CN115829903A - Mask defect detection method and device, computer equipment and storage medium - Google Patents
Mask defect detection method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a mask defect detection method and device, computer equipment and a storage medium, wherein the defect detection method comprises the following steps. Acquiring a first image of a mask to be detected, and preprocessing the first image to obtain a second image, wherein the resolution of the second image is smaller than that of the first image; extracting the region of interest of the second image to determine a suspected defect region; performing image segmentation processing on the suspected defect area to determine a candidate target area; and determining defects existing on the mask to be detected according to the gray value of the candidate target area. The invention carries out image processing based on the mask space image, does not need to train a large amount of data, and realizes mask defect detection and mask defect position determination on the premise of smaller required data amount. The invention has the outstanding advantages of high detection rate, high detection speed, small position deviation, high detection precision, good robustness and the like.
Description
Technical Field
The invention relates to the technical field of mask defect detection, in particular to a mask defect detection method and device, computer equipment and a storage medium.
Background
Currently, euv lithography is the most promising lithography technology, and the fabrication of zero defect masks in euv lithography is one of the major challenges of euv lithography. As long as the mask defects with the line width of more than 10 percent are changed are all defects which can be transferred to the silicon wafer, the mask defects can seriously influence the final silicon wafer photoetching yield. The conventional mask defect detection method mainly comprises a phase recovery algorithm and a mask defect detection algorithm based on a neural network, but the phase recovery algorithm has the problem that the defect position cannot be determined, and the mask defect detection algorithm based on the neural network has the problem that a large amount of original training data is difficult to obtain, so that the detection result is inaccurate.
Disclosure of Invention
In order to solve the problems that the defect positioning cannot be realized or the defect detection result is inaccurate in the existing mask defect detection method, the invention can provide a mask defect detection method and device, computer equipment and storage medium so as to achieve the technical purposes of reliably and accurately detecting and positioning the mask defect and the like.
To achieve the above technical object, the present invention provides a mask defect detecting method including, but not limited to, one or more of the following steps.
A first image of a mask to be inspected is acquired.
And preprocessing the first image to obtain a second image, wherein the resolution of the second image is smaller than that of the first image.
And carrying out region-of-interest extraction processing on the second image to determine a suspected defect region.
And performing image segmentation processing on the suspected defect area to determine a candidate target area.
And determining defects existing on the mask to be detected according to the gray value of the candidate target area.
Further, the acquiring the first image of the mask to be detected includes:
and irradiating the surface of the mask to be detected by a light source to generate a first light ray.
And controlling the first light ray to pass through the zone plate to obtain a second light ray.
And receiving the second light through a charge coupled device to acquire the first image.
Further, the pre-processing the first image comprises:
and carrying out mean value filtering processing on the first image to obtain a mean value filtered image.
And obtaining the second image by carrying out Gaussian pyramid downsampling processing on the image after the average filtering.
Further, the performing of the region-of-interest extraction processing on the second image includes:
a background gray set is generated based on gray values of pixels in the second image.
And carrying out binarization processing on the second image according to whether the gray value of the pixel belongs to the background gray set or not to obtain a current binarization image.
And carrying out circumscribed rectangle processing on the connected region in the current binary image.
And extracting an external rectangular area with the external rectangular area larger than a preset value as a suspected defect area of the second image.
Further, the method also includes:
and carrying out gray inversion processing on the second image to generate a complementary set image.
And determining a suspected defect area of the complementary set image.
Further, the image segmentation processing on the suspected defect area includes:
and respectively generating a first segmentation line of the suspected defect area of the second image and a second segmentation line of the suspected defect area of the complementary set image in a watershed edge detection mode.
And segmenting the suspected defect area of the second image into a plurality of candidate target areas by using the first segmentation line, and segmenting the suspected defect area of the complementary set image into a plurality of candidate target areas by using the second segmentation line.
Further, the determining the defect existing on the mask to be detected according to the gray-scale value of the candidate target region includes:
and determining the gray difference value of the maximum gray value and the minimum gray value of each candidate target area.
Carrying out binarization processing on the second image according to the gray level difference values of all candidate target areas in the second image to obtain a first target binarization image; and carrying out binarization processing on the complementary set image according to the gray difference values of all candidate target areas in the complementary set image to obtain a second target binarization image.
Connecting adjacent candidate target areas in the first target binary image to obtain a first communication area; and connecting adjacent candidate target areas in the second target binary image to obtain a second connected area.
Determining the convex defect on the mask to be detected according to the gray value and the background gray value of the first connected region in the second image, and determining the concave defect on the mask to be detected according to the gray value and the background gray value of the second connected region in the complementary image.
To achieve the above technical objects, the present invention can also provide a mask defect detecting apparatus, which includes, but is not limited to, an image acquiring module, an image processing module, a region extracting module, an image dividing module, and a defect determining module.
The image acquisition module is used for acquiring a first image of the mask to be detected.
And the image processing module is used for preprocessing the first image to obtain a second image, and the resolution of the second image is smaller than that of the first image.
And the region extraction module is used for extracting a region of interest of the second image so as to determine a suspected defect region.
And the image segmentation module is used for carrying out image segmentation processing on the suspected defect area so as to determine a candidate target area.
And the defect judging module is used for determining the defects on the mask to be detected according to the gray value of the candidate target area.
To achieve the above technical object, the present invention can also provide a computer device including a memory and a processor, the memory having stored therein computer readable instructions, which, when executed by the processor, cause the processor to execute the steps of the mask defect detecting method according to any one of the embodiments of the present invention.
To achieve the above technical objects, the present invention may also specifically provide a storage medium storing computer-readable instructions, which when executed by one or more processors, cause the one or more processors to execute the steps of the mask defect detecting method according to any one of the embodiments of the present invention.
The invention has the beneficial effects that: compared with the technical problems that mask defects cannot be positioned or the required training data volume is large in the prior art, the mask defect detection method based on the mask aerial image carries out image processing on the basis of the mask aerial image, a large amount of data does not need to be trained, and mask defect detection and mask defect position determination are achieved on the premise that the required data volume is small, so that reliable, accurate and sufficient data support is provided for repair or compensation of the mask defects. The invention also has the outstanding advantages of higher detection rate, high detection speed, small position deviation, high detection precision, good robustness and the like. The invention can obtain the defect position and size information with small error, and is more beneficial to repairing and compensating the mask defect. A large number of experiments show that the invention can achieve very high defect detection rate for small-size defects with the peak width at half height of less than 80nm, and the average error of the size is less than 5%.
Drawings
FIG. 1 shows a schematic flow diagram of a mask defect detection method in one or more embodiments of the invention.
FIG. 2 shows a detailed flow diagram of an extreme ultraviolet mask defect detection method in one or more embodiments of the invention.
FIG. 3 illustrates a zone plate-based EUV mask defect detection system in accordance with one or more embodiments of the present invention.
FIG. 4 illustrates a schematic view of a first image of a mask to be inspected in one or more embodiments of the invention.
FIG. 5 illustrates an image schematic of a typical raised defect aerial image of a mask in one or more embodiments of the invention.
FIG. 6 depicts a schematic image of a mask representative pit defect aerial image in one or more embodiments of the invention.
FIG. 7 illustrates a Gaussian model of the EUV mask phase defect (H for peak height and W for peak width at half height) in one or more embodiments of the present invention.
FIG. 8 illustrates a schematic diagram of a watershed edge detection algorithm in one or more embodiments of the invention.
Fig. 9 shows a schematic diagram of a convex defect mark (solid mark) and a concave defect mark (hollow mark) outputted in one or more embodiments of the present invention.
Detailed Description
The following explains and explains a mask defect detecting method and apparatus, a computer device, and a storage medium in detail, which are provided by the present invention, with reference to the drawings of the specification.
As shown in fig. 1, and in conjunction with fig. 2-9, one or more embodiments of the present invention may provide a mask defect detection method. The mask can be an Extreme Ultraviolet (EUV) mask, the EUV mask is composed of a substrate, a multilayer film and an absorption layer from bottom to top, defects of the EUV mask are generally located at the bottom of the multilayer film or on the substrate, and due to the deposition effect of the actual multilayer film coating in a high-level process, the defects of the substrate and the defects generated during the coating process can cause the later coated film layer to deform and grow approximately in a gaussian shape, as shown in fig. 7, wherein H represents the peak height, and W represents the peak width at half maximum. The euv mask defect is generally a phase type defect, which refers to a deformation of the multilayer film that affects the amplitude and phase of reflected light of the multilayer film caused by protrusions and depressions on the substrate and particles dropped during the deposition process.
As shown in fig. 1 and 2, the mask defect inspection method of the present invention may include, but is not limited to, one or more of the following steps, which are described in detail below.
First, a first image of a mask to be detected is acquired, and the mask aerial image can be acquired through a system working light source based on a zone plate.
As shown in fig. 3, acquiring a first image of a mask to be detected in the embodiment of the present invention includes: the surface of the mask to be inspected of the present invention is illuminated by a light source after passing through an illumination system, which may include an illumination device such as a mirror, to generate a first light. And then controlling the first light to pass through the zone plate to obtain a second light. And receiving the second light by a Charge-coupled Device (CCD) to obtain a first image, wherein the first image is an image with mask defect characteristics. The light source system based on the zone plate has the advantage of higher resolution, can directly obtain the space intensity image of the mask, namely obtain the first image of the invention, and the first image can be a real-time image with the set resolution of 2nm and further used for processing the image to obtain defect information, and has very ideal effect.
As shown in fig. 4, an embodiment of the present invention illustrates a schematic view of an euv mask aerial image, i.e., a schematic view of a first image acquired by some embodiments of the present invention. Extreme ultraviolet mask phase defects may include both protrusions and pits, which differ in their intensity distribution across the aerial image.
As shown in fig. 5, this embodiment shows an aerial image schematic diagram of a typical convex whiteboard phase defect of an euv mask. The convex phase defect has the characteristics of weak defect strength at the center and strong edge.
As shown in fig. 6, this embodiment shows an aerial image schematic diagram of a typical pit-whiteboard phase defect of an euv mask. The pit phase defect has the characteristics that the defect strength is strong in the center and weak in the edge.
In addition, the oscillation intensity outside the edge of the two types of defects, namely the convex defect and the concave defect, is positively correlated with the distance between the centers of the defects, and the scattered particle noise in a poisson distribution is caused due to the non-uniform distribution of the number of photons in time.
Secondly, the first image is preprocessed to obtain a second image. The embodiment of the present invention reduces the resolution of the first image through a preprocessing method, and the resolution of the second image is smaller than the resolution of the first image, which is specifically described as follows.
As shown in fig. 2, the pre-processing of the first image according to the embodiment of the present invention includes: and performing mean filtering processing on the first image, specifically, performing disc mean filtering to reduce or even filter the particle noise in Poisson distribution to obtain a mean-filtered image. And obtaining a second image by carrying out Gaussian pyramid downsampling processing on the image after the average filtering. The disc mean filtering of the embodiment of the invention can use a circular convolution kernel with the radius of 5; and in the Gaussian pyramid downsampling processing process, a Gaussian kernel convolution is used, all even rows and even columns are removed, and the obtained second image is only one fourth of the second image before sampling. The method can reduce the influence of image shot noise on subsequent image processing by disc mean filtering and image Gaussian pyramid methods from the space image characteristics of mask defects, thereby reducing the sensitivity of the method to noise, obviously reducing the subsequent data processing amount and further improving the defect detection effect.
Thirdly, extracting the region of interest of the second image to determine a suspected defect region; by the method, the redundant operation of the subsequent non-defective background area is reduced, the suspected defect area is processed, and the data processing efficiency is obviously improved.
As shown in FIG. 2, the present invention screens out the region of interest, i.e. the suspected defect region, by using the difference between the background and the gray level fluctuation of the defect region based on the aerial image characteristics of the mask defect. The region of interest extraction processing on the second image according to the embodiment of the present invention includes the following processes.
(1) Generating a background gray set based on the gray value of the pixel in the second image, and performing binarization processing on the second image according to whether the gray value of the pixel belongs to the background gray set or not to obtain a current binarization image; in the embodiment of the invention, gray histogram statistics is carried out through the gray value of the pixel in the second image, the gray value accounting for more than 10% of the total number of the pixels is used as the background gray value according to the gray value statistical result, and a background gray set P is constructed through the background gray value bg . And then, carrying out binarization processing on the second image according to whether the gray value of the pixel belongs to a background gray set or not through the following formula so as to remove the background gray pixels.
Where p (x, y) represents the binarized pixel grayscale value and s (x, y) represents the pixel grayscale value in the second image.
(2) And performing external rectangle processing on a connected region in the current binary image, and extracting an external rectangular region with the external rectangular area larger than a preset value M to remove the external rectangle with a small area, wherein the preset value M can be set according to actual conditions and is used as a suspected defect region of the second image.
As shown in fig. 2, the defect detection method provided by the present invention may further include: the second image is subjected to gray inversion processing, and all gray values G are converted into 255-G to generate a complement image. It should be understood that, in the embodiment of the present invention, a process of generating a suspected defective area of a complement image is the same as a process of generating a suspected defective area of a second image, and details will not be described in this embodiment again.
Then, image segmentation processing is performed on the suspected defect area to determine a candidate target area. The method comprises the steps of carrying out image segmentation processing on the suspected defect area of the second image and carrying out image segmentation processing on the suspected defect area of the complementary set image.
As shown in fig. 2 and 8, the present invention uses a watershed algorithm to segment the image based on the aerial image characteristics of the mask defect. Specifically, in the embodiment of the present invention, the image segmentation processing on the suspected defect area includes: respectively generating a first segmentation line of a suspected defect area of the second image and a second segmentation line of a suspected defect area of the complementary image in a watershed edge detection mode; processing the second image and the complementary set image respectively, specifically, segmenting the suspected defect area of the second image into a plurality of candidate target areas by using a first segmentation line, and segmenting the suspected defect area of the complementary set image into a plurality of candidate target areas by using a second segmentation line; the candidate target area is a sub-area formed by dividing the second image and the complementary set image respectively.
As shown in fig. 8, the principle of watershed edge detection according to an embodiment of the present invention is as follows: the gray level image to be processed is regarded as a topological surface to simulate a bottom-up 'flooding' process, when different water-collecting basins are to be converged, the intersection is marked as a ridge line (a dividing line), and after the topological surface is completely submerged by water, the ridge line is closed to form a watershed (all the dividing lines). The watershed edge detection method is very suitable for processing the defect space image characteristics. Specifically, assume that there are R water-collecting basins, respectively M, at a certain moment in the immersion process 1 ，M 2 ，…，M R The non-water-collecting basin region is N, the non-water-collecting basin region is continuously immersed after the moment, the water area is expanded to the non-water-collecting basin region, and the updating of the water-collecting basin region M and the non-water-collecting basin region N can be completed according to the following modes after each immersion: 1) When a certain pixel in the N range is submerged, the water collecting basin M exists in the eight neighborhoods and only exists i When an inner pixel is present, it is marked as M i An area; 2) When a certain pixel in the N range is submerged and the eight neighborhoods are still the non-water-collecting basin region N, marking the pixel as a new water-collecting basin M R+1 (ii) a 3) When a certain pixel in the N range is submerged and a plurality of pixels in the water collecting basin range exist in the eight neighborhoods, marking the pixel as a watershed; 4) And after the updating is finished, continuously immersing the next gray level, and finishing the next updating until the updating of the immersed maximum gray level is finished.
Finally, determining defects existing on the mask to be detected according to the gray value of the candidate target area; the embodiment of the invention specifically determines the defects on the mask to be detected according to the difference value between the maximum gray value and the minimum gray value of the candidate target area.
As shown in fig. 2, the determining of the defect existing on the mask to be detected according to the gray-level value of the candidate target region according to the embodiment of the present invention may include the following steps.
(1) Marking each candidate target area, and determining the gray difference value delta P between the maximum gray value and the minimum gray value of each candidate target area.
ΔP＝P max -P min
Wherein Δ P represents a gray difference value, P max Representing the maximum value of the gray level, P, of the current candidate target region min Representing the minimum value of the gray level of the current candidate target area.
(2) Carrying out binarization processing on the second image according to the gray level difference values of all candidate target areas in the second image to obtain a first target binarization image; and carrying out binarization processing on the complementary set image according to the gray difference values of all candidate target areas in the complementary set image to obtain a second target binarization image. For the second image or the complementary set image, the embodiment maps each candidate region label to a set of Δ P, and determines a global threshold T, that is, the invention determines the global threshold T for binarization processing according to the gray difference set, and realizes primary screening of the candidate target region through binarization processing.
(3) The embodiment connects adjacent candidate target areas in the first target binary image and obtains a first communication area; connecting adjacent candidate target areas in the second target binary image to obtain a second connected area; the connection of neighboring candidate target regions may be performed by means of a closed operation.
(4) Determining a convex defect on the mask to be detected according to the gray value of the first connected region in the second image and the background gray value, and determining a concave defect on the mask to be detected according to the gray value of the second connected region in the complementary image and the background gray value; the specific determination method is as follows.
Wherein L represents a defect judgment result, G max Representing the maximum value of the gray level, G, in the connected region min Representing the minimum value of the gray level, P, in the connected region 1 Representing the maximum value of background gray in the background region, P 0 Representing the minimum value of the background gray in the background area.
Therefore, the invention can completely divide the convex defects in the second image and the concave defects in the complementary image, and can zero the non-convex area blocks detected in the second image and zero the non-concave area blocks detected in the complementary image. For the reserved area blocks, each area block consists of one or more sub-areas, and each sub-area corresponds to the gray level difference value delta P; the embodiment of the invention also comprises the following steps: the candidate target area with the maximum gray difference value delta P in each area block, namely the sub-area, is reserved, and the candidate target area is used as a target defect area, so that the mask defect can be accurately identified and positioned, in the embodiment, the defect image is marked according to the type, the size and the position of the target defect area, as shown in FIG. 9, and then the marked image and the related defect information are output.
The method selects 5 space images with the size of 1455 multiplied by 1455, the width of a defect peak is 1.5nm, the width of a half-height peak is 20-80 nm, the number, the size and the position are different, an algorithm based on watershed only is an algorithm 1, an algorithm combining an image pyramid and the watershed is an algorithm 2, and the comparison result is as follows.
Based on the verification result, the detection result shows that the algorithm combining the image pyramid and the watershed is more ideal for the defect detection result, the detection rate is higher, the size error is lower, the defect detection type is correct, and the deviation between the output position and the defect position is very small. From the table, it can be seen that, in the defects with the peak width at half height of less than 80nm, the detection rate of the algorithm to the defects can reach 100%, the root mean square errors of the sizes are all less than 5%, and high detection precision is achieved.
The embodiment of the invention combines the watershed edge detection algorithm and the image pyramid, and can further improve the defect detection rate. The invention improves the robustness of a defect detection algorithm through an image pyramid, reduces the sensitivity to noise, realizes a better detection effect on small-size defects with the peak width at half height of less than 80nm, can be applied to a zone plate-based extreme ultraviolet mask defect detection system, and can be suitable for various extreme ultraviolet mask defect detection devices based on the space image imaging principle.
Based on the same inventive concept as the mask defect detection method, the embodiment of the present invention can also provide a mask defect detection apparatus, which includes, but is not limited to, an image acquisition module, an image processing module, an area extraction module, an image segmentation module, and a defect determination module.
The image acquisition module is used for acquiring a first image of a mask to be detected.
The first image in the embodiment of the present invention may be obtained by: irradiating the surface of a mask to be detected by a light source to generate first light; controlling the first light ray to pass through the zone plate to obtain a second light ray; and receiving the second light through the charge coupled device to acquire a first image.
The image processing module is used for preprocessing the first image to obtain a second image, and the resolution of the second image is smaller than that of the first image.
Optionally, the image processing module is specifically configured to perform mean filtering processing on the first image to obtain a mean-filtered image; and the image processing unit is used for obtaining a second image by carrying out Gaussian pyramid downsampling processing on the image after the average value filtering.
The region extraction module is used for carrying out region-of-interest extraction processing on the second image so as to determine a suspected defect region.
Optionally, the region extraction module may be specifically configured to generate a background grayscale set based on grayscale values of pixels in the second image, and perform binarization processing on the second image according to whether the grayscale values of the pixels belong to the background grayscale set, so as to obtain a current binarized image. The region extraction module is also used for carrying out external rectangle processing on the connected region in the current binary image, and extracting an external rectangular region with the external rectangular area larger than a preset value as a suspected defect region of the second image.
Optionally, the defect detection apparatus according to the embodiment of the present invention may further include an image generation module, where the image generation module may be configured to perform gray inversion processing on the second image to generate a complementary set image. The region extraction module is further configured to determine a suspected defect region of the complement image.
The image segmentation module is used for carrying out image segmentation processing on the suspected defect area so as to determine a candidate target area.
Optionally, the image segmentation module is configured to generate a first segmentation line of a suspected defect area of the second image and a second segmentation line of a suspected defect area of the complementary image respectively by a watershed edge detection method; and the image segmentation module is used for segmenting the suspected defect area of the second image into a plurality of candidate target areas by using the first segmentation line and segmenting the suspected defect area of the complementary set image into a plurality of candidate target areas by using the second segmentation line.
The defect judgment module can be used for determining the defects existing on the mask to be detected according to the gray value of the candidate target area.
Optionally, the defect judgment module is configured to determine a gray difference between a maximum gray value and a minimum gray value of each candidate target region, and is configured to perform binarization processing on the second image according to the gray differences of all candidate target regions in the second image to obtain a first target binarized image; and the binarization processing module is used for carrying out binarization processing on the complementary set image according to the gray difference values of all candidate target areas in the complementary set image to obtain a second target binarization image. The defect judging module can be used for connecting adjacent candidate target areas in the first target binary image to obtain a first communication area; and the second connected region can be obtained by connecting the adjacent candidate target regions in the second target binary image. The defect judging module is used for determining the convex defect on the mask to be detected according to the gray value of the first connected region in the second image and the background gray value, and determining the pit defect on the mask to be detected according to the gray value of the second connected region in the complementary image and the background gray value.
Based on the same inventive concept as the defect inspection method, an embodiment of the present invention provides a computer apparatus, which may include a memory and a processor, wherein the memory stores computer-readable instructions, and the computer-readable instructions, when executed by the processor, cause the processor to execute the steps of the mask defect inspection method in any embodiment of the present invention. The mask defect detection method comprises the following steps: first, a first image of a mask to be detected is acquired. Optionally, the acquiring the first image of the mask to be detected according to the embodiment of the present invention includes: the surface of the mask to be detected is irradiated by a light source to generate a first light. And controlling the first light to pass through the zone plate to obtain a second light. And receiving the second light through the charge coupled device to acquire a first image. Secondly, the first image is preprocessed to obtain a second image, and the resolution of the second image is smaller than that of the first image. Optionally, the preprocessing the first image in the embodiment of the present invention includes: and carrying out mean value filtering processing on the first image to obtain a mean value filtered image. And obtaining a second image by carrying out Gaussian pyramid downsampling processing on the image after the average filtering. And thirdly, performing region-of-interest extraction processing on the second image to determine a suspected defect region. Optionally, the performing of the region of interest extraction processing on the second image in the embodiment of the present invention may include: and generating a background gray set based on the gray value of the pixel in the second image, and performing binarization processing on the second image according to whether the gray value of the pixel belongs to the background gray set or not to obtain the current binarization image. And performing circumscribed rectangle processing on the connected region in the current binary image, and extracting an external rectangular region with the circumscribed rectangular area larger than a preset value to be used as a suspected defect region of the second image. Optionally, the defect detection method provided by the present invention further includes: and carrying out gray inversion processing on the second image to generate a complementary set image, and determining the suspected defect area of the complementary set image in the manner. Then, image segmentation processing is performed on the suspected defect area to determine a candidate target area. Optionally, the image segmentation processing on the suspected defect area in the embodiment of the present invention includes: respectively generating a first segmentation line of a suspected defect area of the second image and a second segmentation line of a suspected defect area of the complementary image in a watershed edge detection mode; the suspected defect area of the second image is then segmented into a plurality of candidate target areas using the first segmentation line, and the suspected defect area of the complement image is segmented into a plurality of candidate target areas using the second segmentation line. And finally, determining the defects on the mask to be detected according to the gray value of the candidate target area. Optionally, the determining, according to the gray-scale value of the candidate target region, the defect existing on the mask to be detected includes: determining the gray difference value of the maximum gray value and the minimum gray value of each candidate target area, and performing binarization processing on the second image according to the gray difference values of all candidate target areas in the second image to obtain a first target binarization image; and carrying out binarization processing on the complementary set image according to the gray difference values of all candidate target areas in the complementary set image to obtain a second target binarization image. Connecting adjacent candidate target areas in the first target binary image to obtain a first communication area; and connecting adjacent candidate target areas in the second target binary image to obtain a second connected area. Determining the convex defect on the mask to be detected according to the gray value and the background gray value of the first communicated region in the second image, and determining the pit defect on the mask to be detected according to the gray value and the background gray value of the second communicated region in the complementary image.
Based on the same inventive concept as the defect inspection method, the embodiment of the present invention may further provide a storage medium storing computer-readable instructions, which when executed by one or more processors, cause the one or more processors to perform the steps of the mask defect inspection method according to any embodiment of the present invention. The mask defect detection method comprises the following steps: first, a first image of a mask to be detected is acquired. Optionally, the acquiring the first image of the mask to be detected according to the embodiment of the present invention includes: the surface of the mask to be detected is irradiated by a light source to generate first light. And controlling the first light to pass through the zone plate to obtain a second light. And receiving the second light through the charge coupled device to acquire a first image. Secondly, the first image is preprocessed to obtain a second image, and the resolution of the second image is smaller than that of the first image. Optionally, the preprocessing the first image according to the embodiment of the present invention includes: and carrying out mean value filtering processing on the first image to obtain a mean value filtered image. And obtaining a second image by carrying out Gaussian pyramid downsampling processing on the image subjected to the average filtering. And thirdly, performing region-of-interest extraction processing on the second image to determine a suspected defect region. Optionally, the performing of the region of interest extraction processing on the second image in the embodiment of the present invention may include: and generating a background gray set based on the gray value of the pixel in the second image, and performing binarization processing on the second image according to whether the gray value of the pixel belongs to the background gray set or not to obtain the current binarization image. And performing circumscribed rectangle processing on the connected region in the current binary image, and extracting an external rectangular region with the circumscribed rectangular area larger than a preset value to be used as a suspected defect region of the second image. Optionally, the defect detection method provided by the present invention further includes: and carrying out gray inversion processing on the second image to generate a complementary set image, and determining the suspected defect area of the complementary set image in the manner. Then, image segmentation processing is performed on the suspected defect area to determine a candidate target area. Optionally, the image segmentation processing on the suspected defect area in the embodiment of the present invention includes: respectively generating a first segmentation line of a suspected defect area of the second image and a second segmentation line of a suspected defect area of the complementary image in a watershed edge detection mode; the suspected defect area of the second image is then segmented into a plurality of candidate target areas using the first segmentation line, and the suspected defect area of the complement image is segmented into a plurality of candidate target areas using the second segmentation line. And finally, determining the defects on the mask to be detected according to the gray value of the candidate target area. Optionally, the determining the defect existing on the mask to be detected according to the gray-scale value of the candidate target region in the embodiment of the present invention includes: determining the gray difference value of the maximum gray value and the minimum gray value of each candidate target area, and performing binarization processing on the second image according to the gray difference values of all candidate target areas in the second image to obtain a first target binarization image; and carrying out binarization processing on the complementary set image according to the gray difference values of all candidate target areas in the complementary set image to obtain a second target binarization image. Connecting adjacent candidate target areas in the first target binary image to obtain a first communication area; and connecting adjacent candidate target areas in the second target binary image to obtain a second connected area. Determining the convex defect on the mask to be detected according to the gray value of the first connected region in the second image and the background gray value, and determining the pit defect on the mask to be detected according to the gray value of the second connected region in the complementary image and the background gray value.
The logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable storage medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer cartridge (magnetic device), a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM-Only Memory, or flash Memory), an optical fiber device, and a portable Compact Disc Read-Only Memory (CDROM). Additionally, the computer-readable storage medium may even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following technologies, which are well known in the art, may be used: a discrete logic circuit having a logic Gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic Gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "the present embodiment," "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specified otherwise.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and simplifications made in the spirit of the present invention are intended to be included in the scope of the present invention.
Claims (10)
1. A mask defect inspection method, comprising:
acquiring a first image of a mask to be detected;
preprocessing the first image to obtain a second image, wherein the resolution of the second image is smaller than that of the first image;
extracting the region of interest of the second image to determine a suspected defect region;
performing image segmentation processing on the suspected defect area to determine a candidate target area;
and determining defects existing on the mask to be detected according to the gray value of the candidate target area.
2. The mask defect inspection method of claim 1, wherein said obtaining a first image of a mask to be inspected comprises:
irradiating the surface of the mask to be detected by a light source to generate first light;
controlling the first light to pass through a zone plate to obtain second light;
and receiving the second light through a charge coupled device to acquire the first image.
3. The mask defect inspection method of claim 1, wherein said preprocessing the first image comprises:
carrying out mean value filtering processing on the first image to obtain a mean value filtered image;
and obtaining the second image by carrying out Gaussian pyramid downsampling processing on the image after the average filtering.
4. The mask defect inspection method according to claim 1, wherein said performing region-of-interest extraction processing on the second image comprises:
generating a background gray set based on gray values of pixels in the second image;
performing binarization processing on the second image according to whether the gray value of the pixel belongs to a background gray set or not to obtain a current binarization image;
carrying out circumscribed rectangle processing on the connected region in the current binary image;
and extracting an external rectangular area with the external rectangular area larger than a preset value as a suspected defect area of the second image.
5. The mask defect inspection method of claim 4, further comprising:
performing gray inversion processing on the second image to generate a complementary set image;
and determining a suspected defect area of the complementary set image.
6. The mask defect inspection method of claim 5, wherein said image segmentation processing of the suspected defect area comprises:
respectively generating a first dividing line of a suspected defect area of the second image and a second dividing line of a suspected defect area of the complementary set image in a watershed edge detection mode;
and segmenting the suspected defect area of the second image into a plurality of candidate target areas by using the first segmentation line, and segmenting the suspected defect area of the complementary set image into a plurality of candidate target areas by using the second segmentation line.
7. The mask defect inspection method of claim 6, wherein said determining defects existing on the mask to be inspected according to the gray-level values of the candidate target regions comprises:
determining the gray difference value of the maximum gray value and the minimum gray value of each candidate target area;
carrying out binarization processing on the second image according to the gray level difference values of all candidate target areas in the second image to obtain a first target binarization image; carrying out binarization processing on the complementary set image according to the gray difference values of all candidate target areas in the complementary set image to obtain a second target binarization image;
connecting adjacent candidate target areas in the first target binary image to obtain a first communication area; connecting adjacent candidate target areas in the second target binary image to obtain a second connected area;
determining the convex defect on the mask to be detected according to the gray value and the background gray value of the first connected region in the second image, and determining the concave defect on the mask to be detected according to the gray value and the background gray value of the second connected region in the complementary image.
8. A mask defect inspection apparatus, comprising:
the image acquisition module is used for acquiring a first image of the mask to be detected;
the image processing module is used for preprocessing the first image to obtain a second image, and the resolution of the second image is smaller than that of the first image;
the region extraction module is used for extracting a region of interest of the second image so as to determine a suspected defect region;
the image segmentation module is used for carrying out image segmentation processing on the suspected defect area so as to determine a candidate target area;
and the defect judging module is used for determining the defects on the mask to be detected according to the gray value of the candidate target area.
9. A computer device comprising a memory and a processor, the memory having stored therein computer readable instructions which, when executed by the processor, cause the processor to carry out the steps of the mask defect detection method according to any one of claims 1 to 7.
10. A storage medium having computer-readable instructions stored thereon, which, when executed by one or more processors, cause the one or more processors to perform the steps of the mask defect inspection method of any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116309553A (en) * | 2023-05-12 | 2023-06-23 | 东莞市希锐自动化科技股份有限公司 | Method for detecting electroplating defects of non-planar electroplating hardware |
| CN116433666A (en) * | 2023-06-14 | 2023-07-14 | 江西萤火虫微电子科技有限公司 | Board card line defect online identification method, system, electronic equipment and storage medium |
| CN117808796A (en) * | 2024-02-23 | 2024-04-02 | 陕西长空齿轮有限责任公司 | Gear surface damage detection method based on computer vision |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116309553A (en) * | 2023-05-12 | 2023-06-23 | 东莞市希锐自动化科技股份有限公司 | Method for detecting electroplating defects of non-planar electroplating hardware |
| CN116433666A (en) * | 2023-06-14 | 2023-07-14 | 江西萤火虫微电子科技有限公司 | Board card line defect online identification method, system, electronic equipment and storage medium |
| CN116433666B (en) * | 2023-06-14 | 2023-08-15 | 江西萤火虫微电子科技有限公司 | Board card line defect online identification method, system, electronic equipment and storage medium |
| CN117808796A (en) * | 2024-02-23 | 2024-04-02 | 陕西长空齿轮有限责任公司 | Gear surface damage detection method based on computer vision |
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