CN114565585A - Image detection method - Google Patents

Abstract

The application provides an image detection method, wherein the method comprises the following steps: acquiring an alignment mark image of a semiconductor mask workpiece; extracting at least one partial image from the alignment mark image; for each local image, determining a local image detection value of the local image; determining a global image detection value of the alignment mark image; determining an image quality of the alignment mark image based on at least one local image detection value and the global image detection value. The method and the device have the advantages that the overall quality of the alignment mark image is evaluated, the local image quality of the alignment mark image is evaluated, and the effect of accurately judging the image quality of the alignment mark is achieved.

Description

an image detection method

technical field

The present application relates to the technical field of image recognition, and in particular, to an image detection method.

Background technique

In the manufacturing process of semiconductor process equipment, the image on the mask workpiece needs to be etched onto the wafer workpiece. The off-axis alignment system can be used to align the alignment marks on the mask workpiece and the wafer workpiece to determine the mask workpiece and wafer workpiece. Whether the round workpiece is aligned. When collecting an alignment mark image, the alignment mark image is affected by many factors such as the illumination range of the light source, illumination wavelength, illumination intensity, illumination angle, etc., which may cause the loss, blur, noise or distortion of the mark texture information. At the same time, in the process of signal acquisition, compression, transmission, processing and reconstruction, the alignment mark image itself is accompanied by image quality degradation and distortion. The aforementioned disadvantages have a severe impact on the detection, identification, and positioning of off-axis alignment systems through alignment marks in alignment mark images.

At present, image quality evaluation mainly includes subjective evaluation and objective evaluation. Subjective evaluation requires manual participation, scoring and statistics on all the collected images, which is time-consuming and labor-intensive. At the same time, due to the sensory differences of each person in artificial, the stability of subjective evaluation is insufficient.

Objective evaluation is divided into two methods: with reference and without reference. Since most images do not have a fixed reference scene, a reference-free method is generally used. The traditional image quality evaluation is mainly aimed at evaluating the quality of the overall image area, and does not judge the quality of the parts of interest in the image. It often occurs that although the overall image quality of the alignment mark image meets the standard, the off-axis alignment system still cannot judge whether the semiconductor mask workpiece is aligned due to insufficient image quality of the alignment mark portion.

SUMMARY OF THE INVENTION

In view of this, the object of the present application is to provide an image detection method, which can solve the The problem in the prior art that the alignment mark image conforms to the detection standard, but the image quality of the alignment mark part is not high, achieves the effect of accurately judging the image quality of the alignment mark.

In a first aspect, an embodiment of the present application provides an image detection method, the method includes: acquiring an alignment mark image of a semiconductor mask workpiece, wherein the alignment mark image includes at least one alignment mark, the at least one alignment mark An alignment mark is a mark pre-set on the semiconductor mask workpiece for aligning the semiconductor mask workpiece; at least one partial image is extracted from the alignment mark image, wherein each partial image The image contains a corresponding alignment mark; for each partial image, a partial image detection value of the partial image is determined; a global image detection value of the alignment mark image is determined; according to at least one partial image detection value and the global image detection value The image detection value determines the image quality of the alignment mark image.

Optionally, the step of extracting at least one partial image from the alignment mark image comprises: determining an image position of each alignment mark in the alignment mark image; A detection frame corresponding to the alignment mark is included in the detection frame; for each alignment mark, the image in the detection frame corresponding to the alignment mark is determined as a partial image.

Optionally, the partial image detection value of each partial image is determined by: determining the edge line and edge point of the alignment mark in the partial image; determining the partial image according to the edge line and the edge point the alignment mark area corresponding to the alignment mark in The local image detection value of the image.

Optionally, each partial image detection value includes a partial image contrast value, wherein the partial image contrast value of each partial image is determined by the following steps: determining a first average gray value of the alignment mark area; determining the the second average gray value of the blank area; the ratio of the first average gray value of the alignment mark area in the partial image to the second average gray value of the blank area in the partial image is determined as the The local image contrast value of the local image.

Optionally, each partial image detection value further includes a partial image sharpness value, wherein the partial image sharpness value of each partial image is determined by the following steps: The gray value and the gray value of the reference pixel points adjacent to each pixel point determine the local image sharpness value of the local image.

Optionally, the first average gray value of the alignment mark area is determined by the following formula:

Wherein, GrayMeanValue is the first average gray value of the image in the alignment mark area of the partial image, f(x, y) is the gray value of each pixel of the image in the alignment mark area of the partial image, Area _mean is the area of the alignment mark region of the partial image.

Optionally, the second average gray value of the blank area is determined by the following formula:

Among them, GrayValue _outer is the second average gray value of the blank area of the partial image, f(x, y) _outer is the gray value of each pixel in the blank area of the partial image, and Area _outer is the The area of the blank area.

Optionally, the reference pixel points adjacent to each pixel point in the blank area include a first reference pixel point and a second reference pixel point, and the first reference pixel point is the same as the horizontal coordinate of the corresponding pixel point. Pixels whose coordinates are adjacent; the second reference pixel is a pixel whose ordinate is the same as that of the corresponding pixel, and whose abscissa is adjacent;

Among them, the local image sharpness value of each local image is determined by the following formula:

Among them, DR is the local image sharpness value of the local image, f(x _i , y _i ) _outer is the gray value of each target pixel, and f(x _i +1, y _i ) _outer is the same as each target pixel. The gray value of the first reference pixel adjacent to the pixel, f(x _i , y _i +1) _outer is the gray value of the second reference pixel adjacent to each target pixel, m is the local The number of pixels in the blank area of the image.

Optionally, the step of determining the global image detection value of the alignment mark image includes: determining an area of an effective grayscale image in the alignment mark image that is greater than the preset grayscale level; The ratio of the area of the image to the area of the global image determines the reference gray value of the alignment mark image; according to the area of the effective gray image and the reference gray value, the global image detection is determined value.

Optionally, the global image detection value includes a first global image ratio, wherein the first global image ratio of the alignment mark image is determined by the following steps: selecting presets at different positions in the alignment mark image. A number of target rectangular areas of preset size, the target rectangular area does not include the alignment mark area; for each target rectangular area, according to the gray value of each pixel in the target rectangular area and the reference gray level value to determine the deviation from the reference gray value of the target rectangular area; for each target rectangular area, according to the ratio of the deviation from the reference gray value to the area of the target rectangular area, determine the average deviation of the target rectangular area from the reference gray value value; according to the reference gray value, the deviation from the reference gray value, the area of each gray level pixel and the area of the global image, determine the average deviation of the alignment mark image; according to the deviation from the reference gray The ratio of the degree mean to the mean deviation determines a first global image ratio of the global image.

Optionally, the global image detection value further includes a second global image ratio, wherein the second global image ratio of the alignment mark image is determined by the following steps: according to an effective grayscale image in the alignment mark image The ratio of the area of the alignment mark image to the area of the alignment mark image determines a second global image ratio of the alignment mark image.

Optionally, the area of the effective grayscale image in the alignment mark image is determined by the following formula:

Among them, SumArea ₂ is the area of the effective grayscale image, Area _i is the area of the ith pixel, and n is the number of pixels in the global image that are greater than the preset gray level.

Optionally, the deviation from the reference gray value of the target rectangular area is determined by the following formula:

Among them, SumGrayOffset is the deviation from the reference gray value, y _i is the gray value of the ith pixel, GrayBaseValue is the reference gray value, w is the number of target rectangular areas, and a is the first side of each target rectangular area length, b is the length of the second side of each target rectangular area.

Optionally, the average deviation of the alignment mark images is determined by the following formula:

Wherein, Sig is the average deviation, GrayBaseValue is the reference gray value, L is the average deviation from the reference gray level, Hist(j) is the area of all the pixels whose gray level is j in the global image, and w is the target The number of rectangular areas, a is the length of the first side of each target rectangular area, and b is the length of the second side of each target rectangular area.

Optionally, the first global image ratio of the global image is determined by the following formula:

Wherein, LR is the first global image ratio, L is the average deviation from the reference grayscale, and sig is the average deviation.

Optionally, the second global image ratio of the alignment mark image is determined by the following formula:

Wherein, AR is the ratio of the second image, SumArea ₂ is the area of the effective grayscale image, and SumArea ₁ is the area of the alignment mark image.

Optionally, the local image detection value includes a contrast value and a sharpness value, and the global image detection value includes a first global image ratio and a second global image ratio, wherein according to the local image detection value and the global image image detection value, the step of determining the image quality of the global image includes: calculating the average value of a plurality of local image detection values to obtain the local image detection average value, wherein the local image detection average value includes: a contrast average value and a clear Determine whether the average contrast value is greater than the standard contrast value, whether the average sharpness value is greater than the standard sharpness value, whether the first global image ratio is greater than the first global image standard value, the second global image ratio Whether the image ratio is greater than the second global image standard value; if the contrast average value is greater than the standard contrast value, the sharpness average value is greater than the standard sharpness value, and the first global image ratio is greater than the first global image standard value, so If the ratio of the second global image is greater than the standard value of the second global image, the global image is determined to be a high-quality image; if the average contrast value is not greater than the standard contrast value and/or the average value of the sharpness is not greater than the standard sharpness If the value and/or the first global image ratio is not greater than the first global image standard value and/or the second global image ratio is not greater than the second global image standard value, it is determined that the global image is a low-quality image.

In a second aspect, an embodiment of the present application further provides an image detection device, the device comprising:

An image acquisition module for acquiring an alignment mark image of a semiconductor mask workpiece, wherein the alignment mark image includes at least one alignment mark, and the at least one alignment mark is preset on the semiconductor mask workpiece the marks used to align the semiconductor mask workpiece;

a partial image extraction module, configured to extract at least one partial image from the alignment mark image, wherein each partial image includes a corresponding alignment mark;

The local image detection value calculation module is used to determine the local image detection value of the local image for each local image;

a global image detection value calculation module for determining the global image detection value of the alignment mark image;

An image quality determination module, configured to determine the image quality of the alignment mark image according to at least one local image detection value and the global image detection value.

In a third aspect, embodiments of the present application further provide an electronic device, including: a processor, a memory, and a bus, where the memory stores machine-readable instructions executable by the processor, and when the electronic device runs, the processing The processor and the memory communicate through a bus, and the machine-readable instructions execute the steps of the image detection method as described above when executed by the processor.

In a fourth aspect, embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is run by a processor, the steps of the above-mentioned image detection method are executed.

The image detection method provided by the embodiments of the present application can solve the problem of existing problems in the prior art by not only performing quality evaluation on the global image of the alignment mark, but also performing image quality evaluation on the part of the alignment mark in the alignment mark image. The image of the alignment mark conforms to the detection standard, but the image quality of the alignment mark part is not high, so as to achieve the effect of accurately judging the image quality of the alignment mark.

In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the preferred embodiments are exemplified below, and are described in detail as follows in conjunction with the accompanying drawings.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present application more clearly, the following drawings will briefly introduce the drawings that need to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore do not It should be regarded as a limitation of the scope, and for those of ordinary skill in the art, other related drawings can also be obtained according to these drawings without any creative effort.

1 is a flowchart of an image detection method provided by an embodiment of the present application;

2 is a schematic structural diagram of an off-axis alignment system provided by an embodiment of the present application;

3 is a schematic diagram of a semiconductor mask workpiece provided by an embodiment of the present application;

FIG. 4 is a schematic diagram of a partial image provided by an embodiment of the present application.

Detailed ways

In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only It is a part of the embodiments of the present application, but not all of the embodiments. The components of the embodiments of the present application generally described and illustrated in the drawings herein may be arranged and designed in a variety of different configurations. Thus, the following detailed description of the embodiments of the application provided in the accompanying drawings is not intended to limit the scope of the application as claimed, but is merely representative of selected embodiments of the application. Based on the embodiments of the present application, every other embodiment obtained by those skilled in the art without creative work falls within the protection scope of the present application.

First, the applicable application scenarios of this application are introduced. The present application can be applied to image detection.

After research, it is found that the traditional image quality evaluation is mainly aimed at the evaluation of the quality of the overall image area, and does not judge the quality of the part of interest in the image. It often occurs that although the overall image quality of the alignment mark image meets the standard, the off-axis alignment system still cannot judge whether the semiconductor mask workpiece is aligned due to insufficient image quality of the alignment mark portion.

Based on this, the embodiments of the present application provide an image detection method to perform detection based on the quality of the alignment mark image.

Please refer to FIG. 1 , which is a flowchart of an image detection method provided by an embodiment of the present application. As shown in FIG. 1 , the image detection method provided by the embodiment of the present application includes:

S101. Acquire an alignment mark image of the semiconductor mask workpiece.

Wherein, the alignment mark image includes at least one alignment mark, and the at least one alignment mark is a mark preset on the semiconductor mask workpiece and used for aligning the semiconductor mask workpiece.

It should be noted that the alignment mark image can be captured by an off-axis alignment system. As shown in FIG. 2 , the off-axis alignment system 200 includes: a semiconductor mask workpiece to be detected 201 , a first imaging lens 202 , and a reflecting prism 203 , a second imaging lens 204 , a first dichroic prism 205 , a light source 206 , a third imaging lens 207 , a second dichroic prism 208 , a fourth imaging lens 209 , and a camera 210 .

Here, the light source 206 illuminates the semiconductor mask workpiece. The light of the light source 206 is reflected by the first beam splitting prism 205 , the second imaging lens 204 and the reflecting prism 203 , and the first imaging lens 202 provides illumination for the semiconductor mask workpiece. The camera 210 captures the pair of semiconductor mask workpieces by detecting the first imaging lens 202, the reflecting prism 203, the second imaging lens 204, the first dichroic prism 205, the third imaging lens 207, the second dichroic prism 208 and the fourth imaging lens 209. Standard markup image.

The camera may use a CCD (Charge-coupled Device, charge coupled device) camera or a CMOS (Complementary Metal Oxide Semiconductor, complementary metal oxide semiconductor) camera.

Optionally, after the alignment mark image is acquired, down-sampling and filtering processing may be performed on the alignment mark image. The brightness value of the alignment mark image is optimized, so that the brightness of the particularly bright part of the alignment mark image is reduced, and the brightness of the particularly dark place is increased. In this way, an image-processed alignment mark image can be obtained.

Exemplarily, as shown in FIG. 3 , the semiconductor mask workpiece is provided with at least one alignment mark 303 for aligning the semiconductor mask workpiece.

As shown in FIG. 3 , in the alignment mark image, areas 301 and 302 outside the mark plate may also be photographed, and it is necessary to delete the mark plate area from the alignment mark to avoid the influence of the area outside the mark plate on the alignment. Calculation of labeled images.

S102. Extract at least one partial image from the alignment mark image.

Among them, each partial image contains a corresponding alignment mark.

Wherein, the step of extracting at least one partial image from the alignment mark image includes: determining the image position of each alignment mark in the alignment mark image; for each alignment mark, generating an alignment mark corresponding to the alignment mark The corresponding detection frame contains the alignment mark; for each alignment mark, the image in the detection frame corresponding to the alignment mark is determined as a partial image.

Here, a partial image can be extracted from the alignment mark image by image segmentation technology. Before extracting the partial image, the detection frame needs to be morphologically expanded to determine that the partial image contains complete alignment marks.

S103. For each partial image, determine the partial image detection value of the partial image.

Here, the partial image detection value of each partial image can be determined by: determining the edge line and edge point of the alignment mark in the partial image; the alignment mark area corresponding to the alignment mark; determine the area outside the alignment mark area in the partial image as a blank area; determine the partial image according to the gray value of the alignment mark area and the gray value of the blank area The local image detection value of .

Specifically, as shown in FIG. 4 , the alignment mark 303 area in the detection frame 401 is the alignment mark area, and the blank area between the detection frame 401 and the alignment mark 303 is the above blank area.

Wherein, the local image detection value includes: local image contrast value and local image sharpness value.

Wherein, the local image contrast value of each partial image is determined by the following steps: determining the first average gray value of the alignment mark area; determining the second average gray value of the blank area; The ratio of the first average gray value of the alignment mark area to the second average gray value of the blank area in the partial image is determined as the partial image contrast value of the partial image.

The local image sharpness value of each local image can be determined by the following steps: according to the gray value of each pixel point in the blank area in the local image and the gray value of the reference pixel point adjacent to each pixel point, The partial image sharpness value of the partial image is determined.

Specifically, the first average gray value of the alignment mark area is determined by the following formula:

Wherein, GrayMeanValue is the first average gray value of the image in the alignment mark area of the partial image, f(x, y) is the gray value of each pixel of the image in the alignment mark area of the partial image, Area _mean is the area of the alignment mark region of the partial image.

Specifically, the second average gray value of the blank area is determined by the following formula:

Among them, GrayValue _outer is the second average gray value of the blank area of the partial image, f(x, y) _outer is the gray value of each pixel in the blank area of the partial image, and Area _outer is the The area of the blank area.

The reference pixel points adjacent to each pixel point in the blank area include a first reference pixel point and a second reference pixel point, and the first reference pixel point is the same as the abscissa and the ordinate of the corresponding pixel. The second reference pixel is a pixel whose ordinate is the same as that of the corresponding pixel, and whose abscissa is adjacent.

The local image sharpness value of each local image can be determined by the following formula:

Among them, DR is the local image sharpness value of the local image, f(x _i , y _i ) _outer is the gray value of each target pixel, and f(x _i +1, y _i ) _outer is the same as each target pixel. The gray value of the first reference pixel adjacent to the pixel, f(x _i , y _i +1) _outer is the gray value of the second reference pixel adjacent to each target pixel, m is the local The number of pixels in the blank area of the image.

S104. Determine the global image detection value of the alignment mark image.

Specifically, the step of determining the global image detection value of the alignment mark image includes: determining an area of an effective grayscale image in the alignment mark image that is greater than the preset grayscale level; according to the effective grayscale image The ratio of the area of the effective grayscale image to the area of the global image determines the reference gray value of the alignment mark image; according to the area of the effective grayscale image and the reference gray value, the global image detection value is determined .

Wherein, the global image detection value includes a first global image ratio and a second global image ratio.

Here, the gray level of each pixel is divided into 1 to 255 gray levels. Exemplarily, the preset gray level can be 200 to 255 gray levels. In this way, all gray levels in the alignment mark image can be determined. The pixel points with the gray level from 200 to 255, and the image composed of the above-mentioned pixel points with the gray level from 200 to 255 is determined as an effective grayscale image.

Wherein, the first global image ratio of the alignment mark image is determined by the following steps: selecting a preset number of target rectangular areas with preset sizes in different positions in the alignment mark image, and no target rectangular area in the target rectangular area Including the alignment mark area; for each target rectangular area, according to the gray value of each pixel in the target rectangular area and the reference gray value, determine the deviation reference gray value of the target rectangular area; for each target rectangular area For the target rectangular area, according to the ratio of the deviation from the reference gray value to the area of the target rectangular area, determine the average value of the deviation from the reference grayscale of the target rectangular area; The average deviation of the alignment mark image is determined by the area of pixels of each gray level and the area of the global image; The first image ratio.

Exemplarily, the alignment mark image may be divided into 9 target rectangular acquisition areas, and the target rectangular areas are acquired in the 9 target rectangular acquisition areas respectively to obtain 9 target rectangular areas.

In this way, the target rectangular area can be collected in the alignment mark image on average, so that the data of the first image ratio is more accurate and credible.

Wherein, the area of the effective grayscale image in the alignment mark image can be determined by the following formula:

Among them, SumArea ₂ is the area of the effective grayscale image, Area _i is the area of the ith pixel, and n is the number of pixels in the global image that are greater than the preset gray level.

The deviation from the reference gray value of the target rectangular area can be determined by the following formula:

Among them, SumGrayOffset is the deviation from the reference gray value, y _i is the gray value of the ith pixel, GrayBaseValue is the reference gray value, w is the number of target rectangular areas, and a is the first side of each target rectangular area length, b is the length of the second side of each target rectangular area.

The average deviation of the alignment mark images can be determined by the following formula:

Wherein, Sig is the average deviation, GrayBaseValue is the reference gray value, L is the average deviation from the reference gray level, Hist(j) is the area of all the pixels whose gray level is j in the global image, and w is the target The number of rectangular areas, a is the length of the first side of each target rectangular area, and b is the length of the second side of each target rectangular area.

The first image ratio of the global image can be determined by the following formula:

Wherein, LR is the first image ratio, L is the average deviation from the reference grayscale, and sig is the average deviation.

Wherein, the second global image ratio of the alignment mark image may be determined by the following steps: determining the The second global image ratio of the alignment marker image.

Wherein, the second global image ratio of the alignment mark image is determined by the following formula:

Wherein, AR is the second global image ratio, SumArea ₂ is the area of the effective grayscale image, and SumArea ₁ is the area of the alignment mark image.

S105. Determine the image quality of the alignment mark image according to at least one local image detection value and the global image detection value.

Wherein, the step of determining the image quality of the global image according to the local image detection value and the global image detection value includes:

calculating the average value of a plurality of local image detection values to obtain the local image detection average value, wherein the local image detection average value includes: a contrast average value and a sharpness average value;

Determine whether the average contrast value is greater than the standard contrast value, whether the average sharpness value is greater than the standard sharpness value, whether the first global image ratio is greater than the first global image standard value, and whether the second global image ratio is greater than the second global image standard value;

If the contrast average value is greater than the standard contrast value, the sharpness average value is greater than the standard sharpness value, the first global image ratio is greater than the first global image standard value, and the second global image ratio is greater than the second global image standard value, then it is determined that the global image is a high-quality image;

If the contrast average value is not greater than the standard contrast value and/or the sharpness average value is not greater than the standard sharpness value and/or the first global image ratio value is not greater than the first global image standard value and/or the first global image standard value If the ratio of the two global images is not greater than the standard value of the second global image, it is determined that the global image is a low-quality image.

Optionally, if the alignment mark image is a low-quality image, the low-quality image may be deleted or the alignment mark image may be re-acquired and detected again.

The image detection method provided by the embodiments of the present application can solve the problem of existing problems in the prior art by not only performing quality evaluation on the global image of the alignment mark, but also performing image quality evaluation on the part of the alignment mark in the alignment mark image. The image of the alignment mark conforms to the detection standard, but the image quality of the alignment mark part is not high, so as to achieve the effect of accurately judging the image quality of the alignment mark.

Based on the same inventive concept, the embodiment of the present application also provides an image detection device corresponding to the image detection method. Reference may be made to the implementation of the method, and repeated descriptions will not be repeated.

Specifically, the image detection device includes: an image acquisition module for acquiring an alignment mark image of the semiconductor mask workpiece, wherein the alignment mark image includes at least one alignment mark, and the at least one alignment mark is preset markings on the semiconductor mask workpiece for aligning the semiconductor mask workpiece;

a partial image extraction module, configured to extract at least one partial image from the alignment mark image, wherein each partial image includes a corresponding alignment mark;

The local image detection value calculation module is used to determine the local image detection value of the local image for each local image;

a global image detection value calculation module for determining the global image detection value of the alignment mark image;

An image quality determination module, configured to determine the image quality of the alignment mark image according to at least one local image detection value and the global image detection value.

The image detection device provided by the embodiment of the present application can solve the problem of existing problems in the prior art by performing both the global quality assessment of the alignment mark image and the image quality assessment of the local alignment mark of the alignment mark image. The image of the alignment mark conforms to the detection standard, but the image quality of the alignment mark part is not high, so as to achieve the effect of accurately judging the image quality of the alignment mark.

An electronic device provided by an embodiment of the present application. The electronic device includes a processor, memory, and a bus.

The memory stores machine-readable instructions executable by the processor, when the electronic device is running, the processor and the memory communicate through a bus, and when the machine-readable instructions are executed by the processor , the steps of the image detection method in the method embodiment shown in FIG. 1 may be performed, and the specific implementation can refer to the method embodiment, which will not be repeated here.

Embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is run by a processor, the image detection method in the method embodiment shown in FIG. 1 can be executed. For the specific implementation, please refer to the method embodiment, which will not be repeated here.

Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

In the several embodiments provided in this application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. The apparatus embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces, indirect coupling or communication connection of devices or units, which may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a processor-executable non-volatile computer-readable storage medium. Based on such understanding, the technical solution of the present application can be embodied in the form of a software product in essence, or the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, removable hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and other media that can store program codes.

Finally, it should be noted that the above-mentioned embodiments are only specific implementations of the present application, and are used to illustrate the technical solutions of the present application, rather than limit them. The embodiments describe the application in detail, and those of ordinary skill in the art should understand that: any person skilled in the art can still modify the technical solutions described in the foregoing embodiments within the technical scope disclosed in the application. Or can easily think of changes, or equivalently replace some of the technical features; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions in the embodiments of the application, and should be covered in this application. within the scope of protection. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (17)

1. An image detection method, characterized in that the method comprises:

acquiring an alignment mark image of a semiconductor mask workpiece, wherein the alignment mark image comprises at least one alignment mark, and the at least one alignment mark is a mark which is arranged on the semiconductor mask workpiece in advance and is used for aligning the semiconductor mask workpiece;

extracting at least one partial image from the alignment mark images, wherein each partial image comprises a corresponding alignment mark;

for each local image, determining a local image detection value of the local image;

determining a global image detection value of the alignment mark image;

determining an image quality of the alignment mark image based on at least one local image detection value and the global image detection value.

2. The method of claim 1, wherein the step of extracting at least one partial image from the alignment mark image comprises:

determining an image position of each alignment mark in the alignment mark image;

generating a detection frame corresponding to each alignment mark, wherein the detection frame comprises the alignment mark;

for each alignment mark, determining the image in the detection frame corresponding to the alignment mark as a local image.

3. The method of claim 1, wherein the local image detection values for each local image are determined by:

determining edge lines and edge points of the alignment marks in the local image;

determining an alignment mark area corresponding to an alignment mark in the local image according to the edge line and the edge point;

determining a region other than the alignment mark region in the partial image as a blank region;

and determining a local image detection value of the local image according to the gray value of the alignment mark area and the gray value of the blank area.

4. The method of claim 3, wherein each local image detection value comprises a local image contrast value,

wherein the local image contrast value for each local image is determined by:

determining a first average gray value of the alignment mark region;

determining a second average gray value of the blank area;

and determining the ratio of the first average gray value of the alignment mark area in the local image to the second average gray value of the blank area in the local image as the local image contrast value of the local image.

5. The method of claim 3, wherein each local image detection value further comprises a local image sharpness value,

wherein the local image sharpness value of each local image is determined by:

and determining the local image definition value of the local image according to the gray value of each pixel point in the blank area in the local image and the gray value of the reference pixel point adjacent to each pixel point.

6. The method of claim 3, wherein the first average gray value of the alignment mark region is determined by the following formula:

wherein, GrayMeanValue is the first average gray value of the image of the alignment mark Area of the local image, f (x, y) is the gray value of each pixel point of the image of the alignment mark Area of the local image, Area_meanIs the area of the alignment mark region of the partial image.

7. The method of claim 3, wherein the second average gray value of the blank region is determined by the following formula:

wherein, GrayValue_outerA second average gray value of a blank region of the partial image, f (x, y)_outerIs the gray value, Area, of each pixel point in the blank Area of the local image_outerIs the area of the blank area of the partial image.

8. The method of claim 3, wherein the reference pixels adjacent to each pixel in the blank area comprise a first reference pixel and a second reference pixel, the first reference pixel being a pixel adjacent to the same abscissa as the corresponding pixel and having the same ordinate;

the second reference pixel point is a pixel point which is the same as the corresponding pixel point in vertical coordinate and adjacent to the horizontal coordinate;

wherein the local image sharpness value of each local image is determined by the following formula:

wherein DR is a local image definition value of the local image, f (x)_i，y_i)_outerIs the gray value of each target pixel point, f (x)_i+1，y_i)_outerIs the gray value of the first reference pixel adjacent to each target pixel, f (x)_i，y_i+1)_outerAnd m is the number of pixel points in the blank area of the local image, wherein m is the gray value of a second reference pixel point adjacent to each target pixel point.

9. The method of claim 1, wherein determining a global image detection value for the alignment mark image comprises:

determining the area of an effective gray level image larger than a preset gray level in the alignment mark image;

determining a reference gray value of the alignment mark image according to the ratio of the area of the effective gray image to the area of the global image;

and determining the global image detection value according to the area of the effective gray image and the reference gray value.

10. The method of claim 9, wherein the global image detection value comprises a first global image ratio value,

wherein a first global image ratio of the alignment mark image is determined by:

selecting a preset number of target rectangular areas with preset sizes at different positions in the alignment mark image, wherein the target rectangular areas do not comprise alignment mark areas;

for each target rectangular region, determining a deviation reference gray value of the target rectangular region according to the gray value of each pixel point in the target rectangular region and the reference gray value;

for each target rectangular region, determining an average value of the deviation reference gray scale of the target rectangular region according to the ratio of the deviation reference gray scale value to the area of the target rectangular region;

determining the average deviation of the alignment mark image according to the reference gray value, the deviation reference gray value, the area of the pixel of each gray level and the area of the global image;

and determining a first global image ratio of the global image according to the ratio of the deviation reference gray level average value to the average deviation.

11. The method of claim 9, wherein the global image detection value further comprises a second global image ratio value,

wherein a second global image ratio of the alignment mark image is determined by:

and determining a second global image ratio of the alignment mark image according to the ratio of the area of the effective gray scale image in the alignment mark image to the area of the alignment mark image.

12. The method of claim 9, wherein the area of the effective grayscale image in the alignment mark image is determined by the formula:

wherein, SumArea₂Area, being the Area of the effective gray scale image_iAnd n is the number of pixel points larger than the preset gray level in the global image.

13. The method of claim 10, wherein the deviation from the reference gray value of the target rectangular region is determined by the following formula:

wherein SumGrayOffset is the deviation from the reference gray level value, y_iAnd taking the gray value of the ith pixel point, taking GrayBaseValue as a reference gray value, taking w as the number of the target rectangular regions, taking a as the first side length of each target rectangular region, and taking b as the second side length of each target rectangular region.

14. The method of claim 10, wherein the average deviation of the alignment mark image is determined by the following equation:

wherein Sig is the average deviation, gray base value is a reference gray value, L is a deviation reference gray average value, hist (j) is the area of all pixels with gray level j in the global image, w is the number of the target rectangular regions, a is the first side length of each target rectangular region, and b is the second side length of each target rectangular region.

15. The method of claim 10, wherein the first global image ratio for the global image is determined by the following equation:

wherein LR is the first global image ratio, L is a deviation reference gray level average value, and sig is the average deviation.

16. The method of claim 11, wherein the second global image ratio of the alignment mark image is determined by the following equation:

wherein AR is the second global image ratio, SumArea₂SumArea, the area of the effective gray image₁Is the area of the alignment mark image.

17. The method of claim 1, wherein the local image detection values comprise contrast values and sharpness values, wherein the global image detection values comprise first global image ratios and second global image ratios,

wherein determining the image quality of the global image based on the local image detection value and the global image detection value comprises:

calculating an average value of a plurality of local image detection values to obtain a local image detection average value, wherein the local image detection average value comprises: a contrast average and a sharpness average;

judging whether the contrast average value is greater than a standard contrast value or not, whether the definition average value is greater than a standard definition value or not, whether the first global image ratio is greater than a first global image standard value or not and whether the second global image ratio is greater than a second global image standard value or not;

if the contrast average value is greater than a standard contrast value, the definition average value is greater than a standard definition value, the first global image ratio is greater than a first global image standard value, and the second global image ratio is greater than a second global image standard value, determining that the global image is a high-quality image;

and if the contrast average value is not greater than a standard contrast value and/or the definition average value is not greater than a standard definition value and/or the first global image ratio value is not greater than a first global image standard value and/or the second global image ratio value is not greater than a second global image standard value, determining that the global image is a low-quality image.

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