A method and device for identifying defects in a unitized mask

Abstract

The application relates to a unitized mask defect identification method and device, which comprises the following steps: obtaining a wafer based on a mask, and obtaining a plurality of to-be-tested images based on the wafer, wherein at least one to-be-tested image comprises at least one first target point; obtaining a to-be-analyzed image model based on the plurality of to-be-tested images, wherein the to-be-analyzed image model comprises a second target point formed by the first target point; obtaining effective target points based on the second target point; performing grid division on the to-be-analyzed image model to obtain a plurality of grid units; obtaining the grid positions of the effective target points based on the grid units, and classifying the effective target points based on the grid positions; and determining target defects based on the classified effective target points. The method and device provided by the application can efficiently identify various defects of a mask.

Description

Technical Field

[0001] This invention relates to the field of semiconductor inspection technology, and in particular to a method and device for identifying defects in a unitized mask. Background Technology

[0002] In chip manufacturing, a large number of photomasks are used to transfer designed circuit patterns onto wafers. If defects exist on the photomasks, incorrect circuit patterns will be transferred onto the wafers, resulting in yield losses. Since photomasks are used on tens of thousands, or even millions, of wafers, the detection of photomask defects is crucial in the semiconductor manufacturing process.

[0003] Defects in photomasks are typically detected by transferring the photomask circuit pattern onto the wafer. This method is widely used because it can detect a large number of targets (e.g., the image under test acquired from the wafer) and can be performed simultaneously with other scanning tasks. However, this method still has limitations in terms of recognition efficiency and accuracy. To comprehensively detect and identify defects in the image under test, it is often necessary to calculate the distances between all defect points. When the number of defect points in the image under test is usually large, this results in a very large computational load, making the entire defect detection and identification process time-consuming. Furthermore, the exceptionally large amount of data processing also affects the accuracy of the calculations. Summary of the Invention:

[0004] To address the aforementioned technical problems, the present invention aims to provide a method for identifying defects in a unitized photomask, comprising the following steps:

[0005] A wafer is prepared based on a mask, and multiple images to be tested are obtained based on the wafer, wherein at least one of the images to be tested includes at least one first target point;

[0006] An image model to be analyzed is obtained based on multiple images to be tested, and the image model to be analyzed includes: a second target point formed by the first target point;

[0007] Obtain valid target points based on the second target point;

[0008] The image model to be analyzed is divided into several grid cells.

[0009] The grid positions of the effective target points are obtained based on the grid cells, and the effective target points are classified based on the grid positions.

[0010] The target defect is determined based on the classified valid target points.

[0011] Furthermore, in some embodiments, the step of determining the target defect includes:

[0012] When the number of valid target points with the same grid position is greater than a second preset value, the corresponding grid cell is marked as a first calculation unit, and the valid target points in the first calculation unit are marked as first target defect points;

[0013] At least one of the grid cells adjacent to the first computing unit is marked as a second computing unit;

[0014] The second target defect point is determined based on the distance between the first target defect point and each of the effective target points in the second calculation unit, and the target defect is determined based on the first and second target defect points.

[0015] In some embodiments, the step of meshing the image model to be analyzed includes:

[0016] The allowable deviation is determined based on the accuracy of the image to be tested;

[0017] The image model to be analyzed is divided into grids based on the allowable deviation.

[0018] In some embodiments, the grid position is (int(x / G), int(y / G)), where (x, y) is the absolute position of the effective target point, and G is the side length of the grid cell.

[0019] In some embodiments, the step of obtaining a valid target point based on the second target point includes:

[0020] The occurrence frequency of the second target point is obtained, wherein the occurrence frequency is the number of first target points corresponding to the second target point;

[0021] When the occurrence frequency of the second target point is greater than the first preset value, the corresponding second target point is marked as a valid target point.

[0022] Furthermore, in some embodiments, the step of determining the second target defect includes:

[0023] Obtain the center position of the first target defect point;

[0024] Calculate the first distance between each of the effective target points in the second calculation unit and the center position, and when the first distance is less than a third preset value, mark the corresponding effective target point as the second target defect point.

[0025] Alternatively, in other embodiments, the step of determining the second target defect includes:

[0026] Obtain the location information of each of the first target defect points;

[0027] Based on the location information, a second distance is determined between the effective target point in the second calculation unit and each of the first target defect points;

[0028] When at least one of the second spacings is less than a fourth preset value, or when the average of the second spacings is less than a fourth preset value, the corresponding effective target point in the second calculation unit is marked as a second target defect point.

[0029] In some embodiments, when two or more of the first computing units are adjacent, the grid unit with the most effective target points is still marked as the first computing unit, and the other first computing units adjacent to the first computing unit are marked as the second computing unit.

[0030] In some embodiments, when two or more of the first computing units are adjacent and the number of effective target points of the two or more first computing units is the same, the two or more first computing units are still marked as first computing units.

[0031] In some embodiments, when there is a single grid cell between two first computing units, the first computing unit corresponding to the grid cell is determined based on the distribution state of the effective target points of the separated grid cells, wherein the distribution state includes: the regional distribution trend of the effective target points on the grid cell.

[0032] In some embodiments, the location information is the absolute location of the valid target point.

[0033] A second aspect of the present invention is that, based on the above-described method for identifying defects in a modular mask, a corresponding device for identifying defects in a modular mask is also provided, comprising:

[0034] The image acquisition module is configured to prepare a wafer based on a mask and acquire multiple images to be tested based on the wafer, wherein at least one of the images to be tested includes at least one first target point;

[0035] The image model determination module is configured to obtain an image model to be analyzed based on multiple images to be tested, and the image model to be analyzed includes: a second target point formed by the first target point;

[0036] The effective target point determination module is configured to obtain effective target points based on the second target point;

[0037] The meshing module is configured to divide the image model to be analyzed into several mesh units.

[0038] The effective target point classification module is configured to obtain the grid position of the effective target point based on the grid cell, and classify the effective target point based on the grid position;

[0039] The target defect determination module is configured to determine the target defect based on the classified valid target points;

[0040] In some embodiments, the target defect determination module includes:

[0041] The first target defect point marking unit is configured to mark the corresponding grid unit as a first calculation unit and mark the effective target point in the first calculation unit as a first target defect point when the number of valid target points with the same grid position is greater than a second preset value.

[0042] The second computing unit marking unit is configured to mark at least one of the grid cells adjacent to the first computing unit as the second computing unit;

[0043] The target defect determination unit is configured to determine a second target defect point based on the distance between the first target defect point and each of the effective target points in the second calculation unit, and to determine the target defect based on the first and second target defect points.

[0044] Beneficial technical effects

[0045] This invention provides a unitized mask defect identification method. This method addresses target defects through grid division (i.e., it achieves target defect localization), thereby enabling overall identification of target defects within their respective regions and obtaining comprehensive information about the target defects. Because this application can identify individual target defects based on their address information, it eliminates the need for overall calculation (i.e., pairwise calculation) of target points on the image under test or the image model to be analyzed, significantly reducing computational load, improving defect identification efficiency, and ensuring the accuracy of defect identification. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. The elements or parts in the drawings are not necessarily drawn to scale. Obviously, the drawings described below are some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of the method flow in an exemplary embodiment of the present invention;

[0048] Figure 2a This is a schematic diagram of the structure of the image to be tested in an exemplary embodiment of the present invention;

[0049] Figure 2b For based on Figure 2a A schematic diagram of the image model to be analyzed obtained from the image to be tested, as shown in the figure;

[0050] Figure 3a for Figure 2b The diagram shows a grid division of the image model to be analyzed.

[0051] Figure 3b for Figure 3a A partially enlarged schematic diagram of the first and second computational grids in the diagram;

[0052] Figure 3c for Figure 3a A magnified view of a portion of the defect in the target area;

[0053] Figure 4a This is a schematic diagram of the mesh division of the model to be analyzed in a specific embodiment of the present invention;

[0054] Figure 4b This is a schematic diagram of the mesh division of the model to be analyzed in another specific embodiment of the present invention;

[0055] Figure 4c This is a schematic diagram of the mesh division of the model to be analyzed in another specific embodiment of the present invention;

[0056] Figure 5 This is a schematic diagram of the identification device module structure in an exemplary embodiment of the present invention;

[0057] Figure 6 This is a block diagram of an electronic device in an exemplary embodiment of the present invention.

[0058] 01 is the image to be tested, 011 is the first target point, 02 is the image model to be analyzed, 021 is the second target point, 022 is the effective target point, 03 is the first calculation unit, 04 is the second calculation unit, 05 is the target defect, 06a is the first grid, 06b is the second grid, 06c is the third grid, 06d is the fourth grid, 06e is the fifth grid, 06f is the sixth grid, and 06g is the seventh grid. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0060] In this document, suffixes such as "module," "part," or "unit" used to denote elements are used only for the purpose of illustrative purposes and have no specific meaning in themselves. Therefore, "module," "part," or "unit" may be used interchangeably.

[0061] Definition of the noun:

[0062] In this document, a "mask" generally refers to a transparent substrate having patterned areas of opaque material formed thereon, such as glass, borosilicate glass, and fused silica. The opaque areas can be replaced by areas etched into the transparent substrate. Many different types of masks are known in the art, and the term mask as used herein is intended to encompass all types of masks.

[0063] In this paper, "target defect point" refers to a valid target point (or defect point) belonging to a cluster, that is, a valid target point among the target defects to be identified. Several adjacent "target defect points" will form a target defect (repeater) on the mask. Target defect points include: first target defect point and second target defect point.

[0064] Example 1

[0065] See Figure 1 The present invention provides a method for identifying defects in a unitized mask, comprising the steps of:

[0066] S10: A wafer is prepared based on a mask, and multiple images to be tested are obtained based on the wafer, wherein at least one image to be tested includes at least one first target point;

[0067] S20: Obtain an image model to be analyzed based on multiple images to be tested, and the image model to be analyzed includes: a second target point formed by the first target point;

[0068] S30: Obtain valid target points based on the second target point;

[0069] S40: The image model to be analyzed is divided into several grid cells (e.g., ...). Figure 3a Figure 3b and Figure 3c (as shown);

[0070] S50: Obtain the grid position of valid target points based on grid cells, and classify the valid target points based on the grid position (for example, group valid target points with the same grid position into a set);

[0071] S60: Determine target defects based on the classified valid target points.

[0072] Furthermore, in some embodiments, the target defect 05 is determined (e.g., Figure 3c The steps (as shown) include: when the number of valid target points with the same grid position is greater than a second preset value, the corresponding grid cell is marked as the first computing unit 03 (e.g., ...). Figure 3b (as shown), and the effective target points within the first calculation unit are marked as first target defect points;

[0073] At least one of the grid cells adjacent to the first computing unit is marked as a second computing unit;

[0074] The second target defect point is determined based on the distance between the first target defect point and each of the effective target points in the second calculation unit, and the target defect is determined based on the first and second target defect points.

[0075] Specifically, in some embodiments, when the number of valid target points 022 with the same grid position is greater than a second preset value, the corresponding valid target point 022 is marked as a first target defect point (that is, valid target points with the same grid position are classified into one target defect), and the corresponding grid cell is marked as a first computing unit 03;

[0076] At least one of the grid cells adjacent to the first computing unit 03 is designated as the second computing unit 04;

[0077] The target location of the first target defect point is obtained, and the first distance between at least one valid target point in the second calculation unit and the target location is calculated. When the first distance is less than a third preset value, the corresponding valid target point is marked as the second target defect point.

[0078] The target defect is determined based on the first and second target defect points.

[0079] For example, in some specific embodiments, the circuit pattern on the mask to be tested is first transferred onto one or more wafers. Figure 2a As shown, multiple circuit patterns are acquired on a wafer, and multiple test images 01 are obtained based on these circuit patterns using an image scanning device (such as a scanning electron microscope (SEM), optical microscope, or electron beam microscope, etc.). Each test image 01 includes one or more first target points. The acquired test images are then stacked to obtain an image model 02 to be analyzed (e.g., ...). Figure 2b As shown, multiple first target points 011 on multiple images to be tested correspond to multiple second target points 021 on the image model 02 to be analyzed.

[0080] For example, multiple images to be tested are stacked, and then a three-dimensional space is projected using a neighborhood algorithm to obtain an image model to be analyzed.

[0081] Preferably, in some embodiments, the step of meshing the image model to be analyzed includes:

[0082] The allowable deviation is determined based on the accuracy of the image to be tested;

[0083] The image model to be analyzed is divided into meshes based on the tolerance.

[0084] Preferably, in some embodiments, the accuracy of the image under test can be determined by the accuracy of the image scanning device used to acquire the image. For example, in one specific embodiment, the coordinates of a first target point generated by a defect on one image under test are (100µm, 100µm), and the coordinates of the first target point generated by the defect on another image under test are (101µm, 99.8µm). Both first target points correspond to the same defect location on the mask, but due to potential errors in the actual acquisition of the image under test (such as limited scanning accuracy), the coordinates of these first target points may deviate. In this embodiment, to further improve the accuracy of defect identification and eliminate the influence of instrument accuracy issues, the allowable deviation can be determined based on the accuracy of the image scanning device.

[0085] Preferably, in some embodiments, the allowable deviation is set to twice the device accuracy; for example, the allowable deviation can be set to 5µm, 10µm, or 20µm. Of course, the specific setting of the allowable deviation can be reasonably customized by the user based on their needs.

[0086] For example, in some embodiments, the mesh can be divided with a tolerance as the mesh edge length, or in other embodiments, the mesh edge length can be greater than the tolerance (e.g., the mesh edge length is three times the tolerance).

[0087] Furthermore, in some embodiments, the grid position is (int(x / G), int(y / G)), where (x, y) is the absolute position of the effective target point 022, and G is the side length of the grid cell.

[0088] For example, in some embodiments, when the number of valid target points located in the 5th row and 6th column is greater than a second preset value, the grid cell in the 5th row and 6th column is marked as the first calculation unit, and the valid target point with the grid position in the 5th row and 6th column is marked as the first target defect point.

[0089] Specifically, in some embodiments, a Cartesian coordinate system is established on the image model to be analyzed, and the center point of the Cartesian coordinate system is defined to obtain the Cartesian coordinates (i.e., absolute position) of each effective target point. Further, based on the absolute position of each effective target point and the side length of the grid cell, the grid position (or row and column position) of each effective target point is determined, and the position of the target defect is further located using the row and column positions of the effective target points.

[0090] In this embodiment, the effective target points are first classified quickly and efficiently by the row and column positions (i.e., grid positions) of the effective target points to obtain a set of effective target points that are determined to be target defects (including: the first target defect point and the second target defect point), which means that the target defects are located.

[0091] In this embodiment, in the meshing results obtained based on the allowable deviation, valid target points located at the same mesh position (or adjacent mesh positions) usually originate from the same defect on the mask. Therefore, after meshing based on the allowable deviation, the location of the target defect can be quickly and accurately obtained, and the target defect can be identified as a whole based on the main area of ​​the target defect, thereby obtaining the overall information of the target defect, that is, obtaining multiple valid target points (i.e., target defect points) corresponding to the target defect.

[0092] In some embodiments, the step of obtaining a valid target point 022 based on the second target point 021 includes:

[0093] The occurrence frequency of the second target point 021 is obtained, wherein the occurrence frequency is the number of first target points 011 corresponding to the second target point 021;

[0094] When the occurrence frequency of the second target point 021 is greater than the first preset value, the corresponding second target point 021 is marked as a valid target point 022.

[0095] The occurrence frequency of the second target point indicates the number of times the second target point appears on different test images. For example, in some embodiments, when the number of first target points 011 corresponding to the second target point 021 is 8, that is, the occurrence frequency of the second target point is 8, which means that the second target point appears on all 8 test images.

[0096] To improve the accuracy of defect identification, preferably, in some embodiments, since there are usually multiple target points on the image under test, and two or more target points may be adjacent or overlap, these two or more target points are identified (or determined) as a first target point. For example, when three target points overlap in the image under test, it is preferable to consider that the total number of occurrences of the first target point is 1. In other words, at this time, two or more first target points correspond to only one second target point.

[0097] Specifically, in some embodiments, the first preset value can be set to 5. Of course, the first preset value can also be set to other values ​​based on the staff's historical experience or the accuracy requirements of defect detection, such as 6 times, 9 times, etc.

[0098] In this embodiment, since the meshing of the image model to be analyzed is based on the allowable tolerance, the interval relationship of each effective target point in the same mesh cell can be determined to be a distribution with correlation. That is, multiple effective target points located in the same mesh cell can be determined to belong to the same target defect (such as a cluster).

[0099] Specifically, in some embodiments, when the number of valid target points 022 located in the same grid cell is greater than a second preset value, the corresponding grid cell is marked as a first computing unit 03, and the valid target points within that grid cell are determined as first target defect points. At this time, the first target defect point within the grid cell is considered part of the target defect, that is, one of the target defects has been located, and the grid cell (i.e., the main distribution area of ​​the target defect) is marked as a first computing unit. Subsequently, the target defect is identified as a whole based on its grid location; that is, valid target points in one or more grid cells adjacent to the first computing unit (marked as second computing units) are identified, and it is determined whether there are other valid target points belonging to the target defect in the second computing unit. If other valid target points belonging to the target defect are identified, they are marked as second target defect points, and complete information about the target defect (such as all valid target points corresponding to the target defect) is obtained based on the identified first and second target defect points.

[0100] For example, in some embodiments, a 3x3 computing method is used for the first computing unit, that is, the eight grid cells adjacent to the first computing unit are all marked as the second computing unit.

[0101] Specifically, in some embodiments, the second preset value can be set to 10. Of course, the first preset value can also be set to other values ​​based on the staff's historical experience or the accuracy requirements of defect detection, such as 5 times, 7 times, 11 times, etc.

[0102] When the distribution of the first target defect is relatively uniform (e.g., the distribution pattern of the first target defect is relatively regular, or the distribution pattern of the first target defect is symmetrical or approximately symmetrical), preferably, in some embodiments, the step of determining the second target defect point includes:

[0103] Obtain the center location of the first target defect;

[0104] Calculate the first distance between each of the effective target points 022 in the second calculation unit 04 and the center position, and when the first distance is less than a third preset value, mark the corresponding effective target point 022 as a second target defect point.

[0105] Specifically, in some embodiments, the location information of the first target defect is the absolute position (x, y), and correspondingly, the center position is the average position of each first target defect point.

[0106] Furthermore, in some embodiments, when the distance (first distance) between any valid target point in the second calculation unit and the average position is less than a third preset value, it is considered that the distribution of the valid target point and the target defect point is related, that is, the valid target point is also identified as a target defect point.

[0107] Alternatively, in some other embodiments, in order to improve computational efficiency, the center position can be directly set as the center position of the corresponding computing unit.

[0108] In other embodiments, the step of determining the second target defect point includes:

[0109] Obtain the location information of each of the first target defect points;

[0110] Based on the location information, a second distance is determined between the effective target point 022 in the second calculation unit 04 and each of the effective target points 022 (i.e., target defect points) in the first calculation unit 03;

[0111] When at least one of the second spacings is less than a fourth preset value, or when the average of the second spacings is less than a fourth preset value, the corresponding effective target point 022 in the second calculation unit 04 is marked as a second target defect point.

[0112] Specifically, in some embodiments, the third preset value and the fourth preset value can be set based on the staff's historical experience or the accuracy requirements of defect detection.

[0113] Specifically, in some embodiments, the second distance between any valid target point in the second calculation unit and the first target defect point is the average distance between the valid target point and the plurality of target defect points. Alternatively, in other embodiments, the second distance is the minimum distance between the valid target point and each of the target defect points. Or, in still other embodiments, the second distance includes: the set of distances between the corresponding valid target point and each of the first target defects.

[0114] Alternatively, in other embodiments, the target defect can be determined by calculating the pairwise distances between each effective target point in the first calculation unit and the second calculation unit. Specifically, the pairwise distances between each first target defect point and each effective target point in the second calculation unit are calculated to determine the second target defect point in the second calculation unit.

[0115] In some embodiments, when there are two or more of the first computing units 03 adjacent to each other, the grid unit with the most effective target points 022 is still marked as the first computing unit 03, and the other first computing units adjacent to the first computing unit 03 are marked as the second computing unit 04.

[0116] Specifically, such as Figure 4a As shown, when the second preset value is set to 6, the number of valid target points in both the first grid 06a and the second grid 06b is greater than the second preset value, and both are marked as the first calculation grid. In order to avoid repeated calculations, the first grid 06a, which has more valid target points, is marked as the first calculation grid, and the second grid 06b is remarked as the second calculation grid.

[0117] In some embodiments, when two or more of the first computing units are adjacent and the number of effective target points of the two or more first computing units is the same, the two or more first computing units are still marked as first computing units.

[0118] Specifically, such as Figure 4cAs shown, when the second preset value is set to 6, both the sixth grid 06f and the seventh grid 06g are marked as the first computational grid, and at this time, the number of valid target points in the sixth and seventh grids 06f and 06g is the same. In order to accurately identify target defects, the sixth grid 06f and the seventh grid 06g are still marked as the first computational grid, and the second computational grid can be appropriately divided according to the distribution of valid target points in other adjacent grids.

[0119] Furthermore, in some embodiments, it may be based on manual judgment whether the target defects in the sixth grid and the seventh grid need to be identified as the same target defect.

[0120] In some embodiments, when there is a single grid cell between two first computing units, the first computing unit corresponding to the grid cell is determined based on the distribution state of the effective target points of the separated grid cells, wherein the distribution state includes: the regional distribution trend of the effective target points on the grid cell.

[0121] Furthermore, in some embodiments, the regional distribution tends to include: clustered distribution, dispersed distribution, etc.

[0122] Specifically, in some embodiments, such as Figure 4b As shown, when the third grid 06c and the fourth grid 06d are both designated as the first computational grid, and there is a fifth grid 06e between the third and fourth grids, the distribution of the effective target points in the fifth grid 06e determines whether the fifth grid 06e is the second computational grid corresponding to the third grid 06c or the fourth grid 06d.

[0123] For example, in some embodiments, the affiliation of the fifth grid is determined based on the average position of multiple valid target points in the fifth grid and the distance between the fifth and fourth grids. For example, when the regional distribution tends to be clustered, the center position of the clustered distribution (i.e., the average position of the valid target points) is calculated. If the average position of the valid target points in the fifth grid is closer to the fourth grid, then the fifth grid 06e is marked as the second calculation grid of the fourth grid 06d.

[0124] like Figure 4b As shown, the main effective target points of the fifth grid 06e are distributed in the lower right corner area, which is close to the fourth grid 06d. Therefore, the fifth grid 06e is defined as the second computational grid corresponding to the fourth grid 06d.

[0125] Alternatively, in some other embodiments, when the regional distribution tends to be dispersed, the fifth grid 06e can be further divided into two grids along the diagonal, which are respectively labeled as the second computational grids corresponding to the third grid 06c and the fourth grid 06d.

[0126] In some embodiments, the location information is the absolute position of the valid target point, such as (x, y).

[0127] In this embodiment, the effective target points are characterized by grid positions (such as the 3rd row and 4th column). This allows for a quick and clear division of the positions of effective target points located in the critical region (such as effective target points that are at the same or similar distance to the two target defects), avoiding the problem of ambiguous position division of such effective target points during the grid division process.

[0128] Example 2

[0129] Based on the unitized mask defect identification method in Embodiment 1 above, the present invention also provides a corresponding unitized mask defect identification device, such as... Figure 5 As shown, it includes:

[0130] The image acquisition module 02 is configured to prepare a wafer based on a mask and acquire multiple images to be tested based on the wafer, wherein at least one of the images to be tested includes at least one first target point;

[0131] The image model determination module 04 is configured to obtain an image model to be analyzed based on multiple images to be tested, and the image model to be analyzed includes: a second target point formed by the first target point;

[0132] The effective target point determination module 06 is configured to obtain effective target points based on the second target point;

[0133] The mesh division module 08 is configured to perform mesh division on the image model to be analyzed to obtain several mesh units;

[0134] The effective target point classification module 10 is configured to obtain the grid position of the effective target point based on the grid cell, and classify the effective target point based on the grid position;

[0135] The target defect determination module 12 is configured to determine the target defect based on the classified valid target points.

[0136] Furthermore, in some embodiments, the target defect determination module includes:

[0137] The first target defect point marking unit is configured to mark the corresponding grid unit as a first calculation unit and mark the effective target point in the first calculation unit as a first target defect point when the number of valid target points with the same grid position is greater than a second preset value.

[0138] The second computing unit marking unit is configured to mark at least one of the grid cells adjacent to the first computing unit as the second computing unit;

[0139] The target defect determination unit is configured to determine a second target defect point based on the distance between the first target defect point and each of the effective target points in the second calculation unit, and to determine the target defect based on the first and second target defect points.

[0140] The identification device provided in this embodiment quickly and efficiently classifies valid target points by their row and column coordinates (i.e., grid positions), thereby obtaining a set of valid target points identified as target defects, thus achieving the localization of target defects. Furthermore, it performs targeted identification of the discovered target defects, improving the efficiency of defect identification.

[0141] Furthermore, in some embodiments, the grid position is (int(x / G), int(y / G)), where (x, y) is the absolute position of the effective target point (022), and G is the side length of the grid cell.

[0142] Furthermore, in some embodiments, the effective target point determination module includes:

[0143] The occurrence frequency determination unit is configured to obtain the occurrence frequency of the second target point, wherein the occurrence frequency is the number of first target points corresponding to the second target point;

[0144] The effective target point marking (determination) unit is configured to mark the corresponding second target point as an effective target point when the occurrence frequency of the second target point is greater than a first preset value.

[0145] Furthermore, in some embodiments, the target defect determination unit includes:

[0146] The first subunit is configured to obtain the center position of the first target defect point;

[0147] The second subunit is configured to calculate the first distance between each of the effective target points (022) in the second calculation unit (04) and the center position, and when the first distance is less than a third preset value, mark the corresponding effective target point (022) as the second target defect point.

[0148] Alternatively, in some embodiments

[0149] The third subunit is configured to acquire the location information of each of the first target defect points;

[0150] The fourth subunit is configured to determine the second distance between the effective target point (022) in the second calculation unit (04) and each of the first target defect points based on the location information; and when at least one of the second distances is less than a fourth preset value, or when the average value of the second distances is less than the fourth preset value, the corresponding effective target point (022) in the second calculation unit (04) is marked as a second target defect point.

[0151] Furthermore, in some embodiments, the first target defect marking unit is also configured to, when there are two or more first computing units adjacent to each other, still mark the grid unit with the most effective target points as the first computing unit, and mark the other first computing units adjacent to the first computing unit as the second computing unit.

[0152] Furthermore, in some embodiments, the first target defect marking unit is also configured to mark the two or more first computing units as first computing units when there are two or more adjacent first computing units and the number of effective target points of the two or more first computing units is the same.

[0153] Example 3

[0154] A third aspect of the present invention is to provide an electronic device, including a memory 502, a processor 501, and a computer program stored in the memory 502 and executable on the processor 501, wherein the processor 501 executes the program to implement the steps of the method described above. For ease of explanation, only the parts related to the embodiments of this specification are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of this specification. This electronic device can be any electronic device, including PCs, cloud servers, and even mobile phones, tablets, PDAs (Personal Digital Assistants), POS (Point of Sales) terminals, in-vehicle computers, desktop computers, etc.

[0155] Specifically, Figure 6This is a block diagram illustrating the structural composition of an electronic device according to an exemplary embodiment of the present invention. Bus 500 may include any number of interconnected buses 500 and bridges, linking various circuits including one or more processors represented by processor 501 and memory represented by memory. Bus 500 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be further described herein. Communication interface 503 provides an interface between the bus and receiver and / or transmitter 504, which may be separate independent receivers or transmitters 504 or a single element such as a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 501 is responsible for managing bus 500 and general processing, while memory 502 may be used to store data used by processor 501 during operation.

[0156] The computer-readable storage medium may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0157] Program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0158] The aforementioned computer-readable medium carries one or more programs, which, when executed by a device, cause the computer-readable medium to perform the following functions: prepare a wafer based on a mask, and acquire multiple images to be tested based on the wafer, wherein at least one of the images to be tested includes at least one first target point; obtain an image model to be analyzed based on the multiple images to be tested, wherein the image model to be analyzed includes a second target point corresponding to the first target point; acquire valid target points based on the second target points; divide the image model to be analyzed into a grid to obtain a plurality of grid cells; acquire the grid position of the valid target points based on the grid cells, and classify the valid target points based on the grid position; and determine the target defect based on the classified valid target points. The step of determining the target defect includes: when the number of valid target points with the same grid position is greater than a second preset value, marking the corresponding grid cell as a first computing unit and marking the valid target points within the first computing unit as first target defect points; marking at least one grid cell adjacent to the first computing unit as a second computing unit; determining a second target defect point based on the distance between the first target defect point and each of the valid target points within the second computing unit, and determining the target defect based on the first and second target defect points.

[0159] Those skilled in the art will understand that the above modules can be distributed in the device as described in the embodiments, or they can be modified accordingly and placed in one or more devices that are unique to this embodiment. The modules in the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0160] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a computer terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0161] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0162] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for identifying defects in a modularized photomask, characterized in that, Including the following steps: A wafer is prepared based on a mask, and multiple images to be tested (01) are obtained based on the wafer, wherein at least one of the images to be tested (01) includes at least one first target point (011). An image model (02) to be analyzed is obtained based on multiple images to be tested, and the image model (02) to be analyzed includes: a second target point (021) formed by the first target point (011); Based on the second target point (021), obtain the effective target point (022); The image model (02) to be analyzed is divided into several grid cells; The grid position of the effective target point (022) is obtained based on the grid cell, and the effective target point (022) is classified based on the grid position; Based on the classified valid target points (022), the target defect (05) is determined. The steps for determining the target defect (05) include: When the number of valid target points with the same grid position is greater than a second preset value, the corresponding grid cell is marked as a first calculation unit, and the valid target points in the first calculation unit are marked as first target defect points; At least one of the grid cells adjacent to the first computing unit is marked as a second computing unit; The second target defect point is determined based on the distance between the first target defect point and each of the effective target points in the second calculation unit, and the target defect is determined based on the first and second target defect points; The step of obtaining a valid target point (022) based on the second target point (021) includes: The occurrence frequency of the second target point (021) is obtained, wherein the occurrence frequency is the number of the first target points (011) corresponding to the second target point (021); When the occurrence frequency of the second target point (021) is greater than the first preset value, the corresponding second target point (021) is marked as a valid target point (022).

2. The identification method according to claim 1, characterized in that, The steps for meshing the image model to be analyzed include: The allowable deviation is determined based on the accuracy of the image to be tested; The image model to be analyzed is divided into grids based on the allowable deviation.

3. The identification method according to claim 1, characterized in that, The grid position is (int(x / G), int(y / G)), where (x, y) is the absolute position of the effective target point (022), and G is the side length of the grid cell.

4. The identification method according to claim 1, the step of determining the second target defect point includes: Obtain the center position of the first target defect point; Calculate the first distance between each of the effective target points (022) in the second calculation unit (04) and the center position, and when the first distance is less than a third preset value, mark the corresponding effective target point (022) as the second target defect point.

5. The identification method according to claim 1, characterized in that, The steps for determining the second target defect include: Obtain the location information of each of the first target defect points; Based on the location information, a second distance is determined between the effective target point (022) in the second calculation unit (04) and each of the first target defect points; When at least one of the second spacings is less than the fourth preset value, or when the average of the second spacings is less than the fourth preset value, the corresponding effective target point (022) in the second calculation unit (04) is marked as the second target defect point.

6. The identification method according to claim 1, characterized in that, When there are two or more of the first computing units (03) adjacent to each other, the grid unit with the most effective target points (022) is still marked as the first computing unit (03), and the other first computing units adjacent to the first computing unit (03) are marked as the second computing unit (04). And / or, When two or more of the first computing units are adjacent and the number of effective target points of the two or more first computing units is the same, the two or more first computing units are still marked as first computing units.

7. The identification method according to claim 1, characterized in that, When there is a single grid cell between two first computing units, the first computing unit corresponding to the grid cell is determined based on the distribution state of the effective target points of the separated grid cells, wherein the distribution state includes: the regional distribution trend of the effective target points on the grid cell.

8. The identification method according to claim 5, characterized in that, The location information is the absolute location of the valid target point.

9. A device for identifying defects in a modularized photomask, characterized in that, include: The image acquisition module is configured to prepare a wafer based on a mask and acquire multiple images to be tested based on the wafer, wherein at least one of the images to be tested includes at least one first target point; The image model determination module is configured to obtain an image model to be analyzed based on multiple images to be tested, and the image model to be analyzed includes: a second target point formed by the first target point; The effective target point determination module is configured to obtain effective target points based on the second target point; The meshing module is configured to divide the image model to be analyzed into several mesh units. The effective target point classification module is configured to obtain the grid position of the effective target point based on the grid cell, and classify the effective target point based on the grid position; The target defect determination module is configured to determine the target defect based on the classified valid target points; The target defect determination module includes: The first target defect point marking unit is configured to mark the corresponding grid unit as a first calculation unit and mark the effective target point in the first calculation unit as a first target defect point when the number of valid target points with the same grid position is greater than a second preset value. The second computing unit marking unit is configured to mark at least one of the grid cells adjacent to the first computing unit as the second computing unit; The target defect determination unit is configured to determine a second target defect point based on the distance between the first target defect point and each of the effective target points in the second calculation unit, and to determine the target defect based on the first and second target defect points; The effective target point determination module includes: The occurrence frequency determination unit is configured to obtain the occurrence frequency of the second target point, wherein the occurrence frequency is the number of first target points corresponding to the second target point; The valid target point marking unit is configured to mark the corresponding second target point as a valid target point when the occurrence frequency of the second target point is greater than a first preset value.

Images