Mask detection methods and apparatus, computer-readable storage media, computer program products, terminals

Abstract

A method and apparatus for mask detection, a computer-readable storage medium, a computer program product, and a terminal are disclosed. The method includes: acquiring a target mask, which is a mask after optical proximity correction; acquiring a photomask image, which is an image of a mask plate fabricated based on the target mask; and detecting the consistency between the photomask image and the target mask based on image differences between the photomask image and the target mask. This disclosure enables two-dimensional quality control of the mask, thereby improving the quality of mask fabrication.

Description

Technical Field

[0001] This invention relates to the field of integrated circuit technology, and more specifically to a mask detection method and apparatus, a computer-readable storage medium, a computer program product, and a terminal. Background Technology

[0002] The integrated circuit manufacturing process mainly includes three stages: chip design, chip manufacturing, and packaging and testing. In the chip design stage, specialized software is used to design circuit diagrams, describing the physical design of the integrated circuit, such as multiple components and their interconnections. In the chip manufacturing stage, layers of different materials are deposited on the wafer surface. The manufacturing process for each layer includes creating a corresponding mask (also called a photomask) based on the integrated circuit layout design and fabricating a photomask stencil (also called a photomask, photomask, etc.). The pattern on the photomask is transferred to a photoresist pre-coated on the surface of the layer using photolithography. Subsequent etching, doping, and other processes then produce the physical structure of the integrated circuit at that layer.

[0003] Traditional photomask manufacturing processes generally include exposure, development, removal of photoresist, and photolithography. During this process, the designed circuit pattern (i.e., the mask) is projected onto the photoresist through an exposure system. Due to imperfections in the optical system and diffraction effects, the pattern projected onto the photoresist is not entirely identical to the pattern on the mask. If these distortions are not corrected, they can significantly alter the electrical performance of the manufactured circuit, as the circuit is based on a distorted mask. To address this, on one hand, Optical Proximity Correction (OPC) technology is used to correct the pattern on the mask, ensuring that the pattern projected onto the photoresist conforms as closely as possible to the design requirements. On the other hand, photomask manufacturers also perform critical dimension (also known as line diameter) measurements on the manufactured mask to control its quality. Specifically, during mask creation, spatial tolerances (i.e., critical dimensions) between components or interconnects are defined to ensure that components or interconnects do not interact with each other in undesirable ways. Photomask manufacturers measure the line diameter at corresponding locations in the pattern on the mask to ensure it meets expectations.

[0004] Currently, measurement techniques such as Critical Dimension Scanning Electron Microscope (CD-SEM) are commonly used to measure wire diameter. However, due to the nature of these techniques, only one-dimensional measurements can be performed on the pattern on the mask. In reality, the pattern on the mask (especially after OPC processing) has a complex two-dimensional geometry and is irregularly distributed. Simple one-dimensional measurement cannot accurately characterize the mask quality and is not conducive to quality control of the mask. Summary of the Invention

[0005] The technical problem solved by this invention is how to improve the quality control of masks.

[0006] To address the aforementioned technical problems, embodiments of the present invention provide a mask detection method, comprising: acquiring a target mask, wherein the target mask is a mask after optical proximity correction; acquiring a photomask image, wherein the photomask image is an image of a mask plate made based on the target mask; and detecting the consistency between the photomask image and the target mask based on the image differences between the photomask image and the target mask.

[0007] Optionally, the detection method further includes: adjusting the value of at least one parameter used in optical proximity correction based on the image differences to obtain an updated target mask.

[0008] Optionally, detecting the consistency between the photomask image and the target mask based on the image difference between the photomask image and the target mask includes: overlapping the photomask image and the target mask to obtain an overlapping image, the overlapping image being divided into multiple segments according to the pattern contour of the target mask; for each segment, calculating the image difference degree between the pattern of the photomask image and the pattern of the target mask in the segment; and detecting the consistency between the photomask image and the target mask based on the image difference degree of the multiple segments.

[0009] Optionally, acquiring the target mask includes: acquiring a region image of at least one first preset region of the target mask; acquiring the photomask image includes: acquiring a region image of at least one second preset region of the photomask image, wherein the at least one first preset region and the at least one second preset region correspond one-to-one; overlapping the photomask image and the target mask to obtain an overlapping image includes: for each first preset region, overlapping the region image of the first preset region and the region image of the corresponding second preset region to obtain a corresponding overlapping region image; summing up at least one overlapping region image to obtain the overlapping image.

[0010] Optionally, the step of detecting the consistency between the photomask image and the target mask based on the image difference of the multiple segments includes: statistically analyzing the numerical distribution of the image difference of the multiple segments; and detecting the consistency between the photomask image and the target mask based on the statistical results.

[0011] Optionally, the more dispersed the numerical distribution of the image difference degree of the multiple segments and the further away the peak is from the baseline, the worse the consistency between the photomask image and the target mask. The baseline is used to characterize that the image difference degree is zero.

[0012] To address the aforementioned technical problems, embodiments of the present invention also provide a mask detection device, comprising: a first acquisition module for acquiring a target mask, wherein the target mask is a mask after optical proximity correction; a second acquisition module for acquiring a photomask image, wherein the photomask image is an image of a mask plate made based on the target mask; and a detection module for detecting the consistency between the photomask image and the target mask based on the image differences between the photomask image and the target mask.

[0013] To address the aforementioned technical problems, embodiments of the present invention also provide a computer-readable storage medium, which is a non-volatile or non-transient storage medium storing a computer program thereon. When the computer program is run by a processor, it executes the steps of the above-described method.

[0014] To address the aforementioned technical problems, embodiments of the present invention also provide a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the above-described method.

[0015] To address the aforementioned technical problems, this invention also provides a terminal, including a memory and a processor. The memory stores a computer program that can run on the processor, and the processor executes the steps of the above-described method when running the computer program.

[0016] Compared with the prior art, the technical solution of the embodiments of the present invention has the following beneficial effects:

[0017] This invention provides a mask detection method, comprising: acquiring a target mask, wherein the target mask is a mask after optical proximity correction; acquiring a photomask image, wherein the photomask image is an image of a mask plate made based on the target mask; and detecting the consistency between the photomask image and the target mask based on the image difference between the photomask image and the target mask.

[0018] Compared to existing technologies that only measure one-dimensional wire diameter data as the basis for mask quality control and cannot fully reflect mask quality, this implementation scheme achieves two-dimensional quality control of the mask by detecting the image difference between the pattern on the target mask and the actual pattern formed on the mask plate, thereby helping to improve the mask manufacturing quality.

[0019] Furthermore, the photomask image and the target mask are overlaid to obtain an overlaid image. This overlaid image is divided into multiple segments based on the pattern outline of the target mask. For each segment, the image difference between the pattern of the photomask image and the pattern of the target mask within that segment is calculated. The numerical distribution of the image difference across the multiple segments is statistically analyzed. The consistency between the photomask image and the target mask is then detected based on the statistical results. Thus, by combining image cropping with statistical mathematics, the specific degree of difference between the patterns of the photomask image and the target mask in each segment is intuitively and comprehensively characterized based on the statistical results. The statistical results can serve as one of the bases for mask quality control, helping to optimize mask fabrication. Attached Figure Description

[0020] Figure 1 This is a flowchart of a mask detection method according to an embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram of a typical application scenario of an embodiment of the present invention;

[0022] Figure 3 yes Figure 1 A flowchart of a specific implementation of step S103;

[0023] Figure 4 yes Figure 2 The image difference distribution diagram of the overlapping region shown;

[0024] Figure 5 This is a schematic diagram of the structure of a mask detection device according to an embodiment of the present invention. Detailed Implementation

[0025] As mentioned in the background section, OPC, as a photolithography resolution enhancement technique, has been widely used in integrated circuit manufacturing processes. Specifically, OPC adjusts the amplitude of light transmitted through a photolithographic mask by modifying the mask layout design data used to create the mask. For example, the edges in the mask layout design can be adjusted to make certain portions of the geometry larger or smaller, depending on how much additional exposure (or lack thereof) is expected at certain points on the substrate.

[0026] By checking whether the line diameter of the pattern on the photomask meets expectations, the quality of the photomask can be controlled. For example, based on the measurement results of the line diameter, various parameters during OPC processing of the photomask can be adjusted to optimize the pattern on the fabricated photomask, so that the pattern projected onto the photosensitive emulsion using the photomask is as close as possible to the pattern on the photomask, thereby avoiding distortion of the pattern on the final photomask.

[0027] Existing measurement techniques have limitations, only allowing for one-dimensional measurement of line diameters on the pattern on the mask. The inventors of this application discovered that defects found during testing on the final integrated circuit are two-dimensional patterns, while all line diameter measurements in one dimension of the area containing these patterns on the mask meet the requirements. Therefore, existing methods of measuring mask quality solely through one-dimensional line diameter measurements cannot meet the quality control requirements for masks.

[0028] To address the aforementioned technical problems, embodiments of the present invention provide a mask detection method, comprising: acquiring a target mask, wherein the target mask is a mask after optical proximity correction; acquiring a photomask image, wherein the photomask image is an image of a mask plate made based on the target mask; and detecting the consistency between the photomask image and the target mask based on the image differences between the photomask image and the target mask.

[0029] This implementation scheme achieves two-dimensional quality control of the mask by detecting the image difference between the pattern on the target mask and the pattern obtained by projecting the target mask onto the wafer surface, thereby helping to improve the mask manufacturing quality.

[0030] To make the above-mentioned objectives, features and beneficial effects of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0031] Figure 1 This is a flowchart of a mask detection method according to an embodiment of the present invention.

[0032] This implementation plan can be applied to chip design and manufacturing scenarios. Specifically, it can be applied to mask fabrication, where quality control of the masks can be achieved by implementing this plan.

[0033] In practice, the detection method provided in steps S101 to S103 below can be executed by a terminal. The terminal can be, for example, a computer, a server, or other such device.

[0034] For details, please refer to Figure 1 The mask detection method described in this embodiment may include the following steps:

[0035] Step S101: Obtain the target mask, which is a mask after optical proximity correction;

[0036] Step S102: Obtain a photomask image, wherein the photomask image is an image of a mask plate made based on the target mask;

[0037] Step S103: Detect the consistency between the photomask image and the target mask based on the image difference between the photomask image and the target mask.

[0038] In practical applications, steps S101 and S102 can be executed synchronously or asynchronously. When executed asynchronously, their execution order can be interchanged.

[0039] More specifically, combined Figure 2 The black wireframe exemplifies the target graphic of an integrated circuit layout design plan, upon which a mask is created using tools such as Electronic Design Automation (EDA). Furthermore, EDA integrates an OPC model. Figure 2 The blue-gray filled area exemplifies a portion of the target mask output after processing by the OPC model. Red dots represent convex corner fragments, blue dots represent concave corner fragments, and green dots represent ripple fragments.

[0040] The target mask can be pre-created and stored in a database. During step S101, the terminal can access the database to retrieve the target mask. In some embodiments, the database can be stored locally on the terminal, or it can be set independently of the terminal, for example, stored in the cloud.

[0041] Furthermore, a photomask can be fabricated based on the target mask. For example, quartz glass can be used as a substrate, on which a layer of metallic chromium and photosensitive emulsion are deposited to form a photosensitive material. The target mask is exposed onto the photosensitive emulsion using an electron laser device. The exposed area is developed, forming a circuit pattern on the metallic chromium. The projected circuit is then photo-etched using an integrated circuit lithography machine to obtain the photomask. After exposure, or after photo-etching, an image of the photomask surface can be acquired using a method such as SEM to obtain a photomask image.

[0042] Ideally, the pattern on the mask shown by the photomask image should be completely consistent with the pattern of the target mask. However, in practical applications, due to factors such as manufacturing process and materials, the pattern outlines of the photomask image and the target mask will differ to some extent. Figure 2 The red dashed line in the middle exemplarily shows a portion of the pattern outline of the photomask image.

[0043] After acquisition, the photomask image can undergo optimization processing such as energy, focal length, and distortion correction to improve its sharpness and image accuracy. In some embodiments, multiple photomask images can be acquired and averaged to obtain the final photomask image. The averaging operation can, for example, involve iterating through all pixels in the image and, for each pixel, averaging the pixel values ​​of the multiple photomask images at that pixel to obtain the pixel value of that pixel in the final photomask image.

[0044] Furthermore, after the photomask is projected onto the wafer surface via an exposure system, for example, onto a photoresist layer coated on the wafer surface, an image of the wafer surface can be acquired using methods such as SEM to obtain a wafer image. Ideally, the circuit pattern on the wafer fabricated based on the photomask (i.e., the wafer image) should fit the target pattern as closely as possible. Figure 2 The pattern is shown within the black outline. The greater the deviation between the aforementioned photomask image and the target mask pattern outline, the greater the deviation between the wafer image and the target pattern.

[0045] refer to Figure 2 The pattern contours of the photomask image and the target mask do not completely overlap, which affects the electrical performance of the final fabricated chip. Figure 2 Taking region W as an example, the minimum distance D between adjacent pattern outlines in this region is a crucial monitoring indicator. If the minimum distance D is too small, it means the patterns are too close together, which could cause components or interconnections to connect unexpectedly, leading to circuit failure. However, existing one-dimensional line diameter measurement methods can only measure dimensions in the x or y direction (as shown by line diameters C1 and C2 in the figure), and cannot directly measure the minimum distance D. Even if the values ​​of line diameters C1 and C2 meet the requirements, if the minimum distance D is too small, the photomask image obtained based on the target mask projection will still be unqualified.

[0046] This disclosed solution is tested by performing step S103. Figure 2 The difference between the pattern contours of the photomask image and the target mask in region W is significant. If the pattern contours of the photomask image at both corners of region W protrude outwards from the pattern contours of the target mask, it means that the minimum distance D is clearly less than the expected requirement. Therefore, based on the difference detection results between the photomask image and the target mask, the mask pattern quality can be intuitively analyzed in two dimensions, making the originally one-dimensional and unmeasurable minimum distance D indirectly measurable.

[0047] The specific implementation of step S103 will be explained in detail below.

[0048] In a specific implementation, refer to Figure 3 Step S103 may include the following steps:

[0049] Step S1031: Overlay the photomask image and the target mask to obtain an overlapping image, wherein the overlapping image is divided into multiple segments according to the pattern contour of the target mask;

[0050] Step S1032: For each segment, calculate the image difference between the pattern of the photomask image and the pattern of the target mask in the segment;

[0051] Step S1033: Detect the consistency between the photomask image and the target mask based on the image difference of the multiple segments.

[0052] Specifically, please refer to Figure 2 For any color dot among green, blue, and red, the pattern outline between two adjacent dots can be considered as a segment. For each segment, the pattern outline of the wafer pattern at that segment and the pattern outline of the target mask at that segment are superimposed to obtain the superimposed image for that segment, such as... Figure 2 The segments s1 and s2 are shown in the diagram.

[0053] Furthermore, image difference can be used to characterize the degree of non-overlap of patterns, that is, the degree of deviation of the pattern outline of the photomask image from the pattern outline of the target mask.

[0054] In some embodiments, the photomask image and the target mask are presented at the same image magnification. In step S1031, the photomask image and the target mask are superimposed with a preset coordinate point as a reference to obtain an overlapping image.

[0055] Specifically, preset coordinate points, image magnification, and OPC Monitor Pattern (OMP) can be defined together in a mask critical dimension table (CD on Mask table, CDM table). The mask manufacturer acquires mask images based on the preset coordinate points and image magnification marked in the CDM table. For example, the preset coordinate point can be the center point of the image. When acquiring mask images, the preset coordinate point is used as the center point, and the image is acquired according to the image magnification marked in the CDM table.

[0056] In response to acquiring a photomask image and a target mask acquired at the same image magnification, the two images are overlaid with the center point as a reference to obtain an overlaid image.

[0057] Furthermore, the CDM table may also include a target mask so that the acquirer of the photomask image (e.g., the photomask manufacturer) can check whether the acquired photomask image meets the requirements.

[0058] In one specific implementation, step S101 may include: acquiring a region image of at least one first preset region of the target mask; step S102 may include: acquiring a region image of at least one second preset region of the photomask image, wherein the at least one first preset region and the at least one second preset region correspond one-to-one.

[0059] Specifically, the first preset area can be a risk point on the target mask, i.e., an area requiring special attention. In some embodiments, the risk points on the target mask can be determined based on the locations of defects detected on integrated circuits manufactured using the target mask in the past, for example... Figure 2 The central region W can be identified as the first preset region. Furthermore, the first preset region can be determined comprehensively by combining this with the Mask Rule Check (MRC) used during mask creation.

[0060] Furthermore, after determining the first preset region, the preset coordinate points and image magnification of the first preset region can be recorded in the CDM table. When acquiring photomask images, the image acquired according to the preset coordinate points and image magnification is the region image of the second preset region. In other words, the CDM table can record at least one preset coordinate point and image magnification of the first preset region. Correspondingly, when acquiring photomask images, at least one preset coordinate point can be traversed to perform image acquisition operations respectively, so as to obtain the region image of at least one second preset region.

[0061] Further, step S1031 may include: for each first preset region, overlapping the region image of the first preset region and the region image of the corresponding second preset region to obtain a corresponding overlapping region image; summing up at least one of the overlapping region images to obtain the overlapping image.

[0062] For example, Figure 2 An example is shown of the overlapping region image obtained by overlaying the region images of the first preset region and the corresponding second preset region.

[0063] In some embodiments, after obtaining the images of each overlapping region, the image difference between the pattern contour of the target mask and the pattern contour of the photomask image in each overlapping region image can be detected separately, and then the detection results can be summarized.

[0064] Alternatively, at least one overlapping region image can be stitched together to obtain a complete overlapping image, and then the image difference between the pattern contour of the target mask and the pattern contour of the photomask image in the overlapping image can be detected to obtain the detection result.

[0065] In one specific implementation, in step S1032, the center of the segmentation point (e.g., the midpoint of the segment) can be determined, and the deviation distance between the pattern contour of the target mask and the pattern contour of the photomask image at the center of the segmentation point can be calculated.

[0066] For example, continue to refer to Figure 2The image difference between the target mask and the photomask image at the segmentation point a1 of segment s1 is denoted as A1. An upright triangle indicates that the image difference A1 is negative, meaning the pattern outline of the photomask image at a1 deviates inward by a distance |A1| compared to the pattern outline of the target mask at a1. Here, || represents the absolute value. The segmentation point a1 can be the midpoint of the line connecting the two vertices (red and green dots) of segment s1. Similarly, the image difference between the target mask and the photomask image at the segmentation point a2 of segment s2 is denoted as A2. An inverted triangle indicates that the image difference A2 is positive, meaning the pattern outline of the photomask image at a2 deviates outward by a distance |A2| compared to the pattern outline of the target mask at a2. The segmentation point a2 can be the midpoint of the line connecting the two vertices (green and green dots) of segment s2.

[0067] In one specific implementation, step S1033 may include: statistically analyzing the numerical distribution of the image differences among the multiple segments; and detecting the consistency between the photomask image and the target mask based on the statistical results.

[0068] Still with Figure 2 Taking the overlapping region image shown as an example, by summarizing the image difference detection data of each segment in the image, we can obtain the following: Figure 4 The numerical distribution is shown in the figure. Figure 4 In the diagram, the horizontal axis represents the image difference degree, and the vertical axis represents the counting results. The number of segments whose image difference degree falls within each of the following value ranges can be counted: [-2.5, -2.0), [-2.0, -1.5), [-1.5, -1.0), [-1.0, -0.5), [-0.5, 0.0), [0.0, 0.5), [0.5, 1.0), [1.0, 1.5), [1.5, 2.0), [2.0, 2.5), [2.5, 3.0]. In this embodiment, the unit of distance is nanometers (nm).

[0069] Furthermore, the more dispersed the numerical distribution of the image difference in the multiple segments, and the further the peak is from the baseline, the worse the consistency between the photomask image and the target mask. The baseline is used to characterize an image difference of zero. (Continue to refer to...) Figure 4 The baseline can be determined by setting the position where the image difference equals 0.0. Figure 4 The numerical distribution shown is close to a normal distribution, and the range of the peak value [-0.5, 0.0) is close to the baseline. Therefore, it can be determined that the consistency between the photomask image and the target mask is generally good.

[0070] Further reference Figure 4 It can be seen that a certain number of image differences fall into the ranges [1.0, 1.5) and [1.5, 2.0), which are far from the baseline. Figure 2It can be seen that the image difference falling within these two value ranges is precisely Figure 2 Image difference at the two corners of region W.

[0071] In one specific implementation, the detection result of step S103 can be used as one of the mask quality control specifications to help improve the mask manufacturing quality. Specifically, after step S103, this implementation may further include the step of adjusting the value of at least one parameter used in optical proximity correction according to the image differences to obtain an updated target mask.

[0072] Specifically, at least one parameter can be a parameter used in the OPC model. By adjusting the value of at least one parameter, the amount of correction to the mask layout design data can be adjusted, thereby changing the pattern profile of the updated target mask.

[0073] For example, through Figure 4 The statistical results shown can intuitively and accurately determine Figure 2 The mask at region W needs to be optimized. Accordingly, the value of at least one parameter used by the OPC model when performing OPC processing on the mask at region W can be adjusted to adjust the pattern profile of the updated target mask at region W.

[0074] Furthermore, the detection process and the process of updating the target mask based on the detection results described in this embodiment can be performed iteratively. For example, after one round of operation, the updated target mask and the photomask image of the mask plate made based on the updated target mask are... Figure 2 If the image differences at the two corners of region W in the central region are reduced to 1.2 and 0.8 respectively, then another round of operation can be performed to further reduce the differences between the target mask and photomask images by continuing to adjust the parameters of the OPC model. Figure 2 Image difference at region W in the middle.

[0075] Therefore, by adopting this implementation scheme, the quality control of the mask can be achieved in two dimensions by detecting the image difference between the pattern on the target mask and the pattern actually formed on the mask plate, so as to help improve the production quality of the mask.

[0076] In one variation, the image differences between the target pattern and the wafer image can be compared, and the degree of distortion of the pattern on the mask relative to the target mask can be determined by working backward from the image differences between the two. This can also achieve quality control of the mask.

[0077] Figure 5 This is a schematic diagram of the structure of a mask detection device 5 according to an embodiment of the present invention. Those skilled in the art will understand that the mask detection device 5 described in this embodiment can be used to implement the above-described... Figures 1 to 4 The method described in the embodiments is a technical solution.

[0078] Specifically, refer to Figure 5 The mask detection device 5 described in this embodiment may include: a first acquisition module 51, used to acquire a target mask, the target mask being a mask after optical proximity correction; a second acquisition module 52, used to acquire a photomask image, the photomask image being an image of a mask plate made based on the target mask; and a detection module 53, used to detect the consistency between the photomask image and the target mask based on the image difference between the photomask image and the target mask.

[0079] For more information on the working principle and operation mode of the mask detection device 5, please refer to the above. Figures 1 to 4 The relevant descriptions in the text will not be repeated here.

[0080] Furthermore, embodiments of the present invention also disclose a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor as described above. Figures 1 to 4 The method described in any of the illustrated embodiments is a technical solution. Preferably, the computer-readable storage medium may include computer-readable storage media such as non-volatile memory or non-transitory memory. The computer-readable storage medium may include ROM, RAM, magnetic disk, or optical disk, etc.

[0081] Furthermore, embodiments of the present invention also disclose a terminal, including a memory and a processor, wherein the memory stores a computer program capable of running on the processor, and the processor executes the above-described... Figures 1 to 4 The method described in any of the illustrated embodiments is a technical solution. Specifically, the terminal can be a computer, server, or other device.

[0082] Furthermore, embodiments of the present invention also disclose a computer program product, including a computer program / instructions, which, when executed by a processor, implement the above-described... Figures 1 to 4 The method described in any of the embodiments shown is a technical solution.

[0083] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A method of detecting a mask, characterized by, The method comprises: obtaining a target mask, the target mask being a mask after optical proximity correction; obtaining a mask image, the mask image being an image of a mask plate made based on the target mask; detecting consistency of the mask image and the target mask according to image difference between the mask image and the target mask; wherein the detecting consistency of the mask image and the target mask according to the image difference between the mask image and the target mask comprises: overlapping the mask image and the target mask to obtain an overlapping image, the overlapping image being divided into a plurality of segments according to a pattern profile of the target mask; for each segment, calculating image difference degree between a pattern of the mask image and a pattern of the target mask in the segment; detecting consistency of the mask image and the target mask according to the image difference degrees of the plurality of segments; the obtaining of the target mask comprises: obtaining a region image of at least one first preset region of the target mask; the obtaining of the mask image comprises: obtaining a region image of at least one second preset region of the mask image, the at least one first preset region and the at least one second preset region corresponding to each other; the overlapping of the mask image and the target mask to obtain the overlapping image comprises: for each first preset region, overlapping the region image of the first preset region and the region image of the corresponding second preset region to obtain a corresponding overlapping region image; summarizing at least one overlapping region image to obtain the overlapping image.

2. The detection method according to claim 1, characterized in that, The method further comprises: adjusting a value of at least one parameter used in optical proximity correction according to the image difference to obtain an updated target mask.

3. The method of claim 1, wherein The detecting of consistency of the mask image and the target mask according to the image difference degrees of the plurality of segments comprises: statistically analyzing value distribution of the image difference degrees of the plurality of segments; detecting consistency of the mask image and the target mask according to the statistical result.

4. The detection method according to claim 3, characterized in that, The more dispersed the value distribution of the image difference degrees of the plurality of segments and the farther the peak value from a baseline, the worse the consistency of the mask image and the target mask, wherein the baseline is used to represent that the image difference degree is zero.

5. A mask inspection apparatus, characterized by, The method comprises: a first obtaining module, configured to obtain a target mask, the target mask being a mask after optical proximity correction; a second obtaining module, configured to obtain a mask image, the mask image being an image of a mask plate made based on the target mask; a detecting module, configured to detect consistency of the mask image and the target mask according to image difference between the mask image and the target mask; wherein the detecting module performs the following steps: overlapping the mask image and the target mask to obtain an overlapping image, the overlapping image being divided into a plurality of segments according to a pattern profile of the target mask; for each segment, calculating image difference degree between a pattern of the mask image and a pattern of the target mask in the segment; detecting consistency of the mask image and the target mask according to the image difference degrees of the plurality of segments; the detecting module performs the following steps: The first obtaining module performs the following step: obtaining a region image of at least one first preset region of the target mask; The second obtaining module performs the following step: obtaining a region image of at least one second preset region of the mask image, the at least one first preset region and the at least one second preset region corresponding one by one; The step of superimposing the mask image and the target mask to obtain an overlap image comprises: for each of the first preset regions, superimposing the region image of the first preset region and the region image of the corresponding second preset region to obtain a corresponding overlap region image; and collecting at least one of the overlap region images to obtain the overlap image.

6. A computer-readable storage medium, which is a non-volatile storage medium or a non-transitory storage medium, on which a computer program is stored, characterized by The computer program, when executed by a processor, performs the steps of the method of any one of claims 1 to 4.

7. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instructions, when executed by a processor, implement the steps of the method of any one of claims 1 to 4.

8. A terminal comprising a memory and a processor, said memory having stored thereon a computer program that is operable on said processor, characterized in that, The processor, when executing the computer program, performs the steps of the method of any one of claims 1 to 4.

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