JP2014157053A - Pattern inspection method and pattern inspection device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a circuit pattern inspection method for automatically inspecting a defect of a printed board circuit pattern, LSI circuit pattern, etc. by image processing.SOLUTION: A pattern inspection method for inspecting a defect of an inspection object by comparing an inspection object pattern of the inspection object with a master pattern on the basis of image data includes: a first process for performing distance conversion processing on a reference pattern including a background pattern and a product pattern to acquire an outline distance pattern; a second process for performing a skeleton-structuring processing on the outline distance pattern to acquire a reference skelton pattern; a third process for acquiring a skeleton distance pattern from the reference skelton pattern; a forth process for acquiring a master pattern from the outline distance pattern and the skeleton distance pattern; and a fifth process for comparing the inspection object pattern with the master pattern to detect a difference therebetween.

Description

  The present invention relates to a circuit pattern pattern inspection method and pattern inspection apparatus for automatically inspecting defects such as printed circuit board circuit patterns and LSI circuit patterns by image processing.

  A demand for a circuit pattern in which a conductor pattern is formed on an insulating member is rapidly increasing with the spread of electronic devices such as mobile phones. Since the circuit pattern affects the performance and durability of an electronic device using the circuit pattern, the circuit pattern is not only electrically connected as designed, but also requires a predetermined dimensional accuracy.

  Therefore, all the circuit patterns are inspected after being manufactured, and it is confirmed that a predetermined specification is satisfied. By the way, since the types and number of circuit patterns have become enormous, it is necessary to complete the inspection in as short a time as possible.

  Conventionally, in this inspection, an image comparison method is often used as a defect detection method. This method is a method for obtaining a difference image between binarized imaging data and non-defective product data and obtaining a difference portion of a certain area or more as a defect. Since this method binarizes data, the calculation speed can be increased.

  On the other hand, as a defect determination method, a conventionally proposed method is called a 1/3 rule. This rule determines that a lead has a protrusion if the thickness of the lead portion, which is a conductor of the circuit pattern, is larger than the length of 1/3 of the reference thickness, and from the length of 1/3 If it is thin, it is determined that there is a risk of disconnection.

  In particular, Patent Document 1 discloses a method using an image comparison method, in which an enlarged image in which a reference pattern portion is 4/3 times and a reduced image in which the size is 2/3 times. Are prepared, and each reference image is compared with an image of a circuit pattern to be inspected.

  Patent Document 2 discloses a method for creating an enlarged / reduced reference image corresponding to a line width.

JP 63-19541 A JP 2010-145145 A

  An image comparison method composed of a reference image and an inspection object is useful because it can easily identify a defect in a circuit pattern. The accuracy of defect extraction by this method depends on the result of creating a reference image enlarged or reduced with respect to the reference pattern.

  Patent Document 1 discloses a method for creating an enlarged or reduced pattern of a reference pattern by taking a logical sum with adjacent pixels as an enlargement / reduction method. Since this method increases or decreases the same number of pixels with respect to all pixels, a reference image can be created without any problem when the reference pattern is composed of patterns having substantially the same width. However, when patterns having greatly different line widths are mixed, according to the knowledge of the present inventors, the following problem occurs.

  That is, when the image is reduced to fit the thick line width pattern, the thin line pattern disappears, and when the image is enlarged to fit the thin line width pattern, the thick line pattern cannot be enlarged by a predetermined amount. In other words, there is a problem that it becomes difficult to create an enlarged / reduced reference image corresponding to the line width.

  In addition, the method described in Patent Document 2 is to multiply the distance value by a coefficient by a predetermined coefficient when reproducing the original product pattern by inverse distance conversion from the distance information added to the skeleton pattern of the reference image. This is a method for creating an arbitrary enlargement / reduction pattern according to the line width.

  According to the knowledge of the present inventors, this method is based on the premise that the skeleton pattern has reproducibility of the original product pattern, and the reconstruction of the product pattern by reverse distance transformation is a square with no inclination. It is limited to the shape synthesized by a combination of patterns.

  Therefore, in the case of a shape that includes many square pattern elements such as rhombus / circular / parallelogram, the skeleton pattern that cannot be pruned to maintain the reproducibility of the pattern is near the boundary between the product pattern and the background pattern (corner Part).

  For this reason, there is a problem that the sensitivity is hardly changed even when the reference image is enlarged / reduced in the corner portion of the pattern, resulting in an overdetection state.

  For example, in the case of a pattern as shown in FIG. 19A in which the reference image is composed of triangles, the apex (corner) portion in both the reduced image shown in FIG. 19B and the expanded image shown in FIG. Since there is always a pattern, the sensitivity is always constant at the apex (corner) portion. For this reason, when inspecting a chipped pattern using a reduced image, even if there is a chipping in the apex (corner) portion that is actually acceptable, it is a drawback. The inspection sensitivity becomes excessively sensitive at the apex portion, such as a defect even if there is a protrusion in the apex (corner) portion that is actually acceptable.

  An object of the pattern inspection method and pattern inspection apparatus of the present invention is to solve the above problems.

That is, the present invention
A pattern inspection method for detecting a defect of the inspection object by comparing a master pattern and an inspection object pattern of the inspection object based on image data,
A first step of obtaining a contour distance pattern by performing a contour distance calculation process on a reference pattern having a background pattern and a product pattern;
A second step of skeletonizing the contour distance pattern to obtain a reference skeleton pattern;
A third step of obtaining a skeleton distance pattern from the reference skeleton pattern;
A fourth step of obtaining a master pattern from the contour distance pattern and the skeleton distance pattern;
This is a pattern inspection method having a fifth step of detecting a difference by comparing the master pattern and the inspection target pattern (hereinafter referred to as inspection method (1)).

In the inspection method (1),
The first step is a process of assigning a distance value from a boundary between the background pattern and the product pattern to pixels belonging to the reference pattern. (Hereafter referred to as inspection method (2))
In the inspection method (1) or the inspection method (2),
The second step sequentially deletes the pixels from the pixels belonging to the reference pattern until there are no surrounding pixels whose distance from the boundary of the pixels around the pixel is larger than the distance from the boundary of the pixel. Belongs to the product pattern, a pixel deletion process, a thinning process for deleting the pixels until the line width of the product pattern is minimized, a synthesis process for superimposing the pixel deletion processing image and the thinning process image, and It has a pruning process for deleting branches from the pixels remaining as a result of the synthesis process in the pixels (hereinafter referred to as inspection method (3)).

In the inspection method (1) to the inspection method (3),
The third step is a process of assigning a distance from the reference skeleton pattern to pixels belonging to the reference pattern (hereinafter referred to as inspection method (4)).

In the inspection method (1) to the inspection method (4),
In the fourth step, for each pixel belonging to the reference pattern, the distance from the pixel to the contour and the distance from the pixel to the skeleton are calculated to calculate the distance from the skeleton to the contour. A process of obtaining a multi-master pattern by setting a value obtained by dividing the distance by the distance from the skeleton to the contour as a relative distance value in the pixel is a pixel value in the pixel (hereinafter referred to as inspection method (5)).

In the inspection method (1) to the inspection method (5),
In the fourth step, a pixel value corresponding to the relative distance value is set as a pixel value of a pixel belonging to the master pattern (hereinafter referred to as inspection method (6)).

In the inspection method (6),
The difference detected in the fifth step is further compared with the multi-master pattern (hereinafter referred to as inspection method (7)).

The pattern inspection apparatus of the present invention is
A master pattern creation unit that receives a reference pattern having a background pattern and a product pattern and outputs a master pattern;
A storage unit for recording the master pattern;
An imaging unit for imaging the inspection object and obtaining image data;
A binarization unit that binarizes the image data to obtain an inspection target pattern;
An arithmetic unit that compares the master pattern and the inspection target pattern and outputs a difference;
A determination unit that outputs a determination result based on the output of the arithmetic unit;
The master pattern production unit
Providing a distance from the boundary between the background pattern and the product pattern to the pixels belonging to the product pattern to create a contour distance pattern,
Creating a reference skeleton pattern by skeletonizing the reference pattern;
Providing a distance from the reference skeleton pattern to the pixels belonging to the reference pattern to create a skeleton distance pattern,
The distance from the pixel to the contour and the distance from the pixel to the skeleton are calculated to calculate the distance from the skeleton to the contour, and a value obtained by dividing the distance from the pixel to the skeleton by the distance from the skeleton to the contour A multi master pattern as a master value is prepared, and a master pattern is prepared from the multi master pattern.

  In the pattern inspection method according to the present invention, a pixel located between a distance obtained by multiplying the distance between the skeleton portion and the contour portion of the reference pattern by a predetermined coefficient is used as a master pattern. Even if it is the pattern which is carrying out, the master pattern which expanded and contracted each part by the same magnification can be obtained. Therefore, it is possible to perform a defect inspection of a product in which combinations of patterns having more various line widths are drawn on one substrate.

It is a schematic diagram which shows the outline | summary of the pattern inspection apparatus of this invention. It is a flowchart of a master pattern preparation part. It is a figure explaining the product pattern pd and the background pattern bd in one image data in the 1st process which produces a contour distance pattern. It is a figure explaining the result of giving the value of distance (d) to product pattern pd in the 1st process which creates a contour distance pattern. It is a figure explaining the state which added distance (d) from a negative boundary with respect to all the pixels which belong to a background pattern in the 1st process which produces a contour distance pattern. It is a figure which shows the flow of an outline distance calculation process. It is a figure explaining the reference area | region used by the flow of an outline distance calculation process. It is a figure explaining the contour distance pattern input in a 2nd process. It is a figure explaining the result of pixel deletion processing in the 2nd process. It is a figure explaining the result of thinning processing in the 2nd process. It is the figure which showed the skeleton information of the pixel deletion process result in a 2nd process. It is the figure which showed the skeleton information of the thinning process result in a 2nd process. It is the figure which showed the result of the synthetic | combination process in a 2nd process. It is a figure which shows the reference | standard frame | skeleton pattern which is the output in a 2nd process. It is a figure explaining the flow of an image deletion process among skeletonization processes. It is a figure which shows the thinning element used for a thinning process. It is a figure which shows the flow of a thinning process. It is a figure which shows the flow of the replacement process C of a gradation value among the flows of a thinning process. It is a figure which shows the flow of a pruning process. It is a figure explaining the standard skeleton pattern which is the input in the 3rd process. It is a figure explaining the frame | skeleton distance pattern which is an output in a 3rd process. It is a figure which shows the flow of a skeleton distance calculation process. It is a figure explaining the reference area used in the flow of skeleton distance calculation processing. It is a figure which shows the flow for producing | generating a master pattern from a multi master pattern. It is a figure explaining the structure of a multi master pattern. It is a figure which shows the flow of the 5th process which detects the difference of a master pattern and a test object pattern. It is a figure explaining the example of the test | inspection using a shrinkage | contraction master pattern. It is a figure explaining the example of the test | inspection using an expansion | swelling master pattern. It is a figure which shows the example of the process which test | inspects a chip | tip and a processus | protrusion using a multi master pattern. It is a figure which shows the reference | standard image in the example which produced the reduced image and the expansion | swelling image from the reference | standard image by the conventional method. It is a figure which shows the reduced image in the example which produced the reduced image and the expansion image from the reference | standard image by the conventional method. It is a figure which shows the expansion | swelling image in the example which produced the reduction image and the expansion | swelling image from the reference | standard image by the conventional method. It is a figure which shows the reduced image in the example which produced the reduced image and the expansion image from the reference | standard image by the method by this invention. It is a figure which shows the expansion | swelling image in the example which produced the reduction image and the expansion | swelling image from the reference | standard image by the method by this invention.

  Embodiments of the present invention will be described below, but the present invention is not limited to these embodiments, and can be modified and changed within the scope of the gist of the present invention.

  FIG. 1 is a schematic diagram showing an outline of the pattern inspection apparatus 1 of the present invention. The pattern inspection apparatus of the present invention includes an image capturing unit 20 that captures a circuit pattern of an inspection object 90 as a fifth step, and a binarization unit 30 that binarizes the captured image to obtain an inspection target pattern. , An alignment unit 50 for aligning the position of the inspection target pattern with the reference image (referred to as “master pattern MP”), an arithmetic unit 60 for performing a logical operation on the pixels of the aligned images, and determining the calculation result The determination part 70 to include is included. Moreover, the memory | storage part 80 which memorize | stores a master pattern and the master pattern preparation part 10 which produces the master pattern comprised by the 1st to 4th process are also included.

  The image capturing unit 20 includes at least a camera 21 and an illumination 22. The camera 21 converts the captured image of the circuit pattern of the inspection object 90 into image data Vd and outputs it. Therefore, the one using a photoelectric conversion element such as CCD or CMOS is preferable. The CCD or CMOS may be one in which pixels are arranged two-dimensionally, or one-dimensionally arranged like a line sensor. Moreover, in order to image | photograph the test object 90 from upper direction, what mounted the lens by a telecentric optical system is more preferable. In addition to using the wavelength of normal visible light, light in the near infrared or ultraviolet wavelength band may be detected.

  The image data Vd is a set of pixel information converted into an electrical signal by the photoelectric conversion element. Specifically, a set of values (gradation values) obtained by converting light received by one CCD or CMOS pixel into 8-bit or 10-bit gradation and pixel positions (coordinates, etc.) is collected for one image. Is. As an example, when the number of effective pixels of a CCD or CMOS is 5 million pixels and converted to a 10-bit gradation value per pixel, the image data Vd for one image is 50 million only with the gradation value. It becomes a bit (50 Mbit).

  The illumination 22 is for illuminating the image of the inspection object 90 to obtain contrast. There is no particular limitation, and ring-shaped fluorescent lamps, LEDs, optical fiber light guides, and the like can be suitably used.

  In addition to illuminating the inspection object 90 from above, the transmitted light may be imaged by the camera 21 illuminating from below the inspection object 90.

  In addition to irradiating light with a wavelength of normal visible light, light in the near infrared or ultraviolet wavelength band may be irradiated.

  After the binarization unit 30, image processing is performed on the image data Vd. Each component can also create dedicated hardware. However, considering that it is easy to change and the current computer processing speed is sufficiently fast, software processing by the computer is the main.

  Therefore, the binarization unit 30, the alignment unit 50, the calculation unit 60, the determination unit 70, and the master pattern creation unit 10 will be described as elements of the control device 85 in order to represent that they are executed by a computer and software. These elements can be executed by software and computers.

  The storage unit 80 is illustrated as being included in the control device 85. Specifically, a semiconductor memory is preferably used. However, a secondary storage medium such as a hard disk installed outside the control device 85 may be included.

  Here, the pattern will be described. In addition to the resin sheet of polyethylene terephthalate (PET), the pattern is a circuit or one of the circuits on a resin sheet substrate such as polyethylene naphthalate (PEN), polycarbonate, polymethyl methacrylate (PMMA), cyclic polyolefin (COP), and transparent polyimide. This is image data obtained from a product in which a portion is formed. The product includes a portion where a circuit pattern is formed and a portion where a circuit pattern is not formed, such as a conductor. Image data of a portion where the circuit pattern is formed is referred to as a product pattern pd, and image data where the circuit pattern is not formed is referred to as a background pattern bd. .

  The image data Vd obtained by photographing the inspection object by the image photographing unit 20 in FIG. 1 is a pattern, and the pattern includes a product pattern pd and a background pattern bd. The pattern has pixels, and each pixel has a gradation value and coordinates. The pixel of the product pattern of the inspection target pattern is Epd, and the pixel of the background pattern is Ebd.

  On the other hand, the reference pattern Vsd is image data obtained from products that have passed various tests and CAD data at the time of design (collectively referred to as “reference products”).

  The reference pattern Vsd further has information such as coordinates (x, y), pattern discrimination information (Ev), pixel value (P), distance from the boundary (d), and skeleton information (B) for each pixel. doing. Moreover, you may have master pattern information (mp) showing whether it becomes the pixel which comprises a master pattern.

  The coordinates (x, y) are information representing the position of the pixel in the reference pattern. The pixel value (P) is a gradation value indicating the luminance of the pixel. The pattern distinction information (Ev) is information indicating whether the pixel is a product pattern pixel or a background pattern pixel. The boundary between the product pattern and the background pattern is also simply referred to as “boundary”.

  The pixel value (P) is a gradation value. The distance (d) from the boundary is a pixel belonging to the product pattern pd of the reference pattern, and indicates the distance from the nearest background pattern bd by the number of pixels. The skeleton information (B) is information that indicates whether or not the pixel belongs to the product pattern pd and has become a skeleton as a result of the skeletonization process described later. The master pattern information (mp) is information indicating whether or not a pixel of the master pattern is obtained as a result of a master pattern output process described later.

  It is sufficient that the reference pattern initially has coordinates, pattern distinction information, and pixel values. This is because the other parameters are produced by the master pattern production unit.

  These may be displayed side by side, and specifically (x, y, Ev, P, d, B, mp) may be described for a certain pixel.

  In FIG. 2, the processing flow of the master pattern preparation part 10 is shown. When the reference pattern Vsd is input, the contour distance calculation process (S201) as the first process, the pixel deletion process (S202) as the second process, the thinning process (S203), the synthesis process (S204), and The reference pattern is processed by the pruning process (S205), the skeleton distance calculation process (S206) as the third process, and the multi-master pattern output (S207) as the fourth process, and as a result, the master pattern is output (S208). ). Pixel deletion processing, thinning processing, composition processing, and pruning processing are collectively referred to as skeletonization processing. The flow in FIG. 2 is shown as a subroutine corresponding to the main routine processing with the processing (S209) for returning to the main routine at the end, but may be an independent processing.

  The contour distance calculation process (S201 in FIG. 2) will be described in more detail with reference to FIG.

  FIG. 3A shows that there is a product pattern pd and a background pattern bd in one image data. In the contour distance calculation process, the distance from the boundary between the background pattern bd and the product pattern pd of the input reference pattern Vsd is a positive value for the product pattern pd, and a negative value for the background pattern bd. It is a process which produces the outline distance pattern provided as distance (d) from.

  First, FIG. 3B shows a result obtained by assigning a chess board distance value for obtaining a distance from the contour in the vicinity of the periphery 8 of the target pixel as a value of the distance (d) to the product pattern pd. First, for all the pixels Epd of the product pattern pd, the adjacent pixels are examined, and “0” is given to the distance (d) from the boundary to the pixels adjacent to the background pattern. Subsequently, “1” is assigned to the distance (d) from the boundary of the pixel adjacent to the pixel whose distance (d) from the boundary is “0” for all the pixels of the product pattern pd. As described above, the distance (d) from the boundary by a positive value is obtained by giving the distance from the background pattern by the number of pixels to the product pattern portion of the reference pattern.

  FIG. 3C shows a state in which the distance (d) from the negative boundary is added to all the pixels belonging to the background pattern to the data shown in FIG. First, with respect to all the pixels Ebd of the background pattern bd, the adjacent pixels are examined, and a pixel adjacent to a pixel whose distance (d) from the boundary is “0” is set to a distance “d” from the boundary by “−”. 1 ”is given. Subsequently, “−2” is assigned to the distance (d) from the boundary of the pixel adjacent to the pixel whose distance (d) is “−1” for all the pixels of the background pattern bd. Thus, in the contour distance pattern, the distance from the boundary is given to all the pixels belonging to the reference pattern Vsd.

  FIG. 4A shows a flow of the contour distance calculation process. When the reference pattern is input, the process starts (S220). An end determination is made (S222), and if it is an end (Y branch of S222), the process is returned to the main (step S202 in FIG. 2) (S234). Here, the end determination is made based on whether or not the distance from the boundary is given to all the pixels of the reference pattern. Since the distance (d) from the boundary is the number of pixels from the boundary between the background pattern and the product pattern, data of the distance “0” is previously assigned to the pixel that contacts the boundary between the background pattern and the product pattern of the product pattern. The processing to be performed is performed in advance.

  Next, it is determined whether or not the following processing has been completed for all pixels belonging to all reference patterns (S224). If the process has already been completed, an argument k representing the distance is added (S226), the process returns to step S222, and an end determination is performed. The argument k is given “1” as an initial value immediately after the start of the contour distance calculation process.

  If the processing for pixels belonging to all product patterns has not been completed yet (N branch in S224), the following processing is performed. First, attention is paid to an area of 9 pixels centered on a certain pixel. FIG. 4B shows the pixel. This is hereinafter referred to as a reference area.

  Note that the reference region is not limited to the neighboring pixels in the eight directions around the center pixel, and the range may be appropriately expanded outward.

  The reference area is also an area that defines a “periphery” for a certain pixel in the pattern. The middle pixel is denoted by A, and the surrounding pixels (eight) are denoted by Ap1 to Ap8. If the distance (d) from the boundary of any of the peripheral pixels Ap is (k−1) * Ev (Y branch of S228), the pixel A is assumed to be a pixel to which the distance is given, and from the boundary K * Ev is given to the distance (d) (S230). Here, the distance (d) from the boundary of the central pixel is represented as A (d), and the distance (d) from the boundary of the surrounding pixels is Ap (d). The pattern discrimination information Ev for determining whether the middle pixel A is a pixel belonging to the product pattern or the background pattern is Ev = 1 if the pixel A is a pixel belonging to the product pattern. If the pixel belongs to the pattern, Ev = −1, and a positive value is assigned to the product pattern and a negative value is assigned to the background pattern.

  For example, when searching for a pixel whose distance (d) from the boundary is “1” (when k = 1), a certain pixel belonging to the product pattern is defined as the center of the reference area (pixel A), and the pixel A If there is a pixel belonging to the background pattern of distance “0” in any of the peripheral pixels, “1” is assigned to the distance (d) from the boundary to the pixel A. On the other hand, if there is no pixel whose distance (d) from the boundary is “0” in the peripheral pixel Ap (N branch of S228), step S230 is skipped and the process proceeds to step S232. The pixel Epd belonging to the next product pattern is moved (S232), and the process returns to step S224. To move to the next pixel means to move the reference area to the next pixel. By repeating the above processing, the distance Ep from the boundary can be given to the pixels Epd belonging to all the product patterns, and the contour distance pattern is obtained.

  The contour distance calculation method calculates the chessboard distance in which the reference region is set to 8 neighborhoods of all peripheral pixels (Ap1, Ap2, Ap3, Ap4, Ap5, Ap6, Ap7, Ap8) in contact with the middle pixel A. The calculation method is not limited to the above method, and may be a calculation method based on a city block distance in which the reference region is limited to four neighborhoods of the upper, lower, left, and right peripheral pixels (Ap2, Ap4, Ap5, Ap7) with respect to the middle pixel A. Further, instead of calculating the distance between pixels by the chessboard distance or the city block distance, it may be calculated by the Euclidean distance in which the distance between the middle pixel A and the surrounding pixels is obtained as a two-dimensional Euclidean space. .

  Next, the skeletonization process will be described. The skeletonization processing is processing for performing pixel deletion processing on the image data obtained as a result of the contour distance calculation processing, synthesizing the result of thinning the reference pattern separately, and pruning it to obtain a reference skeleton pattern.

  This will be described in more detail with reference to FIG. FIG. 5A shows the contour distance pattern of FIG. 3C again. In the pixel deletion process, the contour distance pattern shown in FIG. 5A in which a distance (d) from the boundary is given to the pixel Epd belonging to the product pattern is input. The pixel deletion process includes a process of deleting pixels for pixels belonging to the product pattern until there are no pixels having a distance (d) from the boundary larger than the distance (d) from the boundary that the pixel belongs to. The pixel deletion process is a process that does not actually delete a pixel but extracts a skeleton pixel from the pixel Epd belonging to the product pattern. Therefore, the value “1” is given to the skeleton information (B) in advance for all the pixels Epd, and this skeleton information is set to “0”. The result obtained by the pixel deletion processing is referred to as a pixel deletion processing image.

  FIG. 5B shows the pixel A in the middle of the reference area until the distance (d) from the boundary of the peripheral pixels in the vertical and horizontal diagonal directions is larger than the distance (d) from the boundary of the pixel A. The result of deleting is shown. That is, FIG. 5B shows the distance (d) from the boundary of the pixel Epd belonging to the product pattern whose skeleton information (B) is “1” as an integer value. For example, the pixel 111 is a pixel whose distance (d) from the boundary is “3” and whose skeleton information (B) is “1”. Thus, there may be a case where one connected reference skeleton pattern cannot be obtained in the pixel deletion process.

  Next, in parallel with the pixel deletion process, a thinning process image is created by deleting the surrounding pixels until the line thickness is “1”, that is, the line width of the pixel is one pixel, by the thinning process. The thinning process is performed based on the gradation value. Therefore, the reference pattern Vsd is input to the thinning process. FIG. 5C shows a result of generating an image by thinning processing. The result obtained by the thinning process is called a thinning process image.

  Next, a logical sum operation is performed on the result of the pixel deletion processing image and the thinning processing image. This is a synthesis process.

  By the synthesis process, a skeleton pattern obtained by interpolating a portion where the skeleton line segment is divided in the pixel deletion process by the thinning process can be obtained. That is, the skeleton information (B) of the remaining pixels in the thinned image is set to “1”, and the skeleton information (B) is logically summed with the pixel deletion processed image.

  Here, the detail of the said synthetic | combination process is demonstrated. The composition processing is a logical sum of the skeleton information (B) for the pixel deletion processing result in FIG. 5B and the thinned image in FIG. 5C. That is, for FIG. 5D, which is the skeleton information of FIG. 5B, and FIG. 5E, which is the skeleton information of FIG. 5C, only those whose skeleton information (B) is “1” are extracted. FIG. 5F shows the composite processing result image showing the luminance value (d) of the pixel of the contour distance calculation result image corresponding to the extracted pixel.

  Then, a pruning process is performed on the pattern information obtained as a result of the synthesizing process to delete a portion that becomes a branch part. Here, the branch portion is a portion having a tip and continuous at an angle of 45 ° on the image data. In FIG. 5F, portions 113 to 117 correspond to branches. The branch portion is generated by processing the corner of the pattern as the starting point of the line segment by the thinning process.

  The branch portion may generate an unfavorable portion in the master pattern used for defect inspection by a skeleton distance calculation process to be performed later, and needs to be deleted. Further, the pruning process may be performed on a pixel whose skeleton information (B) is “1”. FIG. 5G shows a reference skeleton pattern obtained as a result of pruning processing and deleting branches. It can be seen that one continuous skeleton pattern was obtained by the pixels 112 as compared to the pixel deletion processing image of FIG.

  Next, each process is illustrated using a flow. FIG. 6 shows a flow of pixel deletion processing. When the contour distance pattern is input, it is determined whether or not to end the pixel deletion process (S252). Here, it is determined whether or not there is a pixel to be deleted. Therefore, it is sufficient to determine the pixels belonging to the product pattern. When there are no more pixels to be deleted, the process proceeds to the next process (S204) (S264). Otherwise, it is determined whether the inspection has been completed for all pixels belonging to the product pattern and having a distance (d) m from the boundary (S254). If completed, the parameter m indicating the distance to be deleted is incremented. m is set to “1” by default.

If the processing is not completed for all the pixels whose distance (d) from the boundary belonging to the product pattern is m (N branch in S254), the distance (d) from the boundary of the middle pixel of the reference area and the peripheral pixels The distances (d) from the borders are compared (S258). With reference to FIG. 4B, the peripheral pixels are adjacent pixels (Ap1, Ap2, Ap3, Ap4, Ap5, Ap6, Ap7, Ap8) that are in the peripheral 8 direction with respect to the middle pixel A. Incidentally, it expressed in FIG. 6, the distance from the i-th boundary of the peripheral pixels as "Ap i ^(d)".

  If any of the distances (d) from the boundary of the peripheral pixels is larger than the distance (d) from the boundary of the middle pixel in the reference region (Y branch in S258), the skeleton information (B) of the pixel A is set to “ 0 ”(S260). The skeleton information (B) of “0” indicates that the pixel A in the middle of the reference area cannot be a skeleton pattern. Then, the process proceeds to the next pixel (S262) and returns to step S254.

  By repeating this process, the skeleton information (B) becomes “0” from the pixels close to the background pattern, and finally the information shown in FIG. This process corresponds to a pixel deletion process.

  Next, the thinning process (S203) will be described. The thinning process is a process of changing the brightness value so that the bright part becomes a dark part until the thickness of the bright part (bright part) or the dark part (dark part) becomes one pixel based on the brightness value of the image. As a result, an image composed of a bright line or a dark line having a thickness of “1” is obtained. Several methods have been proposed for this. Here, one typical method will be described. Note that the background pattern bd is a dark pixel (a pixel with a low gradation value), and the product pattern pd is assumed to be composed of a bright pixel (a pixel with a high gradation value).

  In the thinning process, eight reference area patterns (referred to as “thinning elements”) are applied to the image data to determine whether or not each pixel can be deleted. That is, if the same area as the following thinning element is present in the image data, the luminance value of the center pixel is replaced with a dark part.

  FIG. 7 shows the thinning element. Let each be EL1 to EL8. For example, in EL1, when Ap1, Ap2, and Ap3 of the reference region are dark portions (B), and Ap6, AP7, and Ap8 are bright portions (W), if the central pixel (A) is a bright portion, Indicates when it can be changed. Here, “X” may be a dark part or a bright part. Here, when it is changed to a dark part, it seems that the pixel is deleted regarding the luminance value.

  EL1 changes the pixel in contact with the boundary with the dark part to the dark part by one pixel when the pixels are arranged horizontally and continuously. By this processing, a portion that is long in the horizontal direction of the screen is deleted from the upper direction of the screen. However, since there are three reference areas in the horizontal direction, when the image data becomes 2 pixels or less in width, the pixels are not deleted from above.

  EL2 is a process of changing the central pixel (A) corresponding to the corner to a dark part while leaving the oblique portions of Ap4 and Ap7. EL3 to EL8 are different directions of EL1 and EL2. Therefore, the image data is deleted from the upper, right, lower, and left directions by EL1, EL3, EL5, and EL7. However, EL1 and EL5 are not applied when the number of pixels in the horizontal direction of the pixels becomes 2 or less. EL3 and EL7 are not applied when the number of vertical pixels becomes 2 or less. Further, the pixel connection in the oblique direction remains by EL2, EL4, EL6, and EL8.

  By continuing the above processing until the luminance value is not replaced, it is possible to execute a thinning process in which the thickness of the image data is “1”. FIG. 5C shows the result of this processing.

  8 and 9 show the flow of this process. When the thinning process (S203) starts (S400), the variables are initialized. Two variables, “flag” and “EL”, are used. “Flag” is a flag for determining whether or not the gradation value is replaced. If this value is “0”, it is determined that the gradation value has not been replaced for all the pixels, and the process ends. The initial value is “0”. “EL” represents the thinning element described in FIG. For example, EL = 1 represents EL1. The initial value is “1”.

  Two types of image data “in” and “out” are prepared for correcting and updating the image data. These image data may be the gradation value and coordinates of the reference pattern Vsd. In initialization, “in” and “out” may be the same.

  Next, it is determined whether EL = 9 (S404). This is to know whether or not eight types of thinning elements have been applied to the image data. When there are still thinning elements to be applied (N branch in S404), gradation value replacement processing C is performed (S406). Details of step S406 are performed in FIG.

  Then, EL is incremented (S408), and the process returns to the determination (S404) of whether or not EL is “9”. By performing this process, eight thinning elements are sequentially applied to all the pixels, and the gradation value is replaced by the process C. If EL = 9 in step S404, it is determined that all eight thinning elements have been applied, and an end determination is made (S410).

  The end determination is made by determining whether or not the flag is “0”. If completed (Y branch of S410), a thinned image is created (S412), and the process returns to the main routine composition processing (S204) described in FIG. 2 (S414). If not (N branch of S410), the process returns to variable initialization (S402). The thinned image is image data obtained by extracting the same coordinates as the bright pixels obtained by the thinning process from the contour distance pattern. That is, a pixel having the same coordinates as the bright pixel obtained by the thinning process is extracted from the contour distance pattern, and its skeleton information (B) is set to “1”. By this process, the image data of FIG. 5C can be obtained.

  Next, the gradation value replacement processing C will be described with reference to FIG. In the gradation value replacement process C, the positional relationship of the same pixel as one of the eight thinning elements currently set is found and applied. Therefore, first, the center of the reference area is set at the head of the image data (S420). Then, the pixels are sequentially examined until a pixel relationship with the thinning element currently designated in each pixel of the reference pixels is found. Then, it is determined whether or not the gradation value of the central pixel of the reference pixel is larger than the maximum value (MAX (B)) of the dark portion and smaller than the minimum value (MIN (W)) of the bright portion ( S422). That is, an area that matches the thinning element currently applied is found, and it is determined whether or not the central pixel of the reference area is a bright part.

  If it is a thinning element and the central pixel of the reference area is a bright part (W), the pixel is converted into the maximum value of the gradation value of the dark part. That is, a dark part is set (= deleted). The flag is set to “1”. This is because pixel gradation values are replaced.

  It is determined whether or not the pixel is the final pixel (S428). If it is not the final pixel, the process proceeds to the next pixel (S432), and returns to the determination of the pixel value (S422). When the process reaches the last pixel, the content of the image data “in” is replaced with the image data “out”. This is because the replacement of the gradation value is performed on the image data “out”. Then, the process returns to the gradation value replacement process C (S406). As a result, FIG. 5C can be obtained as a thinning process result based on the gradation value.

  Next, a pruning process is performed on the image data resulting from the synthesis process. Reference is made to the flow of FIG. First, it is determined whether or not the pruning process is finished (S272). The termination determination here is the presence or absence of a pixel to be pruned (deleted) as a branch. More specifically, when the parameter indicating the distance (d) from the boundary is “L” (L) and the maximum value of the distance from the boundary among the pixels of the reference pattern is Lmax, the skeleton information (B) Is not “0” and a pixel Epd having a distance (d) from the boundary of L = 1 to L = Lmax−1 is determined whether or not there is a pixel that can be deleted.

  The end determination of the pruning process is also the end determination of the skeletonization process. When there are no more pixels to delete, the skeletonization process ends. Otherwise, it is determined whether the processing has been completed for a predetermined pixel among the pixels belonging to the product pattern (S274). More specifically, among the pixels Epd, it is determined whether or not the processing is completed for the pixels whose skeleton information (B) is “1” and whose distance (d) from the boundary is L. If completed, the parameter 1 indicating the distance to be deleted is incremented. The parameter L is given “1” as an initial value.

  If the inspection for the predetermined pixel Epd as described above has not been completed (N branch of S274), first, the skeleton of the middle pixel in the reference area is set to the peripheral pixels Ap1 to Ap8 other than the middle pixel in the reference area. The number of pixels whose information (B) is “1” is confirmed, and the magnitude of the distance from the boundary of Ap1, Ap3, Ap6, Ap8 is compared only when there is one (S278). Ap1, Ap3, Ap6, and Ap8 are pixels having an oblique relationship with respect to the middle pixel A in the reference region with reference to FIG. 4B.

It should be noted that the number of pixels having the skeleton information (B) of 1 in the peripheral pixels Ap1 to Ap8 other than the pixel in the middle of the reference region is “SAp ⁱ (B)”. In addition, Ap1, Ap3, Ap6, and the distance from the boundary of Ap8 (d) is the "Ap ^o (d)", was Ap2, Ap4, Ap5, Ap7 skeletal information of the (B) and "Ap ^e (B)" .

  Here, any of the distances (d) from the boundary of Ap1, Ap3, Ap6, Ap8 is larger than the distance (d) from the boundary of the pixel A in the middle of the reference region, and the skeleton information of Ap2, Ap4, Ap5, Ap7 If (B) is “0” (Y branch of S278), the skeleton information (B) of the middle pixel is set to “0” (S280). The distance (d) from the current boundary is L, the peripheral pixel has only one skeleton information (B) of “1”, and the pixel exists only in an oblique direction with respect to the pixel A. If the distance (d) from the boundary is larger than the pixel A, it can be determined that the pixel is a branch. Then, the process proceeds to the next pixel and returns to step S274 (S282).

By repeating this process, all branches in the pattern subjected to the pixel deletion process are deleted, and finally the pattern shown in FIG. In this way, a pattern indicating the skeleton of the product pattern and having no branches is referred to as a reference skeleton pattern td. A pixel belonging to the reference skeleton pattern and having skeleton information (B) “1” is represented by a skeleton pixel Tpd.
In the present embodiment, the pixel deletion process and the pruning process are performed on the contour distance pattern to obtain the reference skeleton pattern, but the method for obtaining the reference skeleton pattern is not limited to this.

  Next, the skeleton distance calculation process (S206 in FIG. 2) will be described. In FIG. 11A, FIG. 5F is shown again. In the skeleton distance calculation process, the reference skeleton pattern shown in FIG. The skeleton distance calculation process is a process of creating a skeleton distance pattern that indicates how many pixels from the Tpd the surrounding pixels are located with respect to the skeleton pixel Tpd. FIG. 11B shows a result of creating a skeleton distance pattern in which a chess board distance value for obtaining a distance from the contour in the vicinity of the periphery 8 of the target pixel is given as a value of the distance (d) from the skeleton pixel Tpd. . Depending on the distance from the skeleton, the pixel in the figure shows a value corresponding to the distance.

  FIG. 12 shows a flow of the skeleton distance calculation process. When the reference skeleton pattern is input, the process starts (S300). An end determination is made (S302), and if it is ended (Y branch of S202), the process is returned to the main (step S207 in FIG. 2) (S314). Here, the end determination is made based on whether or not the distance (d) from the skeleton is given to all the pixels. Since the distance (d) from the skeleton is represented by the number of pixels from the reference skeleton pattern, data “0” is assigned to the distance (d) from the skeleton of all the pixels belonging to the image that outputs the skeleton distance calculation result in advance. It is preferable to perform the processing to be performed in advance.

  Next, it is determined whether or not the following processing has been completed for all pixels (S304). If the process has already been completed, the argument n representing the distance is incremented (S306), the process returns to step S302, and the end determination is performed. The argument n is given “0” as an initial value immediately after the start of the skeleton distance calculation process.

  If the following processing has not been completed for all pixels (N branch in S304), the following processing is performed. Similar to the contour distance calculation process, first, attention is paid to a 9-pixel region (reference region) centered on the pixel. The middle pixel is denoted by A, and the surrounding pixels (eight) are denoted by Ap1 to Ap8. If the distance (d) from the skeleton of any of the peripheral pixels Ap is n−1 (Y branch in S308), the distance (d) from the skeleton is assumed that the pixel A is a pixel to which the distance is given. N is assigned to (S310).

  For example, when searching for a pixel whose distance (d) from the skeleton is “1” (when n = 1), a certain pixel is defined as the center of the reference area (pixel A), and any peripheral pixel of the pixel A If there is a pixel belonging to the background pattern whose distance (d) from the skeleton is “0”, “1” is assigned to the distance (d) from the skeleton to the pixel A. If there is no pixel having a distance (d) from the skeleton of “0” in the peripheral pixel Ap (N branch of S308), step S310 is skipped and the process proceeds to step S312. Move to the next pixel (S312), and return to step S304. To move to the next pixel means to move the reference area to the next pixel. By repeating the above process, the distance (d) from the skeleton can be given to all the pixels Epd.

  Note that the skeleton distance calculation method is the same as the contour distance calculation method in the vicinity of all the peripheral pixels (Ap1, Ap2, Ap3, Ap4, Ap5, Ap6, Ap7, Ap8) in contact with the pixel A in the middle. It is not limited to the above-described method for calculating the chessboard distance, and the reference region is based on the city block distance limited to four neighborhoods of the upper, lower, left and right peripheral pixels (Ap2, Ap4, Ap5, Ap7) with respect to the middle pixel A. It is good also as a calculation method. Further, instead of calculating the distance between pixels by the chessboard distance or the city block distance, it may be calculated by the Euclidean distance in which the distance between the middle pixel A and the surrounding pixels is obtained as a two-dimensional Euclidean space. .

Next, the multi-master pattern output process (S207 in FIG. 2) will be described. When the pixel value of the target pixel in the skeleton distance pattern is the distance d1 from the target pixel to the skeleton, and the pixel value of the target pixel in the contour distance image is the distance d2 from the target pixel to the contour, the skeleton to the contour in the target pixel The distance d3 and the multi-master pattern value M at the target pixel are calculated by the following equations.
d3 = d1 + d2 (1)
M = d1 / d3 (2)
The multi-master pattern value M in the target pixel represents a value obtained by normalizing the distance from the skeleton to the contour in each target pixel, that is, a relative distance value from the pattern boundary to the skeleton.

  Next, the master pattern output process (S208 in FIG. 2) will be described. FIG. 13 shows a flow for generating a master pattern from a multi-master pattern. Each pixel of the multi-master pattern is compared with a predetermined coefficient (threshold) determined in advance, and only the pixel position having a pixel value smaller than the predetermined coefficient (threshold), that is, the multi-master pattern value, is set as the master pattern. Let the region be the background pattern. Setting the predetermined coefficient (threshold) to “1” sets the reference pattern, and setting the predetermined coefficient (threshold) to a value smaller than “1” sets the contraction master pattern to a value larger than “1”. Then, an expansion master pattern is obtained.

  By the above operation, a master pattern in which the line width is uniformly multiplied by a predetermined coefficient can be obtained in any part of the reference pattern. Here, the relationship between the predetermined coefficient (threshold value) and the master pattern will be described in detail with reference to FIG. When the predetermined coefficient is R, if R is set to a value larger than 1, a master pattern in which the reference pattern is expanded can be obtained, and if R is set to a value smaller than 1, the master in which the reference pattern is contracted is obtained. A pattern can be obtained. When a plurality of master patterns are desired, steps S290 to S302 may be performed any number of times while changing the predetermined coefficient value R.

  For example, if R = 1.2 is set, only the pixel position where the pixel value of the multi-master pattern is 1.2 or more is set as the master pattern, so that the expansion master pattern 153 in FIG. 14 is obtained. If R = 1 is set, only the pixel position where the pixel value of the multi-master pattern is 1 or less is used as the master pattern, so that the master pattern 152 having the same shape as the reference pattern in FIG. 14 is obtained. Further, if R = 0.8 is set, only the pixel position where the pixel value of the multi-master pattern is smaller than 0.8 is used as the master pattern, so that the contracted master pattern 151 in FIG. 14 is obtained. Further, if R = 0 is set, only the pixel position where the pixel value of the multi-master pattern is 0 is used as the master pattern, so that the contracted master pattern 150 having the same shape as the reference skeleton pattern is obtained.

As described above, the master pattern creation unit 10 creates the master pattern MP based on the input reference pattern. The created expansion / contraction master patterns are an expansion master pattern Bcdx and a contraction master pattern Bcds. That is, Bcdx and Bcds are one form of the master pattern MP. These master patterns are stored in the storage unit 80.
<First Embodiment of the Present Invention>
A pattern inspection method using the master pattern of the present invention will be described with reference to the flowcharts of FIGS. It is assumed that the expansion master pattern and the contraction master pattern are recorded in advance in the storage unit 80 based on the above description. Then, among the pixels of the product pattern, the pixel that should become the master pattern MP is given the maximum value Pmax of the pixel value after setting the master pattern information mp to “1”, and the pixel of the product pattern that does not become the master pattern. Gives the minimum pixel value P ₀ . That is, the master pattern is obtained as binary image data.

  When the inspection is started (S1000), an end determination is made (S1002). Here, the end determination is the presence or absence of an inspection object. Of course, even if there is an inspection object, it may be terminated by interruption processing. If completed (Y branch of S1002), the inspection is terminated (S1016).

  When the inspection object is inserted, the image photographing unit 20 photographs the inspection object and obtains image data Vd (S1004). The image data Vd is converted into binary image data Bcd by the binarizing unit 30 (S1006) (becomes an inspection target pattern). This is for easy comparison with the master pattern. Note that before the image data is binarized, interpolation processing such as enlargement or reduction to a predetermined size may be performed.

  The binary image data Bcd is aligned with the master pattern MP sent from the storage unit 80 by the alignment unit 50 (S1008). This is because two image patterns (inspection target pattern and master pattern) are compared. The two image patterns subjected to the alignment are calculated for each corresponding pixel by the calculation unit 60, and the magnitude comparison with the master pattern is performed (S1010).

  More specifically, the pixel value of the corresponding pixel belonging to the expansion master pattern Bcdx is subtracted from the image data of the pixel belonging to the binary image data Bcd of the inspection object, and the number Cdx when the pixel value becomes positive, and the contraction master pattern The binary image data Bcd of the inspection object is subtracted from Bcds to obtain the number Cds when it becomes positive.

  A pixel value larger than the image of the expansion master pattern among the pixels in the image data of the inspection object means that the inspection object has a portion that is thicker than a predetermined reference. That is, there is a risk of a protrusion defect. In addition, the fact that the contraction master pattern has a pixel value larger than the binarized image data of the inspection object means that the inspection object has a very thin portion. That is, there is a risk of chip defects.

  The determination unit 70 compares the number Cdx of pixels protruding from the expansion master pattern obtained by the calculation unit 60 and the number Cds of pixels thinner than the contraction master pattern with predetermined threshold values Thx and Ths, and determines whether or not it is acceptable. Is determined (S1012). Here, the threshold Thx is called an expansion threshold, and the threshold Ths is called a contraction threshold.

  That is, if Cdx is larger than Thx, an unacceptably thick portion exists in the pattern of the inspection object. Therefore, it is determined as a protrusion defect. Similarly, the fact that Cds is larger than Ths means that an unacceptably thin portion exists in the pattern of the inspection object. Therefore, it is determined as a chip defect.

  Further, the determination unit outputs these results Con (S1014). The output result Con may be only a determination that a defect with respect to the inspection object is acceptable or not, or may directly indicate the numbers of Cdx and Cds that are differences from the master pattern. The output result Con may be displayed on a display device or the like, or may be recorded as it is or transmitted to another device. Reference numeral 88 in FIG. 1 indicates an output destination of the result Con.

  A more specific example of pattern inspection will be described with reference to FIGS. First, referring to FIG. A reference skeleton pattern td is generated from the reference pattern Vsd. The contraction master pattern Bcds is generated by the master pattern preparation unit 10 from the reference skeleton pattern td. Assume that image data Vcd1 is obtained from the inspection object. It is assumed that this inspection object has a disconnection 131 at one place in the product pattern. In Vsd, the region of the product pattern pd, the region of the skeleton information B = 1 in the reference skeleton pattern td, and the region of the master pattern information mp = 1 and the pixel value Pmax in Bcds are represented in white.

When the calculation unit 60 subtracts the pixel value of the corresponding image data Vcd1 from the pixel value of the pixel belonging to the contraction master pattern Bcds, the image data 130 is obtained. If there is a pixel having a pixel value equal to or greater than the minimum pixel value P _{0 in the} image data 130, it can be determined that the defect is a missing defect. This is because there is a thinner part than the contraction master pattern. In FIG. 16, the area 132 corresponds.

Reference is now made to FIG. The master pattern is the expansion master pattern Bcdx. Here, it is assumed that the image data Vcd2 is obtained from the inspection object. It is assumed that this inspection object has a portion 133 that protrudes in the product pattern. When the pixel value of the corresponding image data Vcd2 is subtracted from the pixel value of the pixel belonging to the expansion master pattern, the image data 134 is obtained. If this was a pixel having the minimum value P ₀ or more pixel values of the pixel values in the image data 134, it can be determined that a protrusion defect. This is because there is a thicker part than the expansion master pattern. In FIG. 17, this is a region 135. In FIG. 17, the white area is shown in FIG. However, Bcds is replaced with Bcdx.

As described above, by using the master pattern MP of the present invention, the pattern inspection of the inspection object can be performed. In particular, it is useful to use the master pattern MP according to the present invention when the product pattern includes a portion having a large thickness difference ratio.
<Second Embodiment of the Present Invention>
In the first embodiment, among the pixels of the product pattern, the pixel that is to become the master pattern MP is given the maximum value Pmax of the pixel value after setting the master pattern information mp to “1”, and the product that does not become the master pattern the pixel pattern gave minimum P ₀ of the pixel values. This means that the master pattern is obtained as binarized image data.

  Therefore, the difference between the master pattern and the inspection object pattern (image data Vd) of the inspection object is obtained as the number of pixels (Cdx or Cds), and the degree of defect cannot be determined quantitatively. For example, even if the portion Cdx that protrudes from the expansion master pattern is less than the expansion threshold Thx and the determination by the determination unit is acceptable, the portion that is almost the same as the reference pattern or thicker than the reference pattern Therefore, it is impossible to make a judgment as to whether it was accepted because it did not slightly exceed the expansion threshold value Thx.

  Therefore, in the present embodiment, an embodiment in which the difference from the reference pattern can be evaluated more quantitatively will be described.

  In the present embodiment, when creating a master pattern, a pixel value corresponding to a distance from the reference skeleton pattern is given to pixels belonging to the master pattern MP.

  Next, a method for pattern inspection using a multi-master pattern will be described.

  FIG. 18 shows an example of processing for inspecting chips and protrusions using a multi-master pattern. A reference skeleton pattern td is generated from the reference pattern Vsd. A multi-master pattern is generated by the master pattern preparation unit 10 from the reference skeleton pattern td.

  Assume that the image data Vcd1 is obtained from the inspection object. It is assumed that this inspection object has a disconnection 131 at one place in the product pattern. In Vsd, the product pattern pd area is shown in white, and in Vcd1, the pattern area on the inspection object is shown in white.

  When the arithmetic unit 60 subtracts the pixel value of the corresponding image data Vcd1 from the pixel value of the pixel belonging to the reference pattern Vsd, the image data 130 is obtained. The image data 130 includes a region 132 that is a disconnection defect. If the pixel value of any pixel in the region 171 belonging to the multi-master pattern corresponding to the region 132 is equal to or greater than a predetermined defect inspection threshold, it can be determined that the defect is a defect.

  In addition, by observing the minimum value of the pixel value of the multi-master pattern pixel to which the region 171 belongs, not only whether the detected chip defect exceeds a predetermined chip inspection threshold, but also how much is relative to the line width. It is possible to quantitatively measure whether the ratio is missing.

  Next, it is assumed that image data Vcd2 is obtained from the inspection object. This inspection object is assumed to have one protrusion 133 in the product pattern. In Vsd, the product pattern pd region is shown in white, and in Vcd2, the pattern region on the inspection object is shown in white.

  When the calculation unit 60 subtracts the pixel value of the pixel of the corresponding reference pattern Vsd from the pixel value of the pixel belonging to the image data Vcd2, the image data 134 is obtained. The image data 134 includes a region 135 that is a protrusion defect. When the pixel value of any pixel in the region 173 belonging to the multi-master pattern corresponding to the region 135 is equal to or less than a predetermined protrusion inspection threshold, it can be determined that the defect is a protrusion defect.

  In addition, by observing the maximum value of the pixel value of the multi-master pattern pixel to which the region 173 belongs, not only whether the detected protrusion defect exceeds a predetermined protrusion inspection threshold value, but also how much is relative to the line width. It can be quantitatively measured whether it is what protrudes by the ratio.

  As described above, by using the multi-master pattern, it is possible to quantify the extent of the defect from the relationship with the inspection threshold.

  FIG. 19 shows a contraction image and an expansion image created by using the method of the present invention and the conventional method as a pattern in which the reference image is composed of triangles. 19A is a reference image, FIG. 19B is a reduced image by the conventional method, FIG. 19C is an expanded image by the conventional method, FIG. 20A is a reduced image by the present invention, and FIG. (B) is an expanded image according to the present invention.

  The reduced image and the expanded image obtained by the conventional method do not reflect the shape of the original reference image, whereas the reduced image and the expanded image obtained by the method of the present invention both reflect the shape of the original reference image. You can see that

  The present invention can be suitably used for automatic defect inspection of circuit patterns and the like.

DESCRIPTION OF SYMBOLS 1 Pattern test | inspection apparatus 10 Master pattern preparation part 20 Image photographing part 30 Binarization part 50 Positioning part 60 Calculation part 70 Judgment part 80 Storage part 85 Control apparatus

Claims (8)

A pattern inspection method for detecting a defect of the inspection object by comparing a master pattern and an inspection object pattern of the inspection object based on image data,
A first step of obtaining a contour distance pattern by performing a contour distance calculation process on a reference pattern having a background pattern and a product pattern;
A second step of skeletonizing the contour distance pattern to obtain a reference skeleton pattern;
A third step of obtaining a skeleton distance pattern from the reference skeleton pattern;
A fourth step of obtaining a master pattern from the contour distance pattern and the skeleton distance pattern;
A pattern inspection method comprising a fifth step of detecting a difference by comparing the master pattern and the inspection object pattern. The first step includes
The pattern inspection method according to claim 1, wherein the pattern inspection method is a process of assigning a distance from a boundary between the background pattern and the product pattern to pixels belonging to the reference pattern. The second step includes
Pixel deletion processing that sequentially deletes the pixels until the value of the distance from the boundary of the surrounding pixels of the pixel belonging to the reference pattern is larger than the value of the distance from the boundary of the pixels. A thinning process for deleting the pixels until the line width of the product pattern is minimized, a synthesis process for superimposing the pixel deletion processing image and the thinning processing image, and among the pixels belonging to the reference pattern The pattern inspection method according to claim 1, further comprising a pruning process for deleting branches from pixels remaining as a result of the synthesis process. The third step includes
The pattern inspection method according to claim 1, wherein the pattern inspection method is a process of assigning a distance from the reference skeleton pattern to pixels belonging to the reference pattern. The fourth step includes
For each pixel belonging to the reference pattern, the distance from the pixel to the contour and the distance from the pixel to the skeleton are calculated to calculate the distance from the skeleton to the contour, and the distance from the pixel to the skeleton is calculated from the skeleton to the contour. 5. The pattern inspection method according to claim 1, further comprising a process of obtaining a multi-master pattern by using a value divided by the distance as a relative distance value in the pixel as a master value in the pixel. The fourth step includes
The pattern inspection method according to claim 1, wherein a pixel value corresponding to the relative distance value is set as a pixel value of a pixel belonging to the master pattern. The pattern inspection method according to claim 6, further comparing the difference detected in the fifth step with the multi-master pattern. A master pattern creation unit that receives a reference pattern having a background pattern and a product pattern and outputs a master pattern;
A storage unit for recording the master pattern;
An imaging unit for imaging the inspection object and obtaining image data;
A binarization unit that binarizes the image data to obtain an inspection target pattern;
An arithmetic unit that compares the master pattern and the inspection target pattern and outputs a difference;
A determination unit that outputs a determination result based on the output of the arithmetic unit;
The master pattern production unit
Providing a distance from the boundary between the background pattern and the product pattern to the pixels belonging to the product pattern to create a contour distance pattern,
Creating a reference skeleton pattern by skeletonizing the reference pattern;
Create a skeleton distance pattern by giving a value of the distance from the reference skeleton pattern to the pixels belonging to the reference pattern,
For each pixel belonging to the reference pattern, the distance from the pixel to the contour and the distance from the pixel to the skeleton are calculated to calculate the distance from the skeleton to the contour, and the distance from the pixel to the skeleton is calculated from the skeleton to the contour. Create a multi-master pattern with the pixel value at that pixel divided by the distance
A pattern inspection apparatus for producing a master pattern from the multi-master pattern.