JP2007047020A - Image defect inspection device, image defect inspection system, and image defect inspection method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To reduce the generation of a pseudo-defect when a lightness difference exists between two compared images, in an image defect inspection device and a method for detecting a difference between fellow picture elements corresponding to the two images to detect a picture element portion as a defect when the difference exceeds a detection threshold. <P>SOLUTION: The image defect inspection device 10 of the present invention is provided with a correlation value calculating means 20 for calculating a correlation value between a differential image in the two collated images and at least one out of the two images, and the detection of the defect is restrained in response to an increase of the correlation value. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention detects a gray level difference between corresponding portions of two images, compares the detected gray level difference with a threshold value, and determines that the defect is a defect when the gray level difference is larger than the threshold value. The present invention relates to an image defect inspection system and an image defect inspection method.

  The present invention is directed to an image processing method and apparatus that compares corresponding portions of two images that should be the same and determines a portion having a large difference as a defect. Here, an example of an appearance inspection apparatus (inspection machine) that detects defects in a semiconductor circuit pattern formed on a semiconductor wafer in a semiconductor manufacturing process will be described, but the present invention is not limited to this.

  A general appearance inspection apparatus is a bright field inspection apparatus that illuminates a target surface from the vertical direction and captures an image of reflected light, but a dark field inspection apparatus that does not directly capture illumination light is also used. In the case of a dark field inspection apparatus, a sensor is arranged so that regular reflection is not detected by illuminating the target surface from an oblique direction or a vertical direction, and a dark field image of the target surface is obtained by sequentially scanning the irradiation position of the illumination light. . Therefore, the image sensor may not be used in the dark field device, but this is also an object of the invention. In this way, the present invention compares any corresponding part of two images (signals) that should be the same, and any method and apparatus as long as it is an image processing method and apparatus that determines a part having a large difference as a defect. It is also applicable to the device.

  In the semiconductor manufacturing process, a large number of chips (dies) are formed on a semiconductor wafer. Each die is patterned over several layers. The completed die is electrically inspected by a prober and tester, and the defective die is removed from the assembly process. In the semiconductor manufacturing process, the yield is very important, and the result of the electrical inspection is fed back to the manufacturing process and used for managing each process. However, the semiconductor manufacturing process is formed in many processes, and it takes a very long time from the start of manufacturing until the electrical inspection is performed. At this time, a large number of wafers are already being processed, and the inspection results cannot be fully utilized for improving the yield. Therefore, pattern defect inspection is performed in which a pattern formed in an intermediate process is inspected to detect defects. If pattern defect inspection is performed in a plurality of processes among all processes, defects generated after the previous inspection can be detected, and the inspection result can be quickly reflected in the process management.

  FIG. 1 shows a block diagram of an appearance inspection apparatus proposed by the applicant of the present patent application in Japanese Patent Application No. 2003-188209 (the following Patent Document 1). As shown in the figure, a sample stage (chuck stage) 2 is provided on the upper surface of a stage 1 that can move freely in two-dimensional or three-dimensional directions. On this sample stage, the semiconductor wafer 3 to be inspected is placed and fixed. An imaging device 4 configured using a one-dimensional or two-dimensional CCD camera or the like is provided above the stage, and the imaging device 4 generates an image signal of a pattern formed on the semiconductor wafer 3.

  As shown in FIG. 2, a plurality of dies 3A are repeatedly arranged in a matrix on the semiconductor wafer 3 in the X direction and the Y direction, respectively. Since the same pattern is formed on each die, it is common to compare images of corresponding portions of adjacent dies. If there is no defect in both dies, the gray level difference is less than the threshold, but if there is a defect in one, the gray level difference is greater than the threshold (single detection). Since this does not know which die is defective, it is further compared with adjacent dies on different sides, and if the difference in gray level of the same part becomes larger than the threshold, it can be seen that the die is defective (double Detection).

  The imaging device 4 includes a one-dimensional CCD camera, and moves the stage 1 so that the camera moves (scans) relative to the semiconductor wafer 3 at a constant speed in the X direction or the Y direction. The image signal is converted into a multi-value digital signal (gray level signal) and then input to the difference detection unit 6 and stored in the signal storage unit 5. When the gray level signal (inspection image signal) of the adjacent die is generated by scanning, the gray level signal (reference image signal) of the previous die stored in the signal storage unit 5 is read in synchronization with the gray level signal (inspection image signal). 6 Actually, a minute alignment process is performed, but detailed description is omitted here.

The difference detection unit 6 receives the gray level signals of two adjacent dies, calculates the difference between the two gray level signals (gray level difference), and outputs the difference to the detection threshold calculation unit 7 and the defect detection unit 8. .
Here, the difference detection unit 6 calculates the absolute value of the gray level difference of each pixel included in the captured images of the two dies to be compared, and outputs it as a gray level difference. The detection threshold calculation unit 7 determines a detection threshold according to the distribution of gray level differences and outputs the detection threshold to the defect detection unit 8. The defect detection unit 8 compares the gray level difference with the determined threshold value and determines whether or not the defect is present. In general, a semiconductor pattern has a different noise level depending on a pattern type such as a memory cell portion, a logic circuit portion, a wiring portion, an analog circuit portion, and the like. Correspondence between semiconductor pattern portions and types can be understood from design data. Therefore, for example, the detection threshold calculation unit 7 performs threshold determination processing for each part to determine the threshold, and the defect detection unit 8 performs determination based on the threshold determined for each part.

JP 2004-177397 A JP-A-4-107946 Japanese Patent No. 2996263 Japanese Patent Laid-Open No. 2002-22421

However, in an actual captured image obtained by imaging each die placed on the wafer, a brightness difference (so-called “color unevenness”) often occurs between the captured images of two dies that should be the same.
When defect inspection is performed using such two images with large color unevenness, compared to the case where an image with little color unevenness is used, the gray level difference is generally large, and thus a pseudo defect is likely to occur. For this reason, in the above-mentioned Patent Document 1, an average of gray level differences between two images, a gray level difference at which the cumulative frequency of gray level differences is 50%, or two positive and negative thresholds are separately calculated, and the average is calculated. As a correction value, the gray level difference between the two images is corrected so that the number of zero pixels is the largest.

  However, in the method described in Patent Document 1, the gray level difference is corrected according to the bias of the distribution of the entire gray level difference between the two images, so that the area where the color unevenness occurs in the gray level signal between the two images. When the ratio occupied by is small, processing is performed in the same manner as when no color unevenness occurs, and a pseudo defect occurs in a portion where the color unevenness occurs. In order to avoid this, it is necessary to take measures such as detecting locally uneven color and setting the detection threshold higher.

In view of the above problems, the present invention detects an image defect difference between corresponding pixels of two images, and detects an image defect as a defect when the difference exceeds a detection threshold. The purpose is to reduce the occurrence of pseudo defects when there is a difference in brightness between two images to be compared.
In particular, the present invention aims to reduce the occurrence of pseudo defects in the above-described image defect inspection apparatus and method when the inspection image and the reference image have local brightness differences depending on the patterns included in these images. And

In order to achieve the above object, in the present invention, a correlation value between a difference image between two images to be compared and at least one of these two images is calculated. Suppress detection.
That is, when there is a brightness difference (color unevenness) between the two images to be compared, the correlation value between the difference image and the original two images increases as will be described later. If the detection of defects is suppressed according to the above, it is possible to reduce pseudo defects caused by color unevenness between two images.
On the other hand, the difference image also includes a pattern caused by a true defect included in the original image, but this defect pattern has little correlation with the pattern originally included in the original image, and the correlation between the difference image and the original image. It is difficult to influence the value. Therefore, it is possible to prevent a situation in which defect detection is suppressed due to the presence of a true defect.

That is, the image defect inspection apparatus according to the first embodiment of the present invention detects a difference in pixel values between corresponding pixels of the inspection image and the reference image, and detects a pixel portion as a defect when the difference exceeds a detection threshold. An image defect inspection apparatus, a difference image generation unit for generating a difference image between an inspection image and a reference image, and a correlation value calculation for calculating a correlation value between at least one of the inspection image and the reference image and the difference image And detecting defects as the correlation value increases.
Note that the image defect inspection apparatus may output a correlation value together with the detected defect.

  In addition, the image defect inspection system according to the second aspect of the present invention detects a pixel value difference between corresponding pixels of the inspection image and the reference image, and detects the pixel portion as a defect when the difference exceeds a detection threshold. An image defect inspection apparatus, a difference image generation unit for generating a difference image between an inspection image and a reference image, and a correlation value calculation for calculating a correlation value between at least one of the inspection image and the reference image and the difference image An image defect inspection apparatus that outputs a correlation value together with detected defects, and an automatic defect classification apparatus that classifies the output defects based on the correlation value output by the image defect inspection apparatus. It is prepared for.

Furthermore, the image defect inspection method according to the third aspect of the present invention detects a pixel value difference between corresponding pixels of the inspection image and the reference image, and detects the pixel portion as a defect when the difference exceeds a detection threshold. An image defect inspection method for generating a difference image between an inspection image and a reference image, calculating a correlation value between at least one of the inspection image and the reference image and the difference image, and responding to an increase in the correlation value To suppress the detection of defects.
Note that the image defect inspection method may classify the detected defect based on the correlation value.

  Various means and methods for suppressing the detection of defects may be considered. For example, the defect detection threshold value may be corrected according to the correlation value, or the output value of the detected defect is set as the correlation value. You may judge according to it. Alternatively, the correlation value may be output together with the detected defect, and the defect may be classified and deleted later based on the correlation value.

Various means and methods for calculating a correlation value between at least one of the inspection image and the reference image and the difference image are conceivable. For example, between at least one of the inspection image and the reference image and the difference image, A correlation coefficient may be calculated to obtain a correlation value.
Alternatively, the correlation coefficient may be calculated by calculating a correlation coefficient between at least one of the inspection image and the reference image and an image obtained by performing predetermined image processing (for example, binarization) on the difference image.
Further, a correlation coefficient is calculated between an image (for example, a differential image) obtained by performing predetermined image processing on at least one of the inspection image and the reference image, and an image (for example, differential image) obtained by performing predetermined image processing on the difference image. It may be calculated or a correlation value.

  According to the present invention, even when there is a difference in brightness between an inspection image and a reference image compared with the inspection image, the occurrence of pseudo defects is reduced, thereby contributing to improvement in detection accuracy.

Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. FIG. 3 is a block diagram of an appearance inspection apparatus for a semiconductor circuit according to the first embodiment of the present invention. The appearance inspection apparatus shown in FIG. 3 has a configuration similar to that of the conventional appearance inspection apparatus described with reference to FIG. 1, and therefore, the same components are denoted by the same reference numerals and described. Omitted.
The difference detection unit 6 calculates the pixel value (gray level signal) of each corresponding pixel of the two images from the corresponding part of the captured image obtained by imaging the two dies (one is the inspection image and the other is the reference image). Differences (gray level differences) are detected, and a difference image is created using these difference signals as pixel values.

  The appearance inspection apparatus 10 includes a correlation value calculation unit 20 that calculates a correlation value between the difference image created by the difference detection unit 6 and either the inspection image or the reference image that is the original image, and the detection threshold calculation unit 7. The detection threshold correction unit 21 that suppresses the detection of defects according to the increase in the correlation value calculated by the correlation value calculation unit 20 by correcting the detection threshold calculated by A difference image storage unit 11; a first original image storage unit 12 that stores an inspection image; and a second original image storage unit 13 that stores a reference image.

  Hereinafter, the principle of the present invention will be described with reference to FIGS. 4 to 7 with reference to an actual inspection image, a reference image, and a difference image calculated therefrom. (A), (B), and (C) in FIG. 4 are an inspection image, a reference image, and a difference image between these when no color unevenness is generated in the inspection image. (B) and (C) are an inspection image, a reference image, and a difference image between them when color unevenness occurs in the inspection image, respectively.

When there is no color unevenness in the inspection image ((A) in FIG. 4), there is almost no difference between the inspection image and the reference image, so that the difference image has a small pixel value as shown in FIG. 4 (C). A uniform image.
On the other hand, when there is color unevenness in the inspection image (FIG. 5A), a local area that has a dependency on a pattern (for example, a circuit pattern on the die 3A) that originally exists in the inspection image. If there is color unevenness, a slight brightness difference occurs between the inspection image and the reference image due to the color unevenness, and due to this, the difference image ((C) in FIG. 5) originally exists in the original image. The image has a correlation with the existing pattern.

  Table 1 shown below is a difference image when there is no color unevenness ((C) in FIG. 4), a difference image when there is color unevenness ((C) in FIG. 5), and a basis for generating them. It is a table | surface which contrasts the correlation coefficient between each test | inspection image and a reference | standard image.

Here, the correlation coefficient R1 is normalized by setting each pixel value of _one image as Pixel ₁ (x, y), each pixel value of the other image as Pixel ₂ (x, y), and n as the total number of pixels. The calculated correlation coefficient was calculated by the following equation (1).

As is clear from the correlation value between the inspection image and the difference image shown in Table 1, the correlation value between the difference image and the original image increases when there is color unevenness compared to when there is no color unevenness, It can be seen that the correlation between the difference image and the original image is strong.
On the other hand, (A), (B), and (C) in FIG. 6 are an inspection image, a reference image, and a difference image between these, respectively, when the inspection image does not include a defect. (B) and (C) are an inspection image, a reference image, and a difference image between them, respectively, when the inspection image includes a defect.

In the difference image when the inspection image has a defect, a pattern corresponding to the defect is generated as shown in FIG. 7C. This pattern is generated regardless of the pattern that originally exists in the original image. There is a weak correlation with the original image.
Actually, the difference image when the defect is not included ((C) in FIG. 6), the difference image when the defect is included ((C) in FIG. 7), and the respective inspection images that are the basis for generating these The correlation coefficient with the reference image is appended in Table 1.

As is apparent from the correlation value between the inspection image and the difference image shown in Table 1, the defect is included in comparison with the increase rate (about 9 times) between the case where there is no color unevenness and the case where there is color unevenness. The increase rate (about ½) between the case and the case not including a defect is small, and even if a defect pattern exists as expected, the influence on the correlation with the original image is small.
Therefore, the correlation value calculation unit 20 of the appearance inspection apparatus 10 calculates a correlation coefficient R1 between the difference image and either the inspection image or the reference image that is the original image as a correlation value. Here, the correlation coefficient R1 may be calculated for both the inspection image and the reference image, and the larger value may be output.

The detection threshold correction unit 21 corrects the detection threshold calculated by the detection threshold calculation unit 7 so as to increase as the correlation coefficient R1 increases, thereby reducing the detection sensitivity and suppressing the number of detected defects.
As the correlation value calculated by the correlation value calculation unit 20, various calculated values indicating the correlation between the two types of multivariate can be used in addition to the normalized correlation coefficient R1. For example, the correlation value calculation unit 20 may use a correlation coefficient R2 that is not normalized with a variance value of pixel values as shown in the following equation (2).

  Regarding the correlation coefficient R2, when there is no color unevenness, when there is no color unevenness, when there is no defect, and when a defect is included, between the difference image and the respective inspection images and reference images from which these are generated Table 2 shows the correlation coefficient.

Now, if the original image has color unevenness, a pattern resulting from the pattern that originally exists in the original image is generated in the difference image as described above. Therefore, the correlation with the original image is strengthened and the correlation value is increased. Let
However, local unevenness in brightness difference also occurs in the difference image due to local color unevenness included in the inspection image and the reference image that are the original images. Since the unevenness of the brightness difference is caused by the brightness difference between both the inspection image and the reference image, the increase in the correlation value may be inhibited when the correlation value with either the inspection image or the reference image is calculated. .

Therefore, in the appearance inspection apparatus according to the second embodiment of the present invention shown in FIG. 8, the difference image output from the difference detection unit 6 is subjected to image processing such as edge detection processing and binarization, and is present in the difference image. A difference image image processing unit 31 is provided to remove unevenness in brightness difference.
In Table 3 below, as an example of the above-described image processing, the difference image is binarized. When there is no color unevenness and when there is no color unevenness, and when there is no defect and when there is a defect, The correlation coefficient R1 calculated between each inspection image and the reference image that is the basis for generating these is shown, and Table 4 below shows the correlation coefficient R2 calculated in the same manner.

As can be seen from Tables 3 and 4, compared with the correlation coefficients R1 and R2 calculated in Tables 1 and 2, the increase rate of the correlation coefficient when there is color unevenness relative to the correlation coefficient when there is no color unevenness is It becomes larger and it becomes possible to detect color unevenness more accurately.
On the other hand, as can be seen from Tables 3 and 4, the change rate of the correlation coefficient due to the presence or absence of defects is kept smaller than the change rate of the correlation coefficient due to the presence or absence of color unevenness. It is also possible to prevent this.

  Further, in the appearance inspection apparatus according to the third embodiment of the present invention shown in FIG. 9, the unevenness of the brightness difference existing in the difference image is also obtained by performing image processing such as edge detection processing on the inspection image and the reference image which are original images. Remove. Thus, the correlation coefficient is calculated in a state where the color unevenness of the original image and the color unevenness of the difference image are removed, and the color unevenness is detected more accurately. Therefore, a first original image processing unit 32 that performs image processing on the inspection image and a second original image processing unit 33 that performs image processing on the reference image are provided.

  By removing the color unevenness of the original image, even if the correlation value with the difference image is calculated using either the inspection image or the reference image, the difference between the correlation values can be reduced. As a result, a process of calculating both the correlation value between the inspection image and the correlation image between the inspection image and the reference image performed by the correlation value calculation unit 20 in the first and second embodiments and selecting the larger one is performed. It can be omitted.

As an example of the image processing performed on the difference image, the inspection image, and the reference image, for example, processing that performs edge processing such as differentiation may be employed. 10A shows an image obtained by performing edge processing on the image shown in FIG. 4A, FIG. 10B shows an image obtained by performing edge processing on the image shown in FIG. 4B, FIG. 10C shows an image obtained by performing edge processing on the image of FIG. 11A shows an image obtained by performing edge processing on the image shown in FIG. 5A, and FIG. 11B shows an image obtained by performing edge processing on the image shown in FIG. 5B. FIG. 11C shows an image obtained by performing edge processing on the image of FIG. 5C.
12A shows an image obtained by performing edge processing on the image shown in FIG. 6A, and FIG. 12B shows an image obtained by performing edge processing on the image shown in FIG. 6B. FIG. 12C shows an image obtained by performing edge processing on the image of FIG. 6C. Further, FIG. 13A shows an image obtained by performing edge processing on the image shown in FIG. 7A, and FIG. 13B shows an image obtained by performing edge processing on the image shown in FIG. 7B. FIG. 13C shows an image obtained by performing edge processing on the image of FIG. 7C.

  And in Table 5, when there is no color unevenness and when there is, and when there is no defect and when it contains a defect, a differential image (edge-processed image) of the difference image and the basis for generating these The correlation coefficient R1 calculated between the differential image of the inspection image and the differential image of the reference image is shown. Table 6 below shows the correlation coefficient R2 calculated similarly.

  The method for suppressing defect detection is not limited to the method for correcting the detection threshold as described above. For example, for each of the defects detected by the defect detection unit 8, the above correlation value is obtained with respect to the peripheral image (including the defect portion), and when the correlation value obtained for the defect is large (that is, color unevenness is large). Even if the output of this defect is prohibited, the number of detected defects can be suppressed as the correlation value increases. FIG. 14 shows an appearance inspection apparatus according to the fourth embodiment of the present invention.

In the appearance inspection apparatus 10, the difference image storage unit 11 temporarily stores the difference image output from the difference detection unit 6 at least during the defect detection processing by the defect detection unit 8, and the first original image storage unit 12 temporarily stores the inspection image at least during the defect detection process, and the second original image storage unit 13 temporarily stores the reference image at least during the defect detection process.
Then, the appearance inspection apparatus 10 is based on the defect information including the position and size of the defect detected by the defect detection unit 8, and includes a predetermined part including the defect portion from the difference image stored in the difference image storage unit 11. A difference image selection unit 41 that selects a partial image having a size and outputs the partial image to the correlation value calculation unit 20 in the subsequent stage via the difference image image processing unit 31 and the first original image storage unit 12 based on the defect information. A first original image selection unit 42 that selects a partial image having a predetermined size from the inspection image to be output and outputs the partial image to the correlation value calculation unit 20 at the subsequent stage via the first original image image processing unit 32. Based on the defect information, a partial image having a predetermined size including the defective portion is selected from the reference image stored in the second original image storage unit 13 and the second original image image processing unit 33 is used. Second original image selection to be output to the correlation value calculation unit 20 at the subsequent stage It includes a 43, a.

  Then, the correlation value calculation unit 20 is selected by the difference image selection unit 41 and processed by the difference image image processing unit 31, and the first original image selection unit 42 and the second original image selection unit 43 respectively. A defect output is calculated by calculating a correlation value between at least one of the partial images of each original image selected and processed by the first original image image processing unit 32 and the second original image image processing unit 33, respectively. The result is output to the determination unit 22.

  For example, when the correlation value calculated for the defect is equal to or smaller than a predetermined threshold, the defect output possibility determination unit 22 permits the defect information regarding the defect to be output, and conversely, when the correlation value exceeds the predetermined threshold, the defect information is output. By prohibiting the number of detected defects, it is determined whether or not a defect can be output according to the correlation value, and the number of detected defects is suppressed according to an increase in the correlation value.

In the configuration shown in FIG. 14, the appearance inspection apparatus 10 includes a difference image image processing unit 31, a first original image image processing unit 32, and a second original image image processing unit 33, and the correlation value calculation unit 20 performs image processing. The correlation value between the obtained difference image and the original image is calculated.
However, as in the configuration illustrated in FIG. 3, the difference image image processing unit 31, the first original image image processing unit 32, and the second original image image processing unit 33 are omitted and the correlation value calculation unit 20 is not subjected to image processing. The correlation value between the image and the original image may be calculated. Alternatively, as in the configuration shown in FIG. 8, only the difference image image processing unit 31 may be provided to calculate the correlation value between the image processed difference image and the original image not subjected to image processing.

  Further, in order to suppress the number of detected defects, as shown in FIG. 15, an appearance inspection apparatus 10 and an automatic defect classification (ADC) apparatus 50 that classifies defect information detected and output by the appearance inspection apparatus 10 are provided. The appearance inspection system is configured, and the above-described correlation value information is output from the appearance inspection apparatus 10 side together with the defect information to the automatic defect classification apparatus 50. On the automatic defect classification apparatus 50 side, based on the correlation value information (for example, It may be realized by classifying defect information (according to the magnitude of the correlation value) and deleting defect information having a correlation value larger than a predetermined threshold value.

For this reason, in the appearance inspection apparatus 10 according to the fifth and sixth embodiments of the image defect inspection apparatus according to the present invention shown in FIGS. 16 and 17, the correlation value calculation unit 20 uses the calculated correlation value information as defect information. To the automatic defect classification device 50.
Note that the appearance inspection apparatus shown in FIGS. 16 and 17 has a configuration similar to that of the appearance inspection apparatus described with reference to FIGS. 9 and 14, and therefore, the same reference numerals are assigned to the same components. The description is omitted.

Further, in the configuration shown in FIGS. 16 and 17, the appearance inspection apparatus 10 includes a difference image image processing unit 31, a first original image image processing unit 32, and a second original image image processing unit 33, and a correlation value calculation unit 20. Decided to calculate the correlation value between the image-processed difference image and the original image.
However, as in the configuration illustrated in FIG. 3, the difference image image processing unit 31, the first original image image processing unit 32, and the second original image image processing unit 33 are omitted and the correlation value calculation unit 20 is not subjected to image processing. The correlation value between the image and the original image may be calculated. Alternatively, as in the configuration shown in FIG. 8, only the difference image image processing unit 31 may be provided to calculate the correlation value between the image processed difference image and the original image not subjected to image processing.

  The present invention detects a gray level difference between corresponding portions of two images, compares the detected gray level difference with a threshold value, and determines that the defect is a defect when the gray level difference is larger than the threshold value. It can be used for an image defect inspection system and an image defect inspection method.

It is a block diagram of the conventional external appearance inspection apparatus for semiconductor circuits. It is a figure which shows the arrangement | sequence of the die | dye on a semiconductor wafer. 1 is a block diagram of an appearance inspection apparatus according to a first embodiment of an image defect inspection apparatus according to the present invention. (A) is an inspection image without color unevenness, (B) is a reference image, and (C) is a difference image between the image shown in (A) and the image shown in (B). (A) is an inspection image having color unevenness, (B) is a reference image, and (C) is a difference image between the image shown in (A) and the image shown in (B). (A) is an inspection image not including a defect, (B) is a reference image, and (C) is a difference image between the image shown in (A) and the image shown in (B). (A) is an inspection image including a defect, (B) is a reference image, and (C) is a difference image between the image shown in (A) and the image shown in (B). It is a block diagram of the external appearance inspection apparatus which concerns on 2nd Example of the image defect inspection apparatus by this invention. It is a block diagram of the external appearance inspection apparatus which concerns on 3rd Example of the image defect inspection apparatus by this invention. 4A is an image obtained by performing edge processing on the image shown in FIG. 4A, and FIG. 4B is an image obtained by performing edge processing on the image shown in FIG. ) Is an image obtained by performing edge processing on the difference image between the image shown in FIG. 4A and the image shown in FIG. 5A is an image obtained by performing edge processing on the image shown in FIG. 5A, and FIG. 5B is an image obtained by performing edge processing on the image shown in FIG. ) Is an image obtained by performing edge processing on the difference image between the image shown in FIG. 5A and the image shown in FIG. 6A is an image obtained by performing edge processing on the image shown in FIG. 6A, and FIG. 6B is an image obtained by performing edge processing on the image shown in FIG. ) Is an image obtained by performing edge processing on the difference image between the image shown in FIG. 6A and the image shown in FIG. 7A is an image obtained by performing edge processing on the image shown in FIG. 7A, and FIG. 7B is an image obtained by performing edge processing on the image shown in FIG. ) Is an image obtained by performing edge processing on the difference image between the image shown in FIG. 7A and the image shown in FIG. It is a block diagram of the external appearance inspection apparatus which concerns on 4th Example of the image defect inspection apparatus by this invention. 1 is a block diagram of an appearance inspection system according to an embodiment of an image defect inspection system according to the present invention. It is a block diagram of the external appearance inspection apparatus which concerns on 5th Example of the image defect inspection apparatus by this invention. It is a block diagram of the external appearance inspection apparatus which concerns on 6th Example of the image defect inspection apparatus by this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Stage 2 Sample stand 3 Wafer 4 Imaging device 5 Signal storage part 6 Difference detection part 7 Detection threshold value calculation part 8 Defect detection part 11 Difference image storage part 12 1st original image storage part 13 2nd original image storage part 20 Correlation value calculation Part 21 Detection threshold correction part

Claims (19)

In an image defect inspection apparatus that detects a difference between pixel values of corresponding pixels of an inspection image and a reference image, and detects the pixel portion as a defect when the difference exceeds a detection threshold,
A difference image generation unit for generating a difference image between the inspection image and the reference image;
A correlation value calculation unit that calculates a correlation value between at least one of the inspection image and the reference image and the difference image;
An image defect inspection apparatus that suppresses detection of the defect in accordance with an increase in the correlation value.   The image defect inspection apparatus according to claim 1, further comprising a detection threshold value correction unit that corrects the detection threshold value according to the correlation value.   The image defect inspection apparatus according to claim 1, further comprising a defect output availability determination unit that determines whether the detected defect can be output according to the correlation value.   The correlation value calculation unit calculates a correlation coefficient between at least one of the inspection image and the reference image and the difference image, according to any one of claims 1 to 3. Image defect inspection device.   The correlation value calculation unit calculates a correlation coefficient between at least one of the inspection image and the reference image and an image obtained by performing predetermined image processing on the difference image. 4. The image defect inspection apparatus according to any one of items 3.   6. The image defect inspection according to claim 5, wherein the correlation value calculation unit calculates a correlation coefficient between at least one of the inspection image and the reference image and the binarized difference image. apparatus.   The correlation value calculation unit calculates a correlation coefficient between an image obtained by performing predetermined image processing on at least one of the inspection image and the reference image, and an image obtained by performing the predetermined image processing on the difference image. The image defect inspection apparatus according to any one of claims 1 to 3, wherein   The correlation value calculation unit calculates a correlation coefficient between at least one of the differential image of the inspection image and the differential image of the reference image and the differential image of the difference image. The image defect inspection apparatus described. In an image defect inspection apparatus that detects a difference between pixel values of corresponding pixels of an inspection image and a reference image, and detects the pixel portion as a defect when the difference exceeds a detection threshold,
A difference image generation unit for generating a difference image between the inspection image and the reference image;
A correlation value calculation unit that calculates a correlation value between at least one of the inspection image and the reference image and the difference image;
An image defect inspection apparatus that outputs the correlation value together with the detected defect. An image defect inspection apparatus that detects a pixel value difference between corresponding pixels of an inspection image and a reference image, and detects the pixel portion as a defect when the difference exceeds a detection threshold, the inspection image and the reference image A difference image generation unit that generates a difference image from a reference image, and a correlation value calculation unit that calculates a correlation value between at least one of the inspection image and the reference image and the difference image are detected. An image defect inspection device that outputs the correlation value together with the defect, and an automatic defect classification device that classifies the output defect based on the correlation value output by the image defect inspection device;
An image defect inspection system comprising: In the image defect inspection method for detecting a pixel value difference between corresponding pixels of the inspection image and the reference image and detecting the pixel portion as a defect when the difference exceeds a detection threshold,
Generating a difference image between the inspection image and the reference image;
Calculating a correlation value between at least one of the inspection image and the reference image and the difference image;
An image defect inspection method, wherein detection of the defect is suppressed according to an increase in the correlation value.   The image defect inspection method according to claim 11, wherein the detection threshold value is corrected according to the correlation value.   The image defect inspection method according to claim 11, wherein whether or not the detected defect can be output is determined according to the correlation value.   The image defect according to claim 11, wherein a correlation coefficient between at least one of the inspection image and the reference image and the difference image is calculated as the correlation value. Inspection method.   14. The correlation coefficient between at least one of the inspection image and the reference image and an image obtained by performing predetermined image processing on the difference image is calculated as the correlation value. The image defect inspection method according to any one of the above.   The image defect inspection method according to claim 15, wherein a correlation coefficient between at least one of the inspection image and the reference image and the binarized difference image is calculated as the correlation value.   Calculating, as the correlation value, a correlation coefficient between an image obtained by performing predetermined image processing on at least one of the inspection image and the reference image and an image obtained by performing the predetermined image processing on the difference image The image defect inspection method according to any one of claims 11 to 13.   The correlation coefficient between at least one of the differential image of the inspection image and the differential image of the reference image and the differential image of the difference image is calculated as the correlation value. Image defect inspection method. In the image defect inspection method for detecting a pixel value difference between corresponding pixels of the inspection image and the reference image and detecting the pixel portion as a defect when the difference exceeds a detection threshold,
Generating a difference image between the inspection image and the reference image;
Calculating a correlation value between at least one of the inspection image and the reference image and the difference image;
Classifying the detected defects based on the correlation values;
An image defect inspection method characterized by the above.