Precise image alignment method, device and equipment and readable storage medium

Abstract

The invention discloses an image accurate alignment method, device and equipment and a readable storage medium, and can be applied to the technical field of semiconductors, and the method comprises the steps: obtaining a real shot optical image of a to-be-aligned wafer; determining a target Kernel image corresponding to the actually shot optical image; determining a target alignment template graph corresponding to the target Kernel image based on a preset alignment database; the preset alignment database is constructed based on the design layout; the target alignment template graph is one of all Target alignment graphs in a preset alignment database; determining a translation deviation value corresponding to the target Kernel image based on the target alignment template graph; and compensating all pixels in the target Kernel image based on the translation deviation value to realize image alignment. Thus, by introducing the preset alignment database, the design layout and the image alignment database are directly associated, and the accuracy of alignment between the real shot image and the design layout is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of semiconductor technology, in particular to a method and device for accurate image alignment, equipment and readable storage medium. BACKGROUND

[0002] The image alignment accuracy of a pattern optical bright field defect detection device is a core index for ensuring the yield of a chip. In the application of a wafer in a production line, a detected defect must be accurately mapped to a specific structure of a design layout, otherwise the influence of the defect on the function of the wafer circuit cannot be analyzed.

[0003] For the accuracy of image alignment, the mechanical alignment accuracy must be ensured first, such as the straightness of movement, the repeatability of reaching a position, and the like, which are generally in the order of hundreds of nanometers, but for the application of an advanced process production line, only the mechanical alignment accuracy is far from enough, and an image alignment algorithm based on feature recognition must be developed. The existing image alignment method is generally to recognize the position of a feature pattern in a collected image, and then calculate the deviation value between the theoretical coordinates and the actual coordinates, so as to realize the alignment of the pattern. However, the feature pattern applied to detection is generally an optical image, and the correlation with the design layout is not clear, thereby causing the problem of low alignment accuracy between the actual image and the design layout.

[0004] Therefore, how to improve the alignment accuracy between the actual image and the design layout is a problem that those skilled in the art urgently need to solve. SUMMARY

[0005] Based on the above problems, the present application provides a method and device for accurate image alignment, equipment and readable storage medium, by introducing a preset alignment database, the design layout and the image alignment database are directly associated, and the accuracy of alignment between the actual image and the design layout is improved.

[0006] In a first aspect, the embodiments of the present application provide a method for accurate image alignment, comprising:

[0007] obtaining an actual optical image of a wafer to be aligned;

[0008] determining a target Kernel image corresponding to the actual optical image;

[0009] determining a target alignment template image corresponding to the target Kernel image based on a preset alignment database; the preset alignment database is constructed based on a design layout; the target alignment template image is one of all Target alignment images in the preset alignment database;

[0010] determining a translation deviation value corresponding to the target Kernel image based on the target alignment template image;

[0011] Compensate all pixels in the target Kernel image based on the translation deviation value, and realize image alignment.

[0012] Optionally, the preset alignment database is constructed by the following method:

[0013] Construct a target layout corresponding to the wafer to be aligned.

[0014] Segment the target layout to obtain N alignment Kernel images.

[0015] Determine a Target feature map in each alignment Kernel image and a feature coordinate corresponding to the Target feature map according to a preset judgment basis.

[0016] Collect an alignment actual image corresponding to each feature coordinate.

[0017] Render each Target feature map based on the gray scale information of the alignment actual image to obtain a rendering map corresponding to each Target feature map.

[0018] Match the position of each rendering map and the corresponding alignment actual image to obtain a Target alignment map corresponding to each rendering map.

[0019] Construct a preset alignment database based on the feature coordinates and the Target alignment map.

[0020] Optionally, the construction of the target layout corresponding to the wafer to be aligned comprises:

[0021] Obtain an original design layout corresponding to the wafer to be aligned.

[0022] Extract a target layer corresponding to the target process from the original design layout based on the target process.

[0023] Construct a target layout based on the target layer.

[0024] Optionally, when the number of target layers is greater than or equal to two, the construction of the target layout based on the target layer comprises:

[0025] Merge data corresponding to each target layer.

[0026] Re-number and name the merged target layers to obtain a target layout.

[0027] Optionally, the method further comprises:

[0028] Determine a translation coefficient and a scaling coefficient between a first coordinate system corresponding to the target layout and a second coordinate system corresponding to the wafer to be aligned.

[0029] The acquisition of each feature coordinate corresponding to the real image of the alignment includes:

[0030] Based on the translation coefficient and the scaling coefficient, the acquisition point coordinates corresponding to each feature coordinate are determined.

[0031] Based on the acquisition point coordinates, the real image of the alignment corresponding to each feature coordinate is acquired.

[0032] Optionally, the determination of the translation coefficient and the scaling coefficient between the first coordinate system corresponding to the target layout and the second coordinate system corresponding to the wafer to be aligned includes:

[0033] The first target point and the second target point are determined on the wafer to be aligned, and the first point coordinates corresponding to the first target point and the second point coordinates corresponding to the second target point are determined based on the second coordinate system;

[0034] The third target point corresponding to the first target point and the fourth target point corresponding to the second target point are determined on the target layout;

[0035] The third point coordinates corresponding to the third target point and the fourth point coordinates corresponding to the fourth target point are determined based on the first coordinate system;

[0036] Based on the coordinate system mapping formula, the translation coefficient and the scaling coefficient between the first coordinate system and the second coordinate system are determined by using the first point coordinates, the second point coordinates, the third point coordinates and the fourth point coordinates.

[0037] Optionally, the determination of the Target feature map in each alignment Kernel image and the feature coordinates corresponding to the Target feature map according to the preset judgment basis includes:

[0038] Based on the uniqueness principle, the selected small image in each alignment Kernel image is determined;

[0039] According to the preset judgment basis, the target small image is selected from the selected small image corresponding to each alignment Kernel image as the Target feature map in each alignment Kernel image;

[0040] The feature coordinates of the Target feature map corresponding to each alignment Kernel image are determined.

[0041] Optionally, the selection of the target small image from the selected small image corresponding to each alignment Kernel image as the Target feature map in each alignment Kernel image according to the preset judgment basis includes:

[0042] At least one of a center distance of the candidate sub-image to the Kernel image, a number of polygons in a size range of the candidate sub-image, and an area proportion of the polygons in the candidate sub-image is taken as a preset judgment basis to construct a scoring function.

[0043] A candidate sub-image with a highest score is selected from each candidate sub-image corresponding to each Kernel image based on the scoring function, and is recorded as a target sub-image and taken as a Target feature map in each Kernel image.

[0044] Optionally, the feature coordinates of the Target feature map corresponding to each Kernel image include:

[0045] A center point coordinate of the Target feature map corresponding to each Kernel image is determined.

[0046] The center point coordinate of the Target feature map corresponding to each Kernel image is taken as the feature coordinates of the Target feature map corresponding to each Kernel image.

[0047] Optionally, when the center distance of the candidate sub-image to the Kernel image is taken as the preset judgment basis, different regions in which the candidate sub-image falls in the Kernel image are taken as the scoring function.

[0048] Optionally, when the number of polygons in the size range of the candidate sub-image is taken as the preset judgment basis, an expression of the scoring function is as follows:

[0049]

[0050] In the expression, S2 is the scoring function, A and B are two specific number values, and x is the number of polygons.

[0051] Optionally, when the area proportion of the polygons in the candidate sub-image is taken as the preset judgment basis, an expression of the scoring function is as follows:

[0052]

[0053]

[0054] In the expression, S2 is the scoring function, A and B are two specific number values, and x is the number of polygons. is a total area of the polygons in the candidate sub-image, is a total area of the candidate sub-image, is the area proportion, is the scoring function.

[0055] In a second aspect, an image accurate alignment device is provided, and the device includes:

[0056] ​​​An acquisition module is configured to acquire a real optical image of a wafer to be aligned.

[0057] A first determination module is configured to determine a target Kernel image corresponding to the real optical image.

[0058] A second determination module is configured to determine a target alignment template image corresponding to the target Kernel image based on a preset alignment database; the preset alignment database is constructed based on a design layout; and the target alignment template image is one of all Target alignment images in the preset alignment database.

[0059] A third determination module is configured to determine a translation deviation value corresponding to the target Kernel image based on the target alignment template image.

[0060] A compensation alignment module is configured to compensate all pixels in the target Kernel image based on the translation deviation value to achieve image alignment.

[0061] In a third aspect, an embodiment of the present application provides an image accurate alignment device, which comprises:

[0062] A memory is configured to store a computer program.

[0063] A processor is configured to execute the computer program to implement the steps of the image accurate alignment method.

[0064] In a fourth aspect, an embodiment of the present application provides a readable storage medium, which stores a computer program; when the computer program is executed by a processor, the steps of the image accurate alignment method are implemented.

[0065] As can be seen from the above technical solutions, compared with the prior art, the present application has the following advantages:

[0066] The image accurate alignment method provided by the present application first acquires a real optical image of a wafer to be aligned, and determines a target Kernel image corresponding to the real optical image. Then, a target alignment template image corresponding to the target Kernel image is determined based on a preset alignment database. The preset alignment database is constructed based on a design layout; and the target alignment template image is one of all Target alignment images in the preset alignment database. Finally, a translation deviation value corresponding to the target Kernel image is determined based on the target alignment template image, and all pixels in the target Kernel image are compensated based on the translation deviation value to achieve image alignment. In this way, by introducing the preset alignment database, the design layout and the image alignment database are directly associated, and the accuracy of alignment between the real image and the design layout is improved. BRIEF DESCRIPTION OF DRAWINGS

[0067] Figure 1 A flowchart of a method for accurate image alignment provided by an embodiment of the present application;

[0068] Figure 2 A flowchart of a method for constructing a preset alignment database provided by an embodiment of the present application;

[0069] Figure 3 A schematic diagram of constructing a target layout provided by an embodiment of the present application;

[0070] Figure 4 A schematic diagram of cutting a target layout provided by an embodiment of the present application;

[0071] Figure 5 A schematic diagram of a scoring basis provided by an embodiment of the present application;

[0072] Figure 6 A schematic diagram of a Target feature map position provided by an embodiment of the present application;

[0073] Figure 7 A schematic diagram of target point selection provided by an embodiment of the present application;

[0074] Figure 8 A rendering schematic diagram of a design layout provided by an embodiment of the present application;

[0075] Figure 9 A flowchart of generating a Target alignment map provided by an embodiment of the present application;

[0076] Figure 10 A structural schematic diagram of an apparatus for accurate image alignment provided by an embodiment of the present application. DETAILED DESCRIPTION

[0077] As described above, the existing image alignment method has the problem of low accuracy of alignment between the actual image and the design layout. Specifically, the existing image alignment method is usually to identify the position of the feature pattern in the collected image, and then calculate the deviation value of the theoretical coordinates and the actual coordinates, so as to realize the pattern alignment. However, the feature pattern applied to detection is generally an optical image, and the correlation with the design layout is not clear. Therefore, the problem of low accuracy of alignment between the actual image and the design layout is caused.

[0078] To solve the above problems, the embodiment of the present application provides a method for accurate image alignment, which comprises the following steps: first, obtaining a real optical image of a wafer to be aligned, and determining a target Kernel image corresponding to the real optical image. Then, determining a target alignment template image corresponding to the target Kernel image based on a preset alignment database. The preset alignment database is constructed based on a design layout; the target alignment template image is one of all Target alignment images in the preset alignment database. Finally, determining a translation deviation value corresponding to the target Kernel image based on the target alignment template image, and compensating all pixels in the target Kernel image based on the translation deviation value, so as to realize image alignment.

[0079] In this way, by introducing the preset alignment database, the design layout and the image alignment database are directly associated, thereby improving the accuracy of alignment between the real image and the design layout.

[0080] It should be noted that the method, device, equipment and readable storage medium for accurate image alignment provided by the embodiment of the present application can be applied to the field of semiconductor technology. The above is only an example, and does not limit the application field of the method, device, equipment and readable storage medium for accurate image alignment provided by the present application.

[0081] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme of the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0082] Figure 1 The flowchart of the method for accurate image alignment provided by the embodiment of the present application is shown in the figure. In combination with the figure, the method for accurate image alignment provided by the embodiment of the present application can comprise the following steps: Figure 1

[0083] S101: obtaining a real optical image of a wafer to be aligned.

[0084] In actual application, a Time-Delay Integration (TDI) camera can be used to obtain the real optical image of the wafer to be aligned.

[0085] S102: determining a target Kernel image corresponding to the real optical image.

[0086] ​In practical applications, according to the detection parameters configured by the detection device, such as pixel size, the continuous images collected by the TDI camera are cut to obtain local images, which are called Kernel images. At this time, the cutting parameters are the same as the cutting parameters used when the design layout is divided during the construction of the preset alignment database, and the physical size and position of the Kernel images obtained in each die are consistent with the results of the layout division. It can be understood that the actual optical image of the wafer to be aligned is composed of a large number of Kernel images. The target Kernel image is any one of all Kernel images that constitute the entire actual optical image, that is, the image accurate alignment method provided by the present application is to accurately align each Kernel image.

[0087] S103: determining a target alignment template corresponding to the target Kernel image based on a preset alignment database; the preset alignment database is constructed based on a design layout; the target alignment template is one of all Target alignment images in the preset alignment database.

[0088] In practical applications, the preset alignment database records the association between the actual optical image and the design layout. Specifically, for each Kernel image that constitutes the actual optical image, there is a unique corresponding Target alignment image in the preset alignment database, and the Target alignment image can clearly indicate the position matching between the design layout and the actual optical image within the size range of the corresponding Kernel image. Therefore, based on the preset alignment database, after determining the target Kernel image, the embodiment of the present application needs to first determine a unique corresponding Target alignment image, i.e., a target alignment template, from the preset alignment database.

[0089] S104: determining a translation deviation value corresponding to the target Kernel image based on the target alignment template.

[0090] In practical applications, the Target alignment image can clearly indicate the position matching between the design layout and the actual optical image within the size range of the corresponding Kernel image. Therefore, taking the Target alignment image corresponding to the target Kernel image (i.e., the target alignment template) and the feature coordinates of the design layout as inputs of an algorithm model, the translation deviation value corresponding to the target Kernel image can be obtained.

[0091] S105: compensating all pixels in the target Kernel image based on the translation deviation value to realize image alignment.

[0092] In practical applications, the translation deviation value obtained above is applied to all pixels in the target Kernel image, so as to compensate for the alignment deviation and realize the super-high-precision alignment between the actual image and the design layout.

[0093] To sum up, the method for image accurate alignment provided in the present application first acquires the actual optical image of the wafer to be aligned and determines the target Kernel image corresponding to the actual optical image. Then, the target alignment template image corresponding to the target Kernel image is determined based on the preset alignment database. The preset alignment database is constructed based on the design layout; and the target alignment template image is one of all Target alignment images in the preset alignment database. Finally, the translation deviation value corresponding to the target Kernel image is determined based on the target alignment template image, and all pixels in the target Kernel image are compensated based on the translation deviation value, so as to realize the image alignment. In this way, by introducing the preset alignment database, the design layout and the image alignment database are directly associated, and the accuracy of the alignment between the actual image and the design layout is improved.

[0094] Figure 2 The flowchart of the method for constructing the preset alignment database provided in the embodiments of the present application is shown in FIG. 2. In combination with FIG. 2, the method for constructing the preset alignment database can include the following steps. Figure 2

[0095] S201: Constructing the target layout corresponding to the wafer to be aligned.

[0096] In practical applications, the design layout applied to the wafer production line contains all process layer information of the wafer, and the detection equipment does not need all layer information, but focuses on one or several layers (Layers) faced by the current process detection. Therefore, it is necessary to reconstruct the target layout corresponding to the wafer to be aligned in the current process.

[0097] In addition, since the ways of constructing the target layout are not the same, the embodiments of the present application can describe one possible construction method.

[0098] In one case, S201: constructing the target layout corresponding to the wafer to be aligned, can specifically include the following steps.

[0099] Acquiring the original design layout corresponding to the wafer to be aligned;

[0100] Extracting the target layer corresponding to the target process from the original design layout based on the target process;

[0101] Constructing the target layout based on the target layer.

[0102] ​In practical applications, the original design layout corresponds to the whole process layer information of the wafer to be aligned. In order to conduct targeted image alignment in combination with target process detection, the target process needs to be determined first, and the target layer corresponding to the target process is extracted from the original design layout according to the target process. Then, the target layout corresponding to the target process is constructed based on the target layer and the data corresponding to the target layer.

[0103] Further, when the number of target layers is greater than or equal to two, the construction of the target layout based on the target layers includes:

[0104] merging the data corresponding to each target layer;

[0105] re-numbering and naming the merged target layers to obtain the target layout.

[0106] Figure 3 A schematic diagram of constructing a target layout is provided for the embodiments of the present application. As shown in Figure 3 the original design layout includes the whole process layer information of the wafer to be aligned, such as Layer 1 (Layer1), Layer 2 (Layer2), Layer 3 (Layer3), Layer 4 (Layer4), Layer 5 (Layer5), Layer 6 (Layer6), Layer 7 (Layer7), Layer 8 (Layer8), Layer 9 (Layer9), Layer 10 (Layer10), and Layer 11 (Layer11), etc. The target layer corresponding to the target process may only have Layer2, Layer3, Layer4, Layer7, and Layer9, etc. Therefore, the target layer corresponding to the target process is extracted first, and the data of multiple target layers is merged according to actual requirements. The principle of merging is that the target layers to be merged have the same or similar optical response, such as Layer2, Layer3, and Layer4. Further, the extracted and merged layers are re-numbered (for example, the layer number (Layer NO.) is New Layer1 (New Layer1), New Layer2 (New Layer2), New Layer3 (New Layer3), and New Layer4 (New Layer4)) and named (for example, the name (Description) is Metal1 (Metal1) and AA1, etc.) to facilitate subsequent calling. Finally, the data is saved to obtain the simplified layout data file, i.e., the target layout.

[0107] S202: segmenting the target layout to obtain N alignment Kernel images.

[0108] In practical applications, in order to correspond the N alignment Kernel images segmented from the target layout with the N target Kernel images segmented from the real optical image of the wafer to be aligned, the same segmentation parameters should be used. The size of the Kernel image in the detection process is determined according to the actual detection parameters of the detection device, such as the pixel size, and then the target layout is segmented according to the size to obtain the N alignment Kernel images. Wherein, N is an integer greater than or equal to 1. Figure 4 A segmentation schematic diagram of a target layout is provided for the embodiments of the present application. As shown in Figure 4 According to the segmentation size, the segmentation starts from the top left corner of the target layout. If the horizontal or vertical size of the target layout cannot be divided by the Kernel size, the alignment Kernel image of the last column / row is translated forward to ensure edge alignment, and N alignment Kernel images are obtained. Figure 4 In the embodiment, N=30.

[0109] S203: According to the preset judgment basis, the Target feature map in each alignment Kernel image and the feature coordinates corresponding to the Target feature map are determined.

[0110] In practical applications, for the N alignment Kernel images segmented from the target layout, a feature area needs to be selected from each alignment Kernel image for subsequent alignment. The feature area can be referred to as a Target feature map, and the size of the Target feature map is smaller than the size of the alignment Kernel image. The specific size can be customized by the user. Further, the selection of the Target feature map needs to comply with the preset judgment basis. After determining the Target feature map corresponding to each alignment Kernel image, the feature coordinates corresponding to each Target feature map are correspondingly saved.

[0111] In addition, since the ways of determining the Target feature map in each alignment Kernel image and the feature coordinates corresponding to the Target feature map are different, the embodiments of the present application can describe one possible determination method.

[0112] In one case, the Target feature map in each alignment Kernel image and the feature coordinates corresponding to the Target feature map are determined according to the preset judgment basis, including:

[0113] Based on the uniqueness principle, the candidate small maps in each alignment Kernel image are determined.

[0114] select a target sub-image as a target feature image in each alignment Kernel image according to a preset judgment basis;

[0115] determine feature coordinates of the target feature image corresponding to each alignment Kernel image.

[0116] In actual application, the selection of the target feature image needs to follow the uniqueness principle, that is, each target feature image has uniqueness within a certain range (the range is determined by the alignment accuracy of the detection device, such as 10 pixels (Pixel)) around it. At this time, there may be multiple sub-images in an alignment Kernel image that meet the above principle, which can be referred to as candidate sub-images. Further, a ranking mechanism needs to be introduced, and the best sub-image is selected as the target feature image from each candidate sub-image corresponding to each alignment Kernel image based on a preset judgment basis, and then the feature coordinates of each target feature image are recorded.

[0117] In addition, since the selection of the target feature image is not the same, the embodiments of the present application can describe one possible selection method.

[0118] In one case, the target sub-image is selected as the target feature image in each alignment Kernel image according to a preset judgment basis, including:

[0119] At least one of the distance of the candidate sub-image from the center of the alignment Kernel image, the number of polygons within the size range of the candidate sub-image, and the area proportion of the polygons in the candidate sub-image is used as a preset judgment basis to construct a scoring function;

[0120] The candidate sub-image with the highest score is selected from each candidate sub-image corresponding to each alignment Kernel image based on the scoring function, and is recorded as the target sub-image and as the target feature image in each alignment Kernel image.

[0121] Further, when the distance of the candidate sub-image from the center of the alignment Kernel image is used as the preset judgment basis, the different regions in the alignment Kernel image where the candidate sub-image falls are used as the scoring function.

[0122] When the number of polygons within the size range of the candidate sub-image is used as the preset judgment basis, the expression of the scoring function is:

[0123] ;

[0124] In the formula, S2 is the scoring function, A and B are two specific numerical values set, and x is the number of polygons.

[0125] When the area ratio of the polygon in the selected small image is taken as the preset judgment basis, the expression of the scoring function is:

[0126] ;

[0127] ;

[0128] In the formula, is the total area of the polygon in the selected small image, is the total area of the selected small image, is the area ratio, is the scoring function.

[0129] In actual application, in each alignment Kernel image, the scoring function can be constructed based on the distance of the selected small image from the center of the alignment Kernel image, the number of polygons in the size range of the selected small image, and the area ratio of the polygon, and then the selected small image with higher score is recorded as the target small image and as the Target feature map corresponding to the alignment Kernel image. Specifically, Figure 5 is a schematic diagram of a scoring basis provided by an embodiment of the present application. In combination with Figure 5 , when the distance of the selected small image from the center of the alignment Kernel image is taken as the preset judgment basis, different regions can be divided in the alignment Kernel image, and the region where the selected small image falls is taken as the scoring function, for example, the selected small image can be set to be located closer to the center of the alignment Kernel image, and the higher the score is. Among them, the centermost is 100 points, then 0-100 points, and the edge is 0 points. When the number of polygons in the size range of the selected small image is taken as the preset judgment basis, the scoring function corresponding to the number of polygons in the selected small image can be set to be higher, and the scoring function is as follows:

[0130] ;

[0131] In the formula, S2 is the scoring function, A and B are two specific number values set, and x is the number of polygons. When the area ratio of the polygon in the selected small image is taken as the preset judgment basis, the scoring function corresponding to the area ratio can be set to be higher, and the scoring function is as follows:

[0132] ;

[0133] ;

[0134] In the formula, is the total area of the polygon in the selected small image, is the total area of the selected small image, is the area ratio, is a score function. Thus, if two or more judgment bases are selected to score the to-be-selected small image, the score function is as follows:

[0135] ;

[0136] wherein, is a score function, represents the single-item score of the to-be-selected small image under each judgment basis, represents the weight of each judgment basis, represents different judgment bases. In addition, other judgment bases can be introduced in the embodiments of the present application, such as the number of polygon corner points contained in the to-be-selected small image, the line density of the polygon in the to-be-selected small image, and the like.

[0137] In addition, since the manner of determining the feature coordinates of the Target feature map corresponding to each alignment Kernel image is not the same, the embodiments of the present application can be described in terms of a possible determination manner.

[0138] In one case, the determination of the feature coordinates of the Target feature map corresponding to each alignment Kernel image comprises:

[0139] determining the center point coordinates of the Target feature map corresponding to each alignment Kernel image;

[0140] taking the center point coordinates of the Target feature map corresponding to each alignment Kernel image as the feature coordinates of the Target feature map corresponding to each alignment Kernel image.

[0141] Figure 6 is a schematic diagram of the position of a Target feature map provided by the embodiments of the present application. In combination with FIG. 5, Figure 6 each yellow pentagram in each alignment Kernel image is the position of the Target feature map selected from each to-be-selected small image. Further, the center point coordinates of each Target feature map are determined, and the center point coordinates of the Target feature map corresponding to each alignment Kernel image are taken as the feature coordinates of the Target feature map corresponding to each alignment Kernel image.

[0142] S204: Collecting an alignment real image corresponding to each feature coordinate.

[0143] In practical applications, it is necessary to collect alignment real image from the wafer to be aligned, and then match the alignment real image with the design layout to determine the deviation between them and save the deviation. Specifically, the alignment real image corresponds to the feature coordinates, and it is necessary to adjust the detection equipment parameters, such as waveband, polarization, and pixel size, to be consistent with the formal detection, and collect the image at the feature coordinates of each Target feature map as the alignment real image. Further, during the collection process, the size of the collected image needs to be controlled according to the alignment accuracy of the equipment hardware, so that the size of the collected image is greater than the size of the Target feature map. That is, the alignment real image should be slightly larger than the size of the Target feature map.

[0144] In addition, since the collection methods of the alignment real image are not the same, the embodiments of the present application can be described with respect to one possible collection method.

[0145] In one case, the method further comprises:

[0146] determining a translation coefficient and a scaling coefficient between a first coordinate system corresponding to the target layout and a second coordinate system corresponding to the wafer to be aligned;

[0147] collecting the alignment real image corresponding to each feature coordinate comprises:

[0148] determining a collection point coordinate corresponding to each feature coordinate based on the translation coefficient and the scaling coefficient;

[0149] collecting the alignment real image corresponding to each feature coordinate based on the collection point coordinate.

[0150] In practical applications, the feature coordinates are based on the coordinate system of the design layout, and the alignment real image corresponding to the wafer to be aligned is based on the coordinate system of the detection equipment. Although the design layout and the alignment real image correspond to each other, the coordinates of the two points corresponding to each other on them will be different due to factors such as motion error of the equipment. Therefore, it is necessary to determine the mapping relationship between the two coordinate systems before collecting the alignment real image, that is, to determine the translation coefficient and the scaling coefficient between the first coordinate system corresponding to the target layout and the second coordinate system corresponding to the wafer to be aligned. Further, based on the translation coefficient and the scaling coefficient, the collection point coordinate corresponding to each feature coordinate is determined, and then the image in a certain region is collected as the alignment real image corresponding to the feature coordinate based on the collection point coordinate. For example, it has been determined that the point coordinate (0, 0) on the first coordinate system corresponds to the point coordinate (1, 1) on the second coordinate system, and if the feature coordinate is (0, 0), the alignment real image corresponding to the feature coordinate needs to be collected with (1, 1) as the center.

[0151] In addition, since the manner of determining the translation coefficient and the scaling coefficient between the first coordinate system and the second coordinate system is different, the embodiments of the present application can be described in terms of one possible determination manner.

[0152] In one case, the determining of the translation coefficient and the scaling coefficient between the first coordinate system corresponding to the target layout and the second coordinate system corresponding to the wafer to be aligned includes:

[0153] determining a first target point and a second target point on the wafer to be aligned, and determining a first point coordinate corresponding to the first target point and a second point coordinate corresponding to the second target point based on the second coordinate system;

[0154] determining a third target point corresponding to the first target point and a fourth target point corresponding to the second target point on the target layout;

[0155] determining a third point coordinate corresponding to the third target point and a fourth point coordinate corresponding to the fourth target point based on the first coordinate system;

[0156] determining the translation coefficient and the scaling coefficient between the first coordinate system and the second coordinate system based on a coordinate system mapping formula by using the first point coordinate, the second point coordinate, the third point coordinate, and the fourth point coordinate.

[0157] In actual application, first, the wafer to be aligned is put into a detection device, and a (x, y) coordinate system, i.e., the second coordinate system, is established after completing operations such as rotation and alignment. Figure 7 A schematic diagram of target point selection provided by the embodiments of the present application is shown. As shown in Figure 7 , a Reticle (photomask) region is selected on the wafer to be aligned, which is generally close to the center of the wafer to be aligned. Further, a first target point and a second target point are determined on the Reticle region. The first target point can be a unique and sharp and easily distinguishable corner point at the lower left corner of the Reticle region, and the first point coordinate (x1, y1) is recorded. The second target point can be a unique and sharp and easily distinguishable corner point at the upper right corner of the Reticle region, and the second point coordinate (x2, y2) is recorded. Further, a third target point corresponding to the first target point and a fourth target point corresponding to the second target point are found on the target layout through position matching. The third point coordinate (x3, y3) of the third target point and the fourth point coordinate (x4, y4) of the fourth target point are recorded. Further, the two groups of point positions are substituted into a coordinate system mapping formula, and the corresponding scaling coefficient and translation coefficient can be solved. The corresponding coordinate system mapping formula is as follows: gds1 gds1 gds2 gds2 .​​​

[0158] ;

[0159] wherein x and y respectively correspond to the horizontal and vertical coordinates of the first point coordinate and the second point coordinate, x gds and y gds respectively correspond to the horizontal and vertical coordinates of the third point coordinate and the fourth point coordinate. and respectively correspond to the scaling coefficients of the two coordinate systems on the x-axis and the y-axis, and respectively correspond to the translation coefficients of the two coordinate systems on the x-axis and the y-axis.

[0160] S205: rendering each Target feature map based on the gray information of the alignment real photo image, to obtain a rendering map corresponding to each Target feature map.

[0161] In actual application, the alignment real photo image corresponds to the Target feature map and is slightly larger than the Target feature map in size. Because the Target feature map is cut from the target layout, its contrast is different from that of the real photo optical image, so the Target feature map needs to be rendered according to the gray information of the alignment real photo image in order to better match the two. Specifically, Figure 8 is a rendering schematic diagram of a design layout provided by an embodiment of the present application. In combination with Figure 8 , for each Target feature map, the Target feature map and the alignment real photo image corresponding to the Target feature map are aligned in coordinate position according to the mapping relationship of the coordinate system (for example, a in Figure 8 ), and then the Target feature map is processed according to the gray information of the alignment real photo image to obtain a rendering map corresponding to the Target feature map (for example, b in Figure 8 ).

[0162] S206: positionally matching each rendering map with the corresponding alignment real photo image to obtain a Target alignment map corresponding to each rendering map.

[0163] In actual application, because the target layout only contains several selected target layers corresponding to the target process, and the actual wafer structure optical imaging has complexity, the rendering map cannot be directly used as an alignment template map (a template map corresponding to a target Kernel image in the image accurate alignment process), and the alignment template map needs to come from the optical real photo result. Figure 9 is a flowchart of generating a Target alignment map provided by an embodiment of the present application. In combination with Figure 9As shown in a, a set of Target feature maps are obtained by cutting from the alignment kernel images, and b is a set of rendered maps obtained by rendering each Target feature map according to the gray information of the corresponding alignment real image. It can be understood that, since the Target alignment map used to construct the preset alignment database needs to be an optical real image, and the Target feature map has a large difference in gray information compared with the optical real image, an error is easily introduced when performing position matching. Therefore, in the embodiment of the present application, each Target feature map is first rendered to obtain a corresponding rendered map, and then based on the alignment real image corresponding to each Target feature map, the rendered map and the corresponding alignment real image are used for position template matching, as shown in c of Figure 9 Because the alignment real image is obtained based on the position of the Target feature map, combined with the above example, it is known that the point coordinate (0, 0) in the first coordinate system corresponds to the point coordinate (1, 1) in the second coordinate system, and if there is a Target feature map corresponding to a feature coordinate (0, 0), the alignment real image corresponding to the feature coordinate needs to be collected with (1, 1) as the center. It can be understood that, in the Target alignment map, the center point coordinate (0, 0) of the yellow box should coincide with the center point coordinate (1, 1) of the alignment real image, indicating no deviation. When the center of the yellow box does not coincide with the center of the alignment real image or the center of the Target alignment map, it indicates that there is a translation deviation value. That is, by each Target alignment map, it can be determined whether there is a translation deviation value for the target Kernel image corresponding to the Target alignment map.

[0164] S207: Construct a preset alignment database based on the feature coordinates and the Target alignment map.

[0165] In actual application, each Target alignment map and the feature coordinates of each Target alignment map corresponding to the Target feature map are saved to generate a preset alignment database. In actual alignment, the target alignment template map and the feature coordinates corresponding to the target Kernel image can be determined from the preset alignment database, and then the target alignment template map and the feature coordinates are input into the algorithm model as input to obtain the translation deviation value.

[0166] In summary, this embodiment of the application, when constructing a preset alignment database, firstly constructs a target layout corresponding to the wafer to be aligned, and segments the target layout to obtain N alignment kernel images. Then, based on preset judgment criteria, it determines the target feature map and the corresponding feature coordinates in each alignment kernel image, and acquires an alignment real-shot image corresponding to each feature coordinate. Next, it renders each target feature map based on the grayscale information of the alignment real-shot images, obtaining a rendered image corresponding to each target feature map. Finally, it performs position matching between each rendered image and the corresponding alignment real-shot image to obtain the target alignment map corresponding to each rendered image, and constructs a preset alignment database based on the feature coordinates and the target alignment map. In this way, the alignment database is automatically built, reducing the burden of manual operation, avoiding the introduction of human error, and supporting the configuration of high-density, large-scale feature graphics, further improving the accuracy of alignment between real-shot images and the design layout.

[0167] Figure 10 This is a schematic diagram of a device for precise image alignment provided in an embodiment of this application. (Combined with...) Figure 10 As shown, the image precise alignment device 1000 may include:

[0168] The acquisition module 1001 is used to acquire real-shot optical images of the wafer to be aligned;

[0169] The first determining module 1002 is used to determine the target kernel image corresponding to the real-shot optical image;

[0170] The second determining module 1003 is used to determine a target alignment template image corresponding to the target kernel image based on a preset alignment database; the preset alignment database is constructed based on the design layout; the target alignment template image is one of all target alignment images in the preset alignment database.

[0171] The third determining module 1004 is used to determine the translation deviation value corresponding to the target Kernel image based on the target alignment template image;

[0172] The compensation alignment module 1005 is used to compensate all pixels in the target kernel image based on the translation deviation value to achieve image alignment.

[0173] As one implementation method, the above-mentioned image precise alignment device 1000, which is designed to construct a preset alignment database, further includes: a first construction module, a segmentation module, a fourth determination module, an acquisition module, a rendering module, a position matching module, and a second construction module.

[0174] The first construction module is configured to construct a target layout corresponding to the wafer to be aligned.

[0175] The segmentation module is configured to segment the target layout to obtain N alignment Kernel images.

[0176] The fourth determination module is configured to determine, according to a preset judgment basis, a Target feature map in each alignment Kernel image and a feature coordinate corresponding to the Target feature map.

[0177] The acquisition module is configured to acquire an alignment actual photograph corresponding to each feature coordinate.

[0178] The rendering module is configured to render each Target feature map based on gray information of the alignment actual photograph to obtain a rendering map corresponding to each Target feature map.

[0179] The position matching module is configured to perform position matching between each rendering map and a corresponding alignment actual photograph to obtain a Target alignment map corresponding to each rendering map.

[0180] The second construction module is configured to construct a preset alignment database based on the feature coordinates and the Target alignment map.

[0181] As an implementation form, for how to construct a target layout corresponding to a wafer to be aligned, the first construction module is specifically configured to:

[0182] Obtain an original design layout corresponding to the wafer to be aligned;

[0183] Extract a target layer corresponding to a target process from the original design layout based on the target process;

[0184] Construct a target layout based on the target layer.

[0185] Further, when the number of target layers is greater than or equal to two, the construction of the target layout based on the target layer includes:

[0186] Merge data corresponding to each target layer;

[0187] Re-number and name the merged target layers to obtain a target layout.

[0188] As an implementation form, for how to acquire an alignment actual photograph, the image accurate alignment device 1000 further includes a fifth determination module.

[0189] The fifth determination module is configured to determine a translation coefficient and a scaling coefficient between a first coordinate system corresponding to the target layout and a second coordinate system corresponding to the wafer to be aligned.

[0190] The collection module is specifically configured to:

[0191] determine, based on the translation coefficient and the scaling coefficient, a collection point coordinate corresponding to each feature coordinate;

[0192] collect, based on the collection point coordinate, a real-time image corresponding to each feature coordinate.

[0193] As an implementation form, for how to determine the translation coefficient and the scaling coefficient, the fifth determination module is specifically configured to:

[0194] determine a first target point and a second target point on the wafer to be aligned, and determine a first point coordinate corresponding to the first target point and a second point coordinate corresponding to the second target point based on a second coordinate system;

[0195] determine a third target point corresponding to the first target point and a fourth target point corresponding to the second target point on the target layout;

[0196] determine a third point coordinate corresponding to the third target point and a fourth point coordinate corresponding to the fourth target point based on a first coordinate system;

[0197] determine a translation coefficient and a scaling coefficient between the first coordinate system and the second coordinate system based on a coordinate system mapping formula, by using the first point coordinate, the second point coordinate, the third point coordinate, and the fourth point coordinate.

[0198] As an implementation form, for how to determine the Target feature map and the feature coordinate, the fourth determination module includes a first determination submodule, a selection module, and a second determination submodule.

[0199] The first determination submodule is configured to determine a candidate sub-image in each alignment Kernel image based on a uniqueness principle.

[0200] The selection module is configured to select a target sub-image as a Target feature map in each alignment Kernel image from the candidate sub-images corresponding to each alignment Kernel image according to a preset judgment basis.

[0201] The second determination submodule is configured to determine a feature coordinate of the Target feature map corresponding to each alignment Kernel image.

[0202] As an implementation form, for how to select a target sub-image as a Target feature map in each alignment Kernel image from the candidate sub-images, the selection module is specifically configured to:

[0203] construct a scoring function based on at least one of the following as the preset judging basis: a center distance of the candidate sub-image to the positioning Kernel image, a number of polygons in a size range of the candidate sub-image, and an area proportion of the polygons in the candidate sub-image;

[0204] Based on the scoring function, a candidate sub-image with the highest score from each candidate sub-image corresponding to each positioning Kernel image is selected as a target sub-image and as the Target feature map in each positioning Kernel image.

[0205] Further, when the center distance of the candidate sub-image to the positioning Kernel image is taken as the preset judging basis, different regions in the positioning Kernel image where the candidate sub-image falls are taken as the scoring function;

[0206] When the number of polygons in the size range of the candidate sub-image is taken as the preset judging basis, the expression of the scoring function is:

[0207] ;

[0208] In the formula, S2 is the scoring function, A and B are two specific numerical values set, and x is the number of polygons;

[0209] When the area proportion of the polygons in the candidate sub-image is taken as the preset judging basis, the expression of the scoring function is:

[0210] ;

[0211] ;

[0212] In the formula, is the total area of the polygons in the candidate sub-image, is the total area of the candidate sub-image, is the area proportion, is the scoring function.

[0213] As an implementation manner, for how to determine the feature coordinates of the Target feature map corresponding to each Kernel image, the second determining submodule is specifically configured to:

[0214] determine the center point coordinates of the Target feature map corresponding to each positioning Kernel image;

[0215] take the center point coordinates of the Target feature map corresponding to each positioning Kernel image as the feature coordinates of the Target feature map corresponding to each positioning Kernel image.

[0216] To sum up, the application firstly acquires a real optical image of a wafer to be aligned and determines a target Kernel image corresponding to the real optical image. Then, a target alignment template corresponding to the target Kernel image is determined based on a preset alignment database. The preset alignment database is constructed based on a design layout; and the target alignment template is one of all Target alignment templates in the preset alignment database. Finally, a translation deviation value corresponding to the target Kernel image is determined based on the target alignment template, and all pixels in the target Kernel image are compensated based on the translation deviation value, so as to realize image alignment. In this way, by introducing the preset alignment database, the design layout and the image alignment database are directly associated, and the accuracy of alignment between the real optical image and the design layout is improved.

[0217] In addition, the application further provides an image accurate alignment device, comprising: a memory for storing a computer program; and a processor for implementing the steps of the image accurate alignment method when the computer program is executed.

[0218] In addition, the application further provides a readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the steps of the image accurate alignment method.

[0219] The above description of disclosed embodiments enables one of ordinary skill in the art to make or use the application. Various modifications to these embodiments will be readily apparent to those of ordinary skill in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Accordingly, the application is not to be limited to the embodiments shown herein but is to be accorded the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for accurate image alignment, characterized in that, The method comprises: acquiring a real optical image of a wafer to be aligned; determining a target Kernel image corresponding to the real optical image; determining a target alignment template image corresponding to the target Kernel image based on a preset alignment database; the preset alignment database is constructed based on a design layout; the target alignment template image is one of all Target alignment images in the preset alignment database; determining a translation deviation value corresponding to the target Kernel image based on the target alignment template image; compensating for all pixels in the target Kernel image based on the translation deviation value to achieve image alignment.

2. The method of claim 1, wherein, The preset alignment database is constructed by the following method: constructing a target layout corresponding to the wafer to be aligned; segmenting the target layout to obtain N alignment Kernel images; determining a Target feature image in each alignment Kernel image and a feature coordinate corresponding to the Target feature image according to a preset judgment basis; collecting an alignment real image corresponding to each feature coordinate; rendering each Target feature image based on the gray scale information of the alignment real image to obtain a rendering image corresponding to each Target feature image; positionally matching each rendering image with the corresponding alignment real image to obtain a Target alignment image corresponding to each rendering image; constructing a preset alignment database based on the feature coordinates and the Target alignment images.

3. The method of claim 2, wherein, The construction of the target layout corresponding to the wafer to be aligned comprises: acquiring an original design layout corresponding to the wafer to be aligned; extracting a target layer corresponding to the target process from the original design layout based on the target process; constructing a target layout based on the target layer.

4. The method of claim 3, wherein, When the number of target layers is greater than or equal to two, the construction of the target layout based on the target layer comprises: merging the data corresponding to each target layer; re-numbering and naming the merged target layers to obtain a target layout.

5. The method of claim 2, wherein, The method further comprises: determining a translation coefficient and a scaling coefficient between a first coordinate system corresponding to the target layout and a second coordinate system corresponding to the wafer to be aligned; The collection of an alignment real image corresponding to each feature coordinate comprises: determining a collection point coordinate corresponding to each feature coordinate based on the translation coefficient and the scaling coefficient; collecting an alignment real image corresponding to each feature coordinate based on the collection point coordinate.

6. The method of claim 5, wherein, The determination of a translation coefficient and a scaling coefficient between a first coordinate system corresponding to the target layout and a second coordinate system corresponding to the wafer to be aligned comprises: determining a first target point and a second target point on the wafer to be aligned, and determining a first point coordinate corresponding to the first target point and a second point coordinate corresponding to the second target point based on the second coordinate system; determining a third target point corresponding to the first target point and a fourth target point corresponding to the second target point on the target layout; determining a third point coordinate corresponding to the third target point and a fourth point coordinate corresponding to the fourth target point based on the first coordinate system; The translation coefficient and the scaling coefficient between the first coordinate system and the second coordinate system are determined based on a coordinate system mapping formula, by using the first point coordinate, the second point coordinate, the third point coordinate and the fourth point coordinate.

7. The method of claim 2, wherein, The Target feature map in each alignment Kernel image and the feature coordinates corresponding to the Target feature map are determined according to preset judgment basis, including: Based on the uniqueness principle, determine the candidate subgraphs in each alignment Kernel image; According to the preset judgment basis, the target subgraph is selected from the candidate subgraphs corresponding to each alignment Kernel image as the Target feature map in each alignment Kernel image; Determine the feature coordinates of the Target feature map corresponding to each alignment Kernel image.

8. The method of claim 7, wherein, The target subgraph is selected from the candidate subgraphs corresponding to each alignment Kernel image as the Target feature map in each alignment Kernel image according to the preset judgment basis, including: At least one of the distance of the candidate subgraph from the center of the alignment Kernel image, the number of polygons in the size range of the candidate subgraph, and the area proportion of the polygons in the candidate subgraph is used as the preset judgment basis to construct a scoring function; The candidate subgraph with the highest score is selected from each candidate subgraph corresponding to each alignment Kernel image based on the scoring function, and is recorded as the target subgraph and is used as the Target feature map in each alignment Kernel image.

9. The method of claim 7, wherein, The feature coordinates of the Target feature map corresponding to each alignment Kernel image are determined, including: Determine the center point coordinates of the Target feature map corresponding to each alignment Kernel image; The center point coordinates of the Target feature map corresponding to each alignment Kernel image are used as the feature coordinates of the Target feature map corresponding to each alignment Kernel image.

10. The method of claim 8, wherein, When the distance of the candidate subgraph from the center of the alignment Kernel image is used as the preset judgment basis, the different regions in the alignment Kernel image where the candidate subgraph falls are used as the scoring function.

11. The method of claim 8, wherein, When the number of polygons in the size range of the candidate subgraph is used as the preset judgment basis, the expression of the scoring function is: ； In the formula, S2 is the scoring function, A and B are two specific numerical values, and x is the number of polygons.

12. The method of claim 8, wherein, When the area proportion of the polygons in the candidate subgraph is used as the preset judgment basis, the expression of the scoring function is: ； ； wherein is the total area of the polygons within the candidate tile, is the total area of the candidate tile, is the area fraction, is the score function.

13. A device for precise image alignment, characterized in that, Including: An acquisition module is configured to acquire a real optical image of a wafer to be aligned; A first determination module is configured to determine a target Kernel image corresponding to the real optical image; A second determination module is configured to determine a target alignment template corresponding to the target Kernel image based on a preset alignment database; The preset alignment database is constructed based on a design layout; The target alignment template is one of all Target alignment images in the preset alignment database; A third determination module is configured to determine a translation deviation value corresponding to the target Kernel image based on the target alignment template; A compensation alignment module is used to compensate all pixels in the target Kernel image based on the translation deviation value, so as to realize image alignment.

14. A device for precise image alignment, characterized in that, The application relates to an image alignment method and device. The application relates to an image alignment method and device. The application relates to an image alignment method and device.

15. A readable storage medium, characterized by, The application relates to an image alignment method and device. The application relates to an image alignment method and device. The application relates to an image alignment method and device. The application relates to an image alignment method and device. The application relates to an image alignment method and device. The application relates to an image alignment method and device. The application relates to an image alignment method and device. The application relates to an image alignment method and device. The application relates to an image alignment method and device. The application relates to an image alignment method and device. The application relates to an image alignment method and device. The application relates to an image alignment method and device. The application relates to an image alignment method and device. The application relates to an image alignment method and device. The application relates to an image alignment method and device. The application relates to an image alignment method and device. The application relates to an image alignment method and device. The application relates to an image alignment method and device. The application relates to an image alignment method and device. The application relates to an image alignment method and

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