Mask plate particle detection method, detection device, electronic equipment and storage medium

Abstract

The application discloses a mask plate particle detection method, a detection device, an electronic device and a storage medium. The mask plate particle detection method comprises the following steps: acquiring a plurality of original local images of a mask plate; acquiring images of the mask plate after cleaning to obtain a plurality of local images after cleaning; performing a splicing operation on all the original local images and all the local images after cleaning respectively to obtain an overall original image and an overall cleaning image; acquiring a difference image of the overall original image and the overall cleaning image; and extracting information of mask plate particles from a connected domain of the difference image. The mask plate particle detection method provided by the application has high information detection accuracy of mask plate particles, short detection time, and can fully meet the needs of quickly and accurately obtaining the state of mask plate particles before and after cleaning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of semiconductor technology, and in particular to a mask plate particle detection method, a detection device, an electronic device and a storage medium. BACKGROUND

[0002] In a pattern transfer lithography process, more different masks are needed in the lithography process flow due to the increasing precision of chip pattern of smaller nodes. In order to ensure the yield of chip production, particles on the mask need to be detected and analyzed.

[0003] In the preparation process of a lithography mask, defects such as particles, breakpoints, pinholes and the like will be generated on the mask substrate and patterns due to particle pollution or incomplete chemical reaction in the material preparation process. Particles are a kind of important defects in mask detection. In addition, during the use of the mask, the core mask plate needs to be used multiple times in the process, and particles may also be generated after use due to the process. Therefore, the mask plate needs to be cleaned, and then the increase or decrease of particles needs to be detected to evaluate the influence of the lithography process on the mask plate, and then to determine the use, repair and the like of the subsequent mask plate.

[0004] The detection of mask plate particles is usually performed by devices or systems in the fields of optics and electronics, and the corresponding systems (devices) include optical detection devices and scanning electron microscope devices. The related art mask plate particle detection method has low detection precision and long detection time, and cannot meet the requirements in some cases where the particle state of the mask plate before and after cleaning needs to be quickly and accurately obtained.

[0005] The above statements are only used to provide background technical information related to the present application, and do not necessarily constitute the prior art. SUMMARY

[0006] The purpose of the present application is to provide a mask plate particle detection method, a detection device, an electronic device and a storage medium. In order to have a basic understanding of some aspects of the disclosed embodiments, the following is a simple summary. This part is not a general review, nor does it determine the key / important components or delineate the protection scope of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description below.

[0007] According to one aspect of the embodiments of the present application, a mask plate particle detection method is provided, comprising:

[0008] obtaining a plurality of original local images of the mask plate;

[0009] performing image acquisition on the cleaned mask plate to obtain a plurality of cleaned local images;

[0010] performing a stitching operation on all the original partial images and all the cleaned partial images respectively to obtain an overall original image and an overall cleaned image;

[0011] obtaining a difference image of the overall original image and the overall cleaned image;

[0012] extracting information of the mask plate particles from the connected domain of the difference image.

[0013] In some embodiments of the present application, the stitching operation comprises:

[0014] stitching all the partial images into an overall image in a stitching direction of first from top to bottom and then from left to right by using a block matching method.

[0015] In some embodiments of the present application, the stitching operation comprises:

[0016] for the first partial image and the second partial image which are adjacent and have an overlapping area, selecting an image block in the overlapping area of the first partial image as a matching area;

[0017] sliding matching the matching area in the overlapping area of the second partial image to obtain a matching degree;

[0018] obtaining a relative displacement of the first partial image and the second partial image according to the matching degree;

[0019] stitching the first partial image and the second partial image according to the relative displacement.

[0020] In some embodiments of the present application, the sliding matching the matching area in the overlapping area of the second partial image to obtain a matching degree comprises:

[0021] performing a pixel-by-pixel gray difference calculation on the matching area and a to-be-matched area in the overlapping area of the second partial image to obtain a calculation result;

[0022] if there is a pixel with a gray value difference greater than a preset gray value threshold in the calculation result, skipping the to-be-matched area and turning to perform a pixel-by-pixel gray difference calculation on a next to-be-matched area in the overlapping area of the second partial image and the matching area;

[0023] if there is no pixel with a gray value difference greater than a preset gray value threshold in the calculation result, calculating a standard deviation of the matching area and the to-be-matched area, and if a minimum standard deviation is less than or equal to a preset standard deviation threshold, taking the to-be-matched area corresponding to the minimum standard deviation as a matching result;

[0024] If the found minimum standard deviation is greater than the preset standard deviation threshold, then the preset gray value threshold or the preset standard deviation threshold is increased, and the process shifts to performing pixel-by-pixel gray-level difference calculation on the unmatched region in the overlapping region of the matching region and the second local image.

[0025] In some embodiments of this application, obtaining the difference image between the overall original image and the overall cleaned image includes:

[0026] Align the overall original image and the overall cleaned image;

[0027] A difference image is obtained by performing a difference operation on the aligned original image and the cleaned image.

[0028] In some embodiments of this application, aligning the overall original image and the overall cleaned image includes:

[0029] The motion models that match the overall original image and the overall cleaned image are associated to obtain the associated motion model;

[0030] The correlation coefficient of the associated motion model is estimated to obtain the estimation result;

[0031] Align the overall original image and the overall cleaned image based on the estimation results.

[0032] In some embodiments of this application, aligning the overall original image and the overall cleaned image includes:

[0033] The overall original image and the overall cleaned image are scaled and aligned using a pre-set multi-layer image scaling array.

[0034] According to another aspect of the embodiments of this application, a device for detecting mask particles is provided, comprising:

[0035] The original local image acquisition module is used to acquire multiple original local images of the mask template;

[0036] The local image acquisition module after cleaning is used to acquire images of the cleaned mask and obtain multiple local images after cleaning.

[0037] The stitching module is used to perform stitching operations on all the original local images and all the cleaned local images respectively to obtain the overall original image and the overall cleaned image;

[0038] The difference image acquisition module is used to acquire the difference image between the overall original image and the overall cleaned image;

[0039] A particle information extraction module is configured to extract information of the mask plate particles from the connected domain of the difference image.

[0040] According to another aspect of the embodiments of the present application, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the method for detecting mask plate particles according to any of the embodiments of the present application.

[0041] According to another aspect of the embodiments of the present application, a computer readable storage medium is provided, which stores a computer program executable by a processor to implement the method for detecting mask plate particles according to any of the embodiments of the present application.

[0042] One of the aspects of the embodiments of the present application provides a technical solution which can include the following beneficial effects:

[0043] The method for detecting mask plate particles provided by the embodiments of the present application acquires a plurality of original local images of a mask plate, acquires images of the mask plate after cleaning to obtain a plurality of local images after cleaning, performs splicing operations on all the original local images and all the local images after cleaning respectively to obtain an overall original image and an overall cleaning image, acquires a difference image of the overall original image and the overall cleaning image, extracts information of mask plate particles from the connected domain of the difference image, and has high detection precision for the information of mask plate particles, short detection time, and can fully meet the needs of quickly and accurately acquiring the state of mask plate particles before and after cleaning.

[0044] The above description is only a summary of the technical solutions of the embodiments of the present application, in order to more clearly understand the technical means of the embodiments of the present application, the embodiments of the present application can be implemented according to the content of the description, and in order to make the above and other purposes, features and advantages of the embodiments of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0046] Figure 1 A flow chart of the method for detecting mask plate particles according to an embodiment of the present application is shown.

[0047] Figure 2 A mask plate local image in one example of the present application is shown.

[0048] Figure 3 An overall original image of a mask plate in one example of the present application is shown.

[0049] Figure 4 A flow chart of step S30 in one embodiment of the present application is shown.

[0050] Figure 5 A first partial image in one example of the present application is shown.

[0051] Figure 6 A second partial image in one example of the present application is shown.

[0052] Figure 7 An image in which the first partial image and the second partial image are spliced in one example of the present application is shown.

[0053] Figure 8 An overall original image of a mask plate before cleaning in one example of the present application is shown.

[0054] Figure 9 An overall cleaned image of a mask plate after cleaning in one example of the present application is shown.

[0055] Figure 10 A structure block diagram of a detection device of mask plate particles in one embodiment of the present application is shown.

[0056] Figure 11 A structure block diagram of an electronic device in one embodiment of the present application is shown.

[0057] Figure 12 A schematic diagram of a computer readable storage medium in one embodiment of the present application is shown. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described below in combination with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative efforts fall within the scope of protection of the present application.

[0059] Those skilled in the art can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as generally understood by those skilled in the art in the field of the present application. It should also be understood that terms such as those defined in a general dictionary should be understood as having meanings consistent with those in the context of the prior art, and unless specifically defined as such herein, should not be interpreted in an idealized or overly formal sense.

[0060] The defects of the mask plate particle detection method of the related art include low detection accuracy, long detection time, and inability to meet the requirements in some cases where the particle state of the mask plate before and after cleaning needs to be quickly and accurately obtained. In view of these defects of the related art, an embodiment of the present application provides a mask plate particle detection method to overcome the defects of the related art.

[0061] Reference Figure 1 As shown in the figure, an embodiment of the present application provides a mask plate particle detection method, which can include steps S10 to S50:

[0062] S10, obtaining a plurality of original local images of the mask plate.

[0063] Specifically, the mask plate can be divided into a plurality of local regions, and then corresponding images are collected for each local region of the mask plate, so as to obtain images of all local regions. The local image of the mask plate can be obtained by an optical microscope, and the resolution thereof can be up to 100 nm / pixel. The field of view (size) of the image obtained at high resolution is about 100 um*100 um to 500*500 um. Figure 2 The figure shows a mask plate local image in an example. Adjacent two local images have an overlapping area, i.e., the same area, so that the loss of mask plate information can be avoided. The adjacent two local images can be, for example, two local images adjacent above and below, or two local images adjacent left and right.

[0064] S20, image acquisition is performed on the cleaned mask plate to obtain a plurality of post-cleaning local images.

[0065] During use of the mask plate, particles caused by processes can be increased, so the mask plate needs to be cleaned, and then the increase or decrease of particles in the cleaned mask plate is detected to evaluate the influence of the photolithography process on the mask plate, and then the subsequent operation of continuing to use or repairing the mask plate is determined. For the cleaned mask plate, the cleaned mask plate can be divided into a plurality of local regions, and then corresponding images are collected for each local region of the cleaned mask plate, so as to obtain all post-cleaning local images.

[0066] S30, performing a splicing operation on all the original local images and all the post-cleaning local images respectively to obtain an overall original image and an overall cleaning image.

[0067] The acquired optical microscope images are not completely matched in the repeated areas between each local image due to the accuracy of the displacement table itself and operation, and thus special processing is required when the images are spliced. Similar processing is required for the images before and after cleaning. In order to obtain the entire mask image, each part of the mask needs to be scanned to obtain the original local image of each part, and then the complete image of the mask can be obtained by splicing all the original local images. Figure 3 Fig. 1 shows an example of an overall original image of a mask.

[0068] Specifically, the splicing operation is performed on all original local images to obtain an overall original image, and the splicing operation is performed on all cleaned local images to obtain an overall cleaned image.

[0069] In some embodiments, the splicing operation described above can include overlapping the overlapping areas of two adjacent local images together to merge the two adjacent local images into one.

[0070] For example, the splicing operation described above can include overlapping the overlapping areas of two adjacent local images together to merge the two adjacent local images into one in a sliding matching manner.

[0071] For example, for two local images adjacent above and below, the lower edge part of the upper local image and the upper edge part of the lower local image have an overlapping area. When splicing, the lower edge part of the upper local image and the upper edge part of the lower local image can be made to slide relative to each other, and when the overlapping areas of the two are completely overlapped, the two are spliced together.

[0072] In some embodiments, the splicing operation can include splicing all local images into an overall image in a splicing direction of first from top to bottom and then from left to right in a block matching manner.

[0073] Reference is made to Figure 4 In some embodiments, the splicing operation of splicing all local images into an overall image in a splicing direction of first from top to bottom and then from left to right in a block matching manner can include steps S301 to S304 as shown in the following table:

[0074] S301, for two adjacent first and second local images having an overlapping area, selecting an image block in the overlapping area of the first local image as a matching area.

[0075] The local images corresponding to two adjacent local areas have an overlapping area, for example, the local images corresponding to two local areas adjacent left and right have an overlapping area, and the local images corresponding to two local areas adjacent above and below have an overlapping area.

[0076] S302, slidingly match the matching area in the overlapping area of the second local image to obtain a matching degree.

[0077] S303, obtaining a relative displacement between the first local image and the second local image according to the matching degree.

[0078] Specifically, for example, the relative displacement between the first local image and the second local image can be calculated by selecting the region coordinates whose matching degree reaches a preset matching degree threshold. The matching degree is the highest 100%. The relative displacement can be calculated according to the coordinates set on the first local image and the second local image. As shown in Figure 5 and Figure 6 The upper and lower edges and the left and right edges of the first local image and the second local image are marked with coordinates.

[0079] S304, splicing the first local image and the second local image according to the relative displacement.

[0080] In some embodiments, the slidingly matching the matching area in the overlapping area of the second local image to obtain a matching degree can include:

[0081] S3021, performing a pixel-by-pixel gray difference calculation on the matching area and a to-be-matched region in the overlapping area of the second local image to obtain a calculation result;

[0082] S3022, if there is a pixel with a gray value difference greater than a preset gray value threshold in the calculation result, skipping the to-be-matched region and turning to perform a pixel-by-pixel gray difference calculation on a next to-be-matched region in the overlapping area of the second local image and the matching area;

[0083] S3023, if there is no pixel with a gray value difference greater than a preset gray value threshold in the calculation result, calculating the standard deviation of the matching area and the to-be-matched region, and if the minimum standard deviation is less than or equal to a preset standard deviation threshold, taking the to-be-matched region corresponding to the minimum standard deviation as a matching result;

[0084] S3024, if the found minimum standard deviation is greater than the preset standard deviation threshold, increasing the preset gray value threshold or the preset standard deviation threshold, and turning to perform a pixel-by-pixel gray difference calculation on the to-be-matched region in the overlapping area of the second local image and the matching area.

[0085] Exemplarily, the splicing direction is to splice from top to bottom first, and then from left to right. As shown in Figure 5 and Figure 6As shown, when performing block matching, a block image M in the overlapping area 1 of the first local image is taken as a match area, the match area of the first local image is slidingly matched in a search area in the overlapping area 2 of the second local image, a region with the highest match score is selected, the relative displacement between the first local image and the second local image is calculated according to the region coordinates, and the first local image and the second local image are spliced according to the relative displacement. The image after the first local image and the second local image are spliced is as shown in Figure 7

[0086] Exemplarily, the match score calculation method includes: first, performing pixel-by-pixel gray difference calculation on the match area and the search area, if there is a pixel with a gray value difference greater than a preset gray value threshold (for example, 200, 220, 300, etc.) in the calculation result, then skip this region; if there is no pixel with a gray value difference greater than a preset gray value threshold (for example, 200, 220, 300, etc.) in the calculation result, then calculate the standard deviation of the match area and the search area, and take the search area with the smallest standard deviation as the matching result. If the optimal standard deviation found by the above method is still greater than a preset standard deviation threshold (for example, 1000, 800, 1200, etc.), then it is considered that the matching fails, a more relaxed condition is used to re-search or a preset offset is directly used.

[0087] Performing splicing operation on all original local images to obtain an overall original image before mask plate cleaning. Perform splicing operation on all cleaned local images to obtain an overall cleaned image after mask plate cleaning. As shown in Figure 8 As shown in FIG. 6, an overall original image before mask plate cleaning in an example is Figure 9 As shown in FIG. 7, an overall cleaned image after mask plate cleaning in an example is

[0088] S40, obtaining a difference image of the overall original image and the overall cleaned image.

[0089] In some embodiments, the obtaining of the difference image of the overall original image and the overall cleaned image includes:

[0090] S401, aligning the overall original image and the overall cleaned image;

[0091] S402, performing difference operation on the aligned overall original image and the overall cleaned image to obtain a difference image.

[0092] In some embodiments, the aligning of the overall original image and the overall cleaned image includes:

[0093] ​S4011, associate the motion models matched in the whole original image and the whole cleaning image to obtain associated motion models;

[0094] S4012, estimate the correlation coefficients of the associated motion models to obtain an estimation result;

[0095] S4013, align the whole original image and the whole cleaning image according to the estimation result.

[0096] In some embodiments, the aligning the whole original image and the whole cleaning image comprises: scaling aligning the whole original image and the whole cleaning image by using a pre-set multi-layer image scaling array.

[0097] Exemplarily, an image aligning method based on enhanced correlation coefficient maximization is adopted, the vorticity, position offset, and scaling coefficient in the two pictures are estimated by associating the motion models of the same features and shape contours in the two pictures, an estimation result is obtained, and the whole original image and the whole cleaning image are aligned according to the estimation result. Since the spliced picture is usually large, for example, it may exceed 10000*10000 pixels, directly applying the function will result in a long calculation time, therefore, a pyramid scaling matching mechanism can be added for image alignment. A maximum of five-layer image scaling array is adopted, that is, the picture is first scaled down by five times for alignment, and then enlarged four times for alignment, so that the running speed of image alignment is effectively accelerated by dozens of times.

[0098] On the basis of alignment, the difference image operation is performed on the images before and after cleaning, the same graphic background of the two images is removed, and the remaining is the mask particle information.

[0099] S50, extracting the information of the mask plate particles from the connected domain of the difference image.

[0100] Exemplarily, the connected domain of the mask plate image is detected to obtain a detection result of the mask plate particles. By detecting the connected domain on the mask plate image, the particle contour, position, and other information are extracted, and the detection result of the mask plate particles is obtained.

[0101] The detection method of the embodiment of the application only needs 10-20 minutes to complete detection, the particle detection accuracy rate reaches more than 95%, and the rapid detection of the particles before and after mask cleaning is realized. The method is more rapid and accurate than the optical or electronic detection equipment of related technologies. The method of the embodiment of the application is also applicable to the particle detection before and after the reflection type mask cleaning.

[0102] The mask plate particle detection method of the embodiments of the present application can be practically applied to an analysis transmission mask particle detection system, and is suitable for various optical and electronic mask particle detection devices, and is also suitable for detection of reflective mask particles. The mask plate particle detection method provided in the embodiments of the present application realizes estimation of the positions and sizes of mask plate particles before and after cleaning, and accurately detects the increase or decrease of mask particles before and after cleaning by using image stitching, image alignment and image segmentation processing algorithms, which helps to accurately evaluate the cleaning process and overcome the technical defects in the related art.

[0103] Reference Figure 10 As shown in the drawings, the embodiments of the present application provide a mask plate particle detection device, which comprises:

[0104] An original local image acquisition module is configured to acquire a plurality of original local images of a mask plate.

[0105] A post-cleaning local image acquisition module is configured to acquire images of a mask plate after cleaning to obtain a plurality of post-cleaning local images.

[0106] A stitching module is configured to perform stitching operations on all the original local images and all the post-cleaning local images respectively to obtain an overall original image and an overall post-cleaning image.

[0107] A difference image acquisition module is configured to acquire a difference image of the overall original image and the overall post-cleaning image.

[0108] A particle information extraction module is configured to extract information of mask plate particles from a connected domain of the difference image.

[0109] In some embodiments, the stitching operation can include stitching all the local images into an overall image in a stitching direction of first from top to bottom and then from left to right by using a block matching method.

[0110] In some embodiments, stitching all the local images into an overall image in a stitching direction of first from top to bottom and then from left to right by using a block matching method can include: for adjacent first and second local images with an overlapping area, selecting an image block in the overlapping area of the first local image as a matching area; slidingly matching the matching area in the overlapping area of the second local image to obtain a matching degree; obtaining a relative displacement of the first and second local images according to the matching degree; and stitching the first and second local images according to the relative displacement.

[0111] In some embodiments, the sliding matching of the matching region in the overlapping area of the second local image obtains a matching degree, including: performing pixel-by-pixel gray difference calculation on the matching region and a to-be-matched region in the overlapping area of the second local image to obtain a calculation result; if there is a pixel with a gray value difference greater than a preset gray value threshold in the calculation result, the to-be-matched region is skipped, and pixel-by-pixel gray difference calculation is performed on a next to-be-matched region in the overlapping area of the matching region and the second local image; if there is no pixel with a gray value difference greater than a preset gray value threshold in the calculation result, a standard deviation of the matching region and the to-be-matched region is calculated, and if a minimum standard deviation is less than or equal to a preset standard deviation threshold, the to-be-matched region corresponding to the minimum standard deviation is taken as a matching result; if the minimum standard deviation found is greater than the preset standard deviation threshold, the preset gray value threshold or the preset standard deviation threshold is increased, and the pixel-by-pixel gray difference calculation on the to-be-matched region in the overlapping area of the matching region and the second local image is performed.

[0112] In some embodiments, the difference image obtaining module can include:

[0113] an aligning unit, configured to align the overall original image and the overall cleaning image;

[0114] a difference unit, configured to perform a difference operation on the aligned overall original image and the overall cleaning image to obtain a difference image.

[0115] In some embodiments, the aligning unit can include:

[0116] a correlation subunit, configured to correlate motion models matched in the overall original image and the overall cleaning image to obtain correlated motion models;

[0117] an estimation subunit, configured to estimate correlation coefficients of the correlated motion models to obtain an estimation result;

[0118] an aligning subunit, configured to align the overall original image and the overall cleaning image according to the estimation result.

[0119] In some embodiments, aligning the overall original image and the overall cleaning image can include: performing scaling alignment on the overall original image and the overall cleaning image by using a pre-set multi-layer image scaling array.

[0120] The mask plate particle detection device provided in the embodiments of the present application realizes position and size estimation of particles before and after cleaning of a transmission type mask plate, accurately detects the increase or decrease of mask particles before and after cleaning by using image splicing, image alignment, and image segmentation processing algorithms, and helps to accurately evaluate the cleaning process.

[0121] Another embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for detecting mask plate particles according to any one of the preceding embodiments.

[0122] Reference Figure 11 As shown in the figure, the electronic device 10 can include a processor 100, a memory 101, a bus 102, and a communication interface 103, wherein the processor 100, the communication interface 103, and the memory 101 are connected through the bus 102; the memory 101 stores a computer program executable on the processor 100, and the processor 100 executes the computer program to implement the method provided in any one of the preceding embodiments.

[0123] The memory 101 can include a high-speed random access memory (RAM: Random Access Memory), and can also include a non-volatile memory, such as at least one disk memory. The communication connection between the device network element and at least one other network element is realized through at least one communication interface 103 (which can be wired or wireless), and the Internet, a wide area network, a local network, a metropolitan area network, etc. can be used.

[0124] The bus 102 can be an ISA bus, a PCI bus, an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 101 is used to store a program, and the processor 100 executes the program after receiving an execution instruction. The method disclosed in any one of the preceding embodiments of the present application can be applied to the processor 100 or implemented by the processor 100.

[0125] The processor 100 can be an integrated circuit chip with signal processing capability. In implementation, each step of the above method can be completed by integrated logic circuits or instructions in the form of software in the processor 100. The processor 100 described above can be a general-purpose processor, which can include a central processing unit (CPU), a network processor (NP), etc.; or can be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a ready programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. Each method, step and logic block diagram disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as a hardware code processor for execution, or a combination of hardware and software modules in the code processor for execution. The software module can be located in a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium in the memory 101 is read by the processor 100, and the hardware thereof is combined to complete the steps of the above method.

[0126] The electronic device provided by the embodiments of the present application and the method provided by the embodiments of the present application have the same beneficial effects as the method adopted, run or implemented by them.

[0127] Another embodiment of the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the method for detecting mask plate particles according to any one of the above embodiments. For reference Figure 12 As shown, the computer readable storage medium is a disc 20, and a computer program (i.e. program product) is stored on the disc 20, wherein the computer program is executed by a processor to implement the method provided by any one of the above embodiments.

[0128] It should be noted that examples of the computer readable storage medium can also include, but are not limited to, a phase change memory (PRAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), other types of random access memory (RAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory or other optical, magnetic storage medium, which will not be described one by one here.

[0129] The computer readable storage medium provided in the above embodiments of the present application has the same inventive concept as the method provided in the embodiments of the present application, and has the same beneficial effects as the method adopted, run or implemented by the application stored therein.

[0130] It should be noted that:

[0131] The term "module" is not intended to be limited to a particular physical form. Depending on the specific application, a module can be implemented as hardware, firmware, software, and / or a combination thereof. In addition, different modules can share common components or even be implemented by the same components. There can or can not be a clear boundary between different modules.

[0132] The algorithms and displays presented herein are not inherently related to any particular computer, virtual apparatus, or other apparatus. Various general purpose systems can be used with these examples based upon the description herein. General purpose systems can be constructed to implement the required structure for the apparatuses described herein. In addition, the present application is not intended to be limited to any particular programming language. It will be appreciated that there are many programming languages that can be used to implement the teachings of the present application as described herein, and any such programming language can be used in the context of the present application.

[0133] It should be understood that, although each step in the flowchart of the accompanying drawings is shown in sequence according to the direction of the arrow, these steps are not necessarily executed in sequence according to the direction of the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and they can be executed in other sequences. Moreover, at least part of the steps in the flowchart of the accompanying drawings can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence is not necessarily sequential, but can be alternately or alternately executed with at least part of other steps or sub-steps or stages of other steps.

[0134] The above embodiments only express the implementation of the present application, and the description is more specific and detailed, but it should not be understood as limiting the scope of the patent of the present application. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A method for detecting particles in a photomask, characterized in that, include: Obtain multiple original local images of the mask template; Image acquisition is performed on the cleaned mask to obtain multiple local images after cleaning; Perform a stitching operation on all the original local images and all the cleaned local images respectively to obtain the overall original image and the overall cleaned image; Obtain the difference image between the overall original image and the overall cleaned image; Extract information about the mask particles from the connected components of the difference image; The stitching operation includes: overlapping the overlapping areas of two adjacent local images to merge the two adjacent local images into one; The step of overlapping the overlapping areas of two adjacent local images to merge them into one includes: For adjacent first and second local images that have overlapping areas, an image patch is selected from the overlapping area of ​​the first local image as a matching area. The matching region is slidably matched in the overlapping area of ​​the second local image to obtain the matching degree; The relative displacement of the first local image and the second local image is obtained based on the matching degree; The first local image and the second local image are stitched together according to the relative displacement; The step of sliding the matching region within the overlapping area of ​​the second local image to obtain the matching degree includes: The pixel-by-pixel gray level difference is calculated for the region to be matched in the overlapping area of ​​the matching region and the second local image to obtain the calculation result; If there are pixels in the calculation result whose gray value difference is greater than the preset gray value threshold, then skip the region to be matched and proceed to perform pixel-by-pixel gray value difference calculation on the next region to be matched in the overlapping area of ​​the matching region and the second local image. If there are no pixels in the calculation result whose gray value difference is greater than the preset gray value threshold, then the standard deviation of the matching area and the area to be matched is calculated. If the minimum standard deviation is less than or equal to the preset standard deviation threshold, then the area to be matched corresponding to the minimum standard deviation is taken as the matching result. If the found minimum standard deviation is greater than the preset standard deviation threshold, then the preset gray value threshold or the preset standard deviation threshold is increased, and the process shifts to performing pixel-by-pixel gray-level difference calculation on the unmatched region in the overlapping region of the matching region and the second local image.

2. The method according to claim 1, characterized in that, The step of obtaining the difference image between the overall original image and the overall cleaned image includes: Align the overall original image and the overall cleaned image; A difference image is obtained by performing a difference operation on the aligned original image and the cleaned image.

3. The method according to claim 2, characterized in that, The alignment of the overall original image and the overall cleaned image includes: The motion models that match the overall original image and the overall cleaned image are associated to obtain the associated motion model; The correlation coefficient of the associated motion model is estimated to obtain the estimation result; Align the overall original image and the overall cleaned image based on the estimation results.

4. The method according to claim 2, characterized in that, The alignment of the overall original image and the overall cleaned image includes: The overall original image and the overall cleaned image are scaled and aligned using a pre-set multi-layer image scaling array.

5. A device for detecting particles in a mask template, characterized in that, include: The original local image acquisition module is used to acquire multiple original local images of the mask template; The local image acquisition module after cleaning is used to acquire images of the cleaned mask and obtain multiple local images after cleaning. The stitching module is used to perform stitching operations on all the original local images and all the cleaned local images respectively to obtain the overall original image and the overall cleaned image; The difference image acquisition module is used to acquire the difference image between the overall original image and the overall cleaned image; The particle information extraction module is used to extract information about the mask particles from the connected components of the difference image; The stitching operation includes: overlapping the overlapping areas of two adjacent local images to merge the two adjacent local images into one; The step of overlapping the overlapping areas of two adjacent local images to merge them into one includes: For adjacent first and second local images that have overlapping areas, an image patch is selected from the overlapping area of ​​the first local image as a matching area. The matching region is slidably matched in the overlapping area of ​​the second local image to obtain the matching degree; The relative displacement of the first local image and the second local image is obtained based on the matching degree; The first local image and the second local image are stitched together according to the relative displacement; The step of sliding the matching region within the overlapping area of ​​the second local image to obtain the matching degree includes: The pixel-by-pixel gray level difference is calculated for the region to be matched in the overlapping area of ​​the matching region and the second local image to obtain the calculation result; If there are pixels in the calculation result whose gray value difference is greater than the preset gray value threshold, then skip the region to be matched and proceed to perform pixel-by-pixel gray value difference calculation on the next region to be matched in the overlapping area of ​​the matching region and the second local image. If there are no pixels in the calculation result whose gray value difference is greater than the preset gray value threshold, then the standard deviation of the matching area and the area to be matched is calculated. If the minimum standard deviation is less than or equal to the preset standard deviation threshold, then the area to be matched corresponding to the minimum standard deviation is taken as the matching result. If the found minimum standard deviation is greater than the preset standard deviation threshold, then the preset gray value threshold or the preset standard deviation threshold is increased, and the process shifts to performing pixel-by-pixel gray-level difference calculation on the unmatched region in the overlapping region of the matching region and the second local image.

6. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for detecting mask particles as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor to implement the method for detecting mask particles as described in any one of claims 1-4.

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