic configuration of an apparatus A1 capable of performing the above-described mask image generation method. FIG. 4 shows the relationship between a bonding pad on an inspection object and an inspection area set by a mask image according to the prior art. (A), a diagram showing each of the embodiments (b and c), and an example (d), FIG. 5 is an explanatory diagram of the binarization process and the contraction process, and FIG. And the density distribution in one section thereof (right figure), the middle part shows the inspection target screen and the conventional fixed mask image superimposed (left figure), the density distribution in one section thereof (right figure), and the lower part shows the above inspection target Screen and obtained by the method according to the present embodiment It illustrates that overlapped the disk image (left) and concentration distribution in the one cross section (right). The mask image generation device A1 according to the present embodiment is an example of embodying a characteristic portion of the defect inspection device according to the present invention, and has a schematic configuration as shown in FIG. An inspection object 0 such as a printed circuit board is set on a stage 1 and is
The camera 3 captures the grayscale image. CC above
The image captured by the D camera 3 is stored in the image memory 5 through the image detection processing unit 4 in the form of a grayscale image. The arithmetic unit 6 (an example of a binarizing unit, a mask image generating unit, and a fixed mask image generating unit) generates a mask image by using the image stored in the image memory 5 according to a procedure described later.

Next, an example of a procedure of a mask image generation process performed using the mask image generation apparatus A1 will be described with reference to FIG. In the conventional defect inspection method, a fixed mask image is generated by using a plurality of samples of the same type as the inspection object before starting the defect detection processing of the inspection object, and each inspection in the actual defect inspection processing is performed. The common fixed mask image generated in advance is used for masking the object. However, in the present embodiment, a mask image is generated for each inspection object in an actual defect inspection process. When the defect inspection process starts,
First, the first inspection object 0 is set on the stage 1,
The image is captured by the CCD camera 3. Inspection target image A obtained from the CCD camera 3 through the image detection processing unit 4
Is stored in the image memory 5 (step S1). Subsequently, the arithmetic unit 6 reads the inspection target image A from the image memory 5 and performs a binarization process using a predetermined density threshold to generate a binarized image F (Alow). . Here, as shown in FIG. 5, the concentration threshold is set, for example, to the concentration between the top portion of the bonding pad and the base material (step S2; an example of a binarization process). Further, in the arithmetic unit 6, the binary image F
(Alow) is subjected to a contraction process so that the inspection region (mask region) is substantially the same as the top portion of the bonding pad (step S3; an example of a mask image generation process) (see FIG. 5). Prior to the contraction process, a filter process (such as a median filter process for removing isolated point noise) may be performed. The contracted binary image EF (Alow) obtained by the contraction processing is a mask image of the inspection target image A. By the above contraction processing,
A portion having a large concentration gradient between the top portion of the bonding pad and the base portion is removed from the inspection area. In the shrinking process, for example, the width or area of the top portion of the bonding pad may be set in advance, and shrinking may be performed until the width or area of the inspection region matches the set value. The inspection target image A stored in the image memory 5 is subjected to a masking process by a logical AND operation with the contracted binary image EF (Alow), which is the mask image obtained in step S3 (step S4). Then, a defect detection process in the next step is performed based on the obtained inspection image.

The inspection region of the mask image obtained in step S3 is a region equal to the top portion of the bonding pad in the current inspection target image, as shown by the hatched portion in FIG. In the mask image generation processing according to the present embodiment, since the inspection area is determined based on the inspection target image itself that is actually the target of the defect inspection, there is no influence of a resist coating deviation or the like for each inspection target. There is no dead zone near the boundary of the bonding pad. In a conventional fixed mask image, a plurality of samples of the same type as the inspection object are used, and the area of the greatest common denominator is used as the inspection area. Therefore, as shown in FIG. Is out of the inspection area, and that part becomes a dead zone. FIG. 6 compares the results of masking performed by the method according to the present embodiment with the conventional method using a fixed mask. The upper part of FIG. 6 shows the image to be inspected (left figure) and the density distribution in one section thereof (right figure)
The middle part is obtained by overlaying the inspection target screen and the conventional fixed mask image (left figure) and the density distribution in one cross section (right figure), and the lower part is obtained by the inspection target screen and the method according to the present embodiment. (Left figure) and a density distribution (right figure) in one cross section of the mask image. In the density distribution diagram (inspection target image) in the upper part of FIG. 6, the width of the top portion of the bonding pad of the inspection target is about 14.8 pixels, but the inspection target screen and the conventional fixed mask image are overlapped. In the density distribution diagram in the middle stage, the mask width is about 12.6 pixels, and it can be seen that the mask width is considerably smaller than the top portion of the actual bonding pad (that is, a dead zone is generated). On the other hand, in the density distribution diagram in the lower part in which the inspection target screen and the mask image obtained by the method according to the present embodiment are overlapped, the mask width is 14.8 pixels, which is the same as the actual top part, and In the method according to the embodiment, it can be seen that the entire surface of the top portion of the bonding pad becomes the inspection region and no dead zone is generated. As described above, the use of the mask image generation method according to the present embodiment makes it possible to eliminate a dead zone that may cause a defect to be overlooked, and furthermore, based on the inspection target image itself that is actually the target of the defect inspection. Since the inspection area is determined by the inspection, there is no problem of over-detection due to positioning errors and product variations. Note that the contraction processing in step S3 is not essential. For example, in step S2, the density threshold used for binarization is set to t
If the density can be made to substantially match the density on the outer periphery of the op portion, the above-described shrinking process is unnecessary. However, it is difficult to set such a threshold value for all inspection objects because the density of captured images varies from product to product. Is realistic.

[0011]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS By using the mask image generating method according to the above-described embodiment, it is possible to accurately detect a defect existing inside a pad, even a small one existing in an outer peripheral portion thereof. However, the method according to the above-described embodiment cannot detect a defect in which a pad is largely lost. This is because the mask image is generated based on the inspection target image itself that is actually the target of the defect inspection, and therefore, if there is a defective portion on the pad, the inspection area is set to avoid the defective portion. (See FIG. 4C). FIG. 2 shows a further improvement of the method of generating a mask image according to the embodiment shown in FIG. 1 so that a pattern defect can be detected. Steps S1 to S3 are exactly the same as those in FIG. In the above embodiment, the contracted binary image EF (Alow) obtained in step S3 is used as a mask image as it is. In the present embodiment, the fixed mask image M used in the conventional defect detection and the contracted An image obtained by a logical sum with the binarized image EF (Alow) is set as a mask image (step S3 '). still,
The fixed mask image M is generated in advance by the method described in the related art before starting the defect inspection processing (step S0; an example of a fixed mask image generation step). The inspection area of the mask image generated by such a method is shown in FIG.
4 (d) (the inspection area A in FIG.
(Sum of the inspection area B in (c)), it is possible to accurately detect a pattern defect while eliminating a dead zone near the resist-coated portion.

[0012]

As described above, the present invention performs an AND operation on a captured image of an inspection object and a predetermined mask image defining an inspection area of the inspection object, and obtains an inspection image obtained. In the defect inspection method for inspecting the defect of the inspection object based on the threshold value, a binarization step of binarizing a captured image of the inspection object based on a predetermined density threshold, and a binarization step are provided. And a mask image generating step of generating the mask image based on the binarized image obtained or a contracted binarized image obtained by performing a predetermined contraction process on the binarized image. Since the defect inspection method is configured as described above, it is possible to eliminate a dead zone that causes a defect to be overlooked, and there is no problem of over-detection due to positioning errors or product variations. In addition, a fixed mask image generating step of generating binary images in advance from captured images of a plurality of samples of the same type as the inspection object and generating a fixed mask image by performing a logical product operation of the binary images is provided. In the mask image generating step, the image obtained by the logical sum of the binarized image or the contracted binary image and the fixed mask image obtained in the fixed mask image generating step is used as the mask image. With such a configuration, not only a defect existing inside a pad or the like but also a pattern defect can be accurately detected.

[Brief description of the drawings]

FIG. 1 is a flowchart showing an example of a processing procedure of a mask image generation method according to an embodiment of the present invention.

FIG. 2 is a flowchart illustrating an example of a processing procedure of a mask image generation method according to the embodiment of the present invention.

FIG. 3 is an apparatus A that can execute the mask image generating method.
1 is a block diagram showing a schematic configuration of FIG.

FIG. 4 is a diagram showing a relationship between a bonding pad on an inspection object and an inspection area set by a mask image for each of the related art (a), the embodiments (b and c), and the example (d). .

FIG. 5 is an explanatory diagram of a binarization process and a contraction process.

6 is an image to be inspected (left figure) and a density distribution in one section thereof (right figure), and an upper part is an image in which the inspection object screen is superimposed on the conventional fixed mask image (left figure) and one section thereof. , The lower part shows the overlay of the inspection target screen and the mask image obtained by the method according to the present embodiment (left figure), and the density distribution in one section thereof (right figure). Figure.

FIG. 7 is a diagram schematically showing an example of a captured image of an inspection object.

FIG. 8 is an explanatory diagram of a mask image generation method according to the related art.

[Explanation of symbols]

DESCRIPTION OF SYMBOLS 1 ... Moving stage 2 ... Lighting 3 ... CCD camera 4 ... Image detection processing part 5 ... Image memory 6 ... Operation part (an example of a binarization part, a mask image generation part, a fixed mask image generation part)

 ──────────────────────────────────────────────────続 き Continued on the front page F term (reference) 2G051 AA65 AB02 CA03 CA04 EA11 EA23 EA30 EB01 EB09 ED01 ED15 5B057 AA03 BA02 CA08 CA12 CA16 CC03 DA03 DB02 DB09 5L096 AA06 BA03 CA02 DA01 EA02 EA03 EA13 GA23 FA37 GA51

Claims (10)

[Claims] An AND operation of a captured image of an inspection object and a predetermined mask image defining an inspection area of the inspection object is performed, and a defect of the inspection object is determined based on the obtained inspection image. In the defect inspection method for inspecting, a binarizing step of binarizing a captured image of the inspection object based on a predetermined density threshold, a binarized image obtained in the binarizing step, or the binary image A mask image generating step of generating the mask image based on a contracted binary image obtained by performing a predetermined contraction process on the transformed image. 2. A fixed mask image in which a binary image is generated in advance from captured images of a plurality of samples of the same type as the inspection object, and a fixed mask image is generated by a logical product operation of the binary images. A mask image generating step, wherein in the mask image generating step, an image obtained by a logical sum of the binary image or the contracted binary image and the fixed mask image obtained in the fixed mask image generating step is masked. The defect inspection method according to claim 1, wherein the defect inspection method is an image. 3. The method according to claim 2, wherein the inspection object has a pad portion protruding from the surface of the base material portion, and the predetermined density threshold is:
A predetermined amount of shrinking processing is performed on the binarized image obtained by using the density threshold value in the binarizing step and set to a value between the density of the base portion of the inspection object and the density of the pad portion. The defect inspection method according to claim 1 or 2, wherein the shrinking binarized image is generated by performing the following. 4. The defect according to claim 3, wherein the contraction processing is performed so that a predetermined width of the predetermined portion of the pad portion and a corresponding width on the binarized image are substantially the same. Inspection methods. 5. The defect inspection according to claim 3, wherein the contraction processing is performed so that the preset area value of the pad portion is substantially the same as the corresponding area value on the binarized image. Method. 6. An AND operation of a picked-up image of the inspection object and a predetermined mask image defining an inspection area of the inspection object, and determining a defect of the inspection object based on the obtained inspection image. In a defect inspection apparatus for inspecting, a binarizing means for binarizing a captured image of the inspection object based on a predetermined density threshold, a binarized image obtained by the binarizing means, or the binary image A defect inspection apparatus comprising: a mask image generating unit configured to generate the mask image based on a contracted binary image obtained by performing a predetermined contraction process on the transformed image. 7. A fixed mask image which previously generates a binary image from captured images of a plurality of samples of the same type as the inspection object, and generates a fixed mask image by performing a logical product operation of the binary images. Generating means for generating an image obtained by the logical sum of the binary image or the contracted binary image and the fixed mask image obtained by the fixed mask image generating means. 7. The defect inspection apparatus according to claim 6, wherein the defect inspection apparatus is an image. 8. The inspection object has a pad portion raised from the surface of the base material portion, and the predetermined density threshold is:
A predetermined amount of shrinkage processing is performed on the binarized image obtained by using the density threshold by the binarization means and set to a value between the density of the base portion of the inspection object and the density of the pad portion. The defect inspection apparatus according to claim 6, wherein the defect inspection apparatus generates the contracted binarized image by performing the following. 9. The defect according to claim 8, wherein the contraction processing is performed such that a predetermined width of the predetermined portion of the pad portion and a corresponding width on the binarized image are substantially the same. Inspection equipment. 10. The defect inspection according to claim 8, wherein the contraction processing is performed so that the preset area value of the pad portion is substantially the same as the corresponding area value on the binarized image. apparatus.