Calibration method for extreme ultraviolet mask detection system and mask detection method

Abstract

The present application provides a calibration method and a mask detection method for an extreme ultraviolet mask detection system. The method includes: after the extreme ultraviolet mask detection system performs mask detection on each group of target patterns, determining the mask detection image of each group of target patterns, the target pattern is a standard pattern selected based on spatial frequency, each group of standard patterns includes at least two elbow-shaped patterns, and the directions of the elbow-shaped patterns are different; obtaining an ideal image of each group of target patterns; determining the point spread function from the mask detection image to the ideal image of each group of target patterns; and determining the target point spread function of the extreme ultraviolet mask detection system based on each point spread function, the target point spread function being used to adjust the mask detection image of the sample to be detected. The present application solves the optimal point spread function of the extreme ultraviolet mask detection system by including a target pattern with at least two elbow-shaped patterns and the corresponding mask detection image and ideal image, thereby improving the resolution of the detection system and achieving high-resolution mask detection.

Description

Technical Field

[0001] The present application belongs to the field of extreme ultraviolet mask detection technology, and in particular relates to a calibration method for an extreme ultraviolet mask detection system and a mask detection method. Background Art

[0002] The Extreme Ultraviolet Lithography (EUV) mask inspection system can use the EUV band to image the mask and realize the inspection of the mask.

[0003] However, because the EUV lithography mask adopts a reflective design, its structure consists of a multilayer film composed of approximately 40 layers of Mo and Si, as well as the limitations of diffraction and processing technology, the resolution of the image obtained by the EUV mask detection system is insufficient. Summary of the Invention

[0004] The embodiments of the present application provide a calibration method for an extreme ultraviolet mask detection system, a mask detection method, an electronic device, a computer-readable storage medium, and a computer program product, which can solve the problem of insufficient resolution.

[0005] In a first aspect, an embodiment of the present application provides a calibration method for an extreme ultraviolet mask detection system, comprising:

[0006] After the extreme ultraviolet mask detection system performs mask detection on each group of target patterns, determining a mask detection image of each group of the target patterns, wherein the target patterns are standard patterns selected based on spatial frequency in a standard sample, the standard sample includes multiple groups of the standard patterns, each group of the standard patterns includes at least two elbow patterns, and the directions of the elbow patterns are different;

[0007] Acquire an ideal image of each group of the target patterns, where the position of the target pattern in the ideal image matches the position of the target pattern in the mask detection image;

[0008] For each group of target patterns, calculating a point spread function from the mask detection image to the ideal image by deconvolution;

[0009] A target point spread function of the extreme ultraviolet mask detection system is determined according to the point spread functions of each group of target patterns.

[0010] In one embodiment, after performing mask detection on each group of target patterns in the extreme ultraviolet mask detection system, determining a mask detection image of each group of target patterns includes:

[0011] After the extreme ultraviolet mask detection system performs mask detection on each group of target patterns, image data of each group of target patterns is obtained;

[0012] For each group of image data, the area where the target pattern is located in the image data is intercepted. After obtaining the target area, interpolation processing is performed on the target area, and zero padding processing is performed on the outside of the target area to obtain the mask detection image.

[0013] In one embodiment, calculating the point spread function from the mask detection image to the ideal image by deconvolution comprises:

[0014] performing noise reduction processing on the mask detection image to obtain a noise-reduced mask detection image;

[0015] Performing Fourier transformation on the denoised mask detection image and the ideal image to obtain a transformed mask detection image and a transformed ideal image;

[0016] Dividing the transformed mask detection image and the transformed ideal image to obtain a division result;

[0017] Performing inverse Fourier transform on the division result to obtain the point spread function.

[0018] In one embodiment, before determining the target point spread function of the extreme ultraviolet mask detection system according to the point spread function of each group of the target patterns, the method further includes:

[0019] For each group of target patterns, rotating the mask detection image and the ideal image of the target pattern to obtain a rotated mask detection image and a rotated ideal image;

[0020] A point spread function is determined from the rotated mask detection image to the rotated ideal image.

[0021] In one embodiment, the target pattern is selected based on the spatial frequency of the extreme ultraviolet mask detection system or the spatial frequency at which the cross-section contrast of the target line density pattern is greater than a preset contrast;

[0022] The line density of each set of standard patterns is different.

[0023] In one embodiment, the overall offset between the target pattern in the ideal image and the target pattern in the mask detection image is less than a first preset offset, and the relative offset between each two elbow-shaped patterns in the target pattern in the mask detection image is less than a second preset offset.

[0024] In a second aspect, an embodiment of the present application provides a mask inspection method based on an extreme ultraviolet mask inspection system, comprising:

[0025] After the extreme ultraviolet mask detection system performs mask detection on the pattern of the sample to be detected, obtaining image data of the pattern of the sample to be detected;

[0026] Performing interpolation and zero-padding processing on the image data of the pattern of the sample to be detected to obtain a mask detection image to be enhanced;

[0027] The target point spread function of the extreme ultraviolet mask detection system is used to perform convolution calculation on the mask detection image to be enhanced to obtain an enhanced mask detection image, wherein the target point spread function is determined by the method described in any one of the first aspects above.

[0028] In one embodiment, the angular direction of the mask detection image to be enhanced is orthogonal to the angular direction corresponding to the target point spread function.

[0029] In a third aspect, an embodiment of the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described in any one of the first and second aspects above is implemented.

[0030] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method as described in any one of the first or second aspects above is implemented.

[0031] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when executed on an electronic device, enables the electronic device to execute any one of the methods described in the first or second aspect above.

[0032] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0033] An embodiment of the present application includes determining a mask detection image of each group of target patterns after an extreme ultraviolet mask detection system performs mask detection on each group of target patterns, where the target pattern is a standard pattern selected based on spatial frequency in a standard sample, and the standard sample includes multiple groups of standard patterns, each group of standard patterns includes at least two elbow-shaped patterns, and the directions of the elbow-shaped patterns are different; obtaining an ideal image of each group of target patterns, where the position of the target pattern in the ideal image matches the position of the target pattern in the mask detection image; determining a point spread function from the mask detection image to the ideal image for each group of target patterns; determining a target point spread function of the extreme ultraviolet mask detection system based on the point spread function of each group of target patterns to calibrate the detection system, and solving a better point spread function for the extreme ultraviolet mask detection system by using a target pattern including at least two elbow-shaped patterns and a mask detection image and an ideal image of the corresponding target pattern to improve the resolution of the detection system and achieve high-resolution mask detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0035] Figure 1 This is a flow chart of a calibration method provided in one embodiment of the present application;

[0036] Figure 2 is an example diagram of a standard pattern provided in one embodiment of the present application;

[0037] Figure 3 This is an example diagram of position matching provided by an embodiment of the present application;

[0038] Figure 4 is an example diagram of image data provided by an embodiment of the present application;

[0039] Figure 5 is an example diagram of a mask detection image provided by an embodiment of the present application;

[0040] Figure 6 This is an example diagram of a multi-angle pattern provided by an embodiment of the present application;

[0041] Figure 7 This is an example diagram of the overall offset provided by an embodiment of the present application;

[0042] Figure 8 This is an example diagram of relative offset provided by an embodiment of the present application;

[0043] Figure 91 is a flow chart of a mask detection method provided in one embodiment of the present application;

[0044] Figure 10 It is a structural diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0045] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0046] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0047] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0048] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0049] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0050] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0051] The EUV mask inspection system uses the EUV band to image the mask and perform inspection. The EUV mask inspection system consists of two components: illumination and imaging. The illumination component, which precedes the light incident on the sample to be inspected, includes the light source, light-collecting zone plate / elliptical mirror, aperture, polarizer / galvanometer, and decoherence module. The imaging component, which precedes the light exiting the sample to be inspected, includes the off-axis zone plate / elliptical mirror and EUV data acquisition structure.

[0052] Because the extreme ultraviolet mask inspection system is the key to the development of extreme ultraviolet lithography technology, it generally has high-resolution requirements. However, it is difficult for the extreme ultraviolet mask inspection system to achieve high-resolution mask inspection simply through optical system design, resulting in the image obtained by the extreme ultraviolet mask inspection system having insufficient resolution.

[0053] Therefore, the embodiments of the present application provide a calibration method, a mask detection method, a calibration device, an electronic device and a computer-readable storage medium applied to an extreme ultraviolet mask detection system to solve a more optimal point spread function for the extreme ultraviolet mask detection system, so as to improve the resolution of the detection system and achieve high-resolution mask detection.

[0054] Figure 1 This is a flow chart of the calibration method provided in one embodiment of the present application. Figure 1 As shown, the method includes:

[0055] S11: After the extreme ultraviolet mask detection system performs mask detection on each group of target patterns, a mask detection image of each group of target patterns is determined.

[0056] The target pattern is a standard pattern selected based on spatial frequency in the standard sample. The standard sample includes multiple groups of standard patterns. Each group of standard patterns includes at least two elbow patterns, and the directions of the elbow patterns are different.

[0057] The standard pattern is a standard sample designed for the extreme ultraviolet mask detection system, providing a basis for solving the point spread function with good response.

[0058] The target pattern is a standard pattern selected based on spatial frequency, and is selected for the spatial frequency range of mask detection to improve the resolution within the spatial frequency range of mask detection and achieve better mask detection results.

[0059] Figure 2 This is an example diagram of a standard pattern provided in one embodiment of the present application. Figure 2 As shown, each set of standard patterns includes two elbow patterns, one of which is obtained by rotating the other 45°. The elbow pattern has an elbow angle of 90° and consists of several short lines and one long line. The line density of the elbow pattern indicates the resolution of the pattern and also the resolution of the set of standard patterns.

[0060] In the application, each set of target patterns is placed near the center of the field of view of the EUV mask inspection system, and then the EUV mask inspection system performs mask inspection on each set of target patterns and determines the mask inspection image of each set of target patterns.

[0061] S12: Acquire an ideal image of each group of target patterns, and match the position of the target pattern in the ideal image with the position of the target pattern in the mask detection image.

[0062] In one possible implementation, an ideal image of each set of standard patterns is constructed based on the actual patterns of the standard patterns. Specifically, when the machining accuracy of the standard sample is significantly higher than the linear density of the standard pattern, the designed standard pattern is considered the actual pattern. When the machining accuracy of the standard sample is close to the linear density of the standard pattern, a mask inspection system with a mask inspection resolution three times higher than the linear density of the standard pattern is used to perform mask inspection, and the resulting image is used as the actual pattern of the standard pattern.

[0063] Among them, the processing accuracy is the minimum processing size, and the line density of the standard pattern can be expressed in cycles or half cycles. Generally, the processing accuracy is one order of magnitude finer than the half cycle.

[0064] Figure 3 This is an example diagram of position matching provided by an embodiment of the present application. Figure 3 As shown in FIG, the position of the target pattern in the ideal image matches the position of the target pattern in the mask detection image, wherein the ideal image and the mask detection image have the same size.

[0065] S13: For each group of target patterns, a point spread function from the mask detection image to the ideal image is calculated by deconvolution.

[0066] In the application, deconvolution calculation is performed on the mask detection image and the ideal image of each group of target patterns to obtain the point spread function from the mask detection image to the ideal image.

[0067] S14: Determine a target point spread function of the extreme ultraviolet mask detection system according to the point spread functions of each group of target patterns.

[0068] In one possible implementation, the point spread functions of each group of target patterns are averaged to obtain a target point spread function, which is the point spread function of the EUV mask detection system, thereby achieving calibration of the EUV mask detection system.

[0069] It is explained that the above method is used to solve a better point spread function for the extreme ultraviolet mask detection system, and the solution to the better point spread function is to use multiple groups of standard patterns containing elbow-shaped patterns in different directions, which can have a good recovery effect on patterns of different directions, different resolutions, and different shapes. Therefore, in the subsequent mask detection, there is no need to make specific requirements on the orientation, shape, and line density of the sample to be detected, which is convenient to meet the needs of mask detection and reduce the difficulty of mask detection.

[0070] The above method can easily solve the target point spread function of the extreme ultraviolet mask detection system, with low computational complexity and reduced difficulty in mask detection. The low computational complexity can ensure detection speed and thus practicality.

[0071] This embodiment includes determining a mask detection image of each group of target patterns after the extreme ultraviolet mask detection system performs mask detection on each group of target patterns, where the target pattern is a standard pattern selected based on spatial frequency in a standard sample, and the standard sample includes multiple groups of standard patterns, each group of standard patterns includes at least two elbow-shaped patterns, and the directions of the elbow-shaped patterns are different; obtaining an ideal image of each group of target patterns, where the position of the target pattern in the ideal image matches the position of the target pattern in the mask detection image; determining a point spread function from the mask detection image to the ideal image for each group of target patterns; determining a target point spread function of the extreme ultraviolet mask detection system based on the point spread function of each group of target patterns to calibrate the detection system, and solving a better point spread function for the extreme ultraviolet mask detection system by using a target pattern including at least two elbow-shaped patterns and a mask detection image and an ideal image of the corresponding target pattern to improve the resolution of the detection system and achieve high-resolution mask detection.

[0072] In one embodiment, step S11 includes:

[0073] S111: After the EUV mask detection system performs mask detection on each group of target patterns, image data of each group of target patterns is obtained.

[0074] Figure 4 This is an example diagram of image data provided by an embodiment of the present application. Figure 4As shown in FIG. 1 , the figure is image data, and the two elbow patterns in the figure are patterns without interpolation and zero padding.

[0075] S112: For each set of image data, the area where the target pattern is located in the image data is intercepted. After obtaining the target area, interpolation processing is performed on the target area, and zero padding processing is performed on the outside of the target area to obtain a mask detection image.

[0076] In a possible implementation, a bidirectional multiple equidistant interpolation process is performed on the target area, and a zero-filling process is performed on the bidirectional outer areas of the target area.

[0077] In a possible implementation, a bidirectional five-fold equidistant interpolation process is performed on the target area, and a zero-filling process is performed on the bidirectional outer one-fold area of ​​the target area.

[0078] Figure 5 is an example diagram of a mask detection image provided by an embodiment of the present application. Figure 5 As shown in Figure 1, the mask detection image is obtained after performing mask detection on two elbow patterns in the target pattern. The mask detection image includes two elbow patterns with an angle difference of 45° between them. The two elbow patterns in this figure have been interpolated and zero-padded.

[0079] This embodiment intercepts the area where the target pattern is located in each set of image data, obtains the target area, performs interpolation processing on the target area, and performs zero padding processing on the outside of the target area to obtain a better and clearer mask detection image.

[0080] In one embodiment, step S13 includes:

[0081] S131: performing noise reduction processing on the mask detection image to obtain a noise-reduced mask detection image.

[0082] In applications, the mask detection image usually contains noise, which can be expressed as I i -z,I i is the i-th mask detection image, and z is the noise signal. In order to better solve the point spread function, the mask detection image is subjected to noise suppression calculation to remove the noise z in the mask detection image.

[0083] S132: Performing Fourier transform on the denoised mask detection image and the ideal image to obtain a transformed mask detection image and a transformed ideal image.

[0084] S133: Divide the transformed mask detection image and the transformed ideal image to obtain a division result.

[0085] S134: Perform inverse Fourier transform on the division result to obtain a point spread function.

[0086] In application, the above process is expressed by the formula: i =F -1 [F(J i ) / F(I i )],x i is the point spread function, F -1 is the inverse Fourier transform, F is the Fourier transform, J i is the i-th ideal image.

[0087] It should be noted that the above method is only one implementation of deconvolution calculation.

[0088] This embodiment performs noise reduction processing on the mask detection image to obtain a noise-reduced mask detection image, performs Fourier transform on the noise-reduced mask detection image and the ideal image to obtain a transformed mask detection image and a transformed ideal image; divides the transformed mask detection image and the transformed ideal image to obtain a division result; and performs an inverse Fourier transform on the division result to simply obtain a point spread function, effectively reducing the amount of calculation.

[0089] In one embodiment, before step S14, the method further includes:

[0090] S21: For each group of target patterns, rotate the mask detection image and the ideal image of the target pattern to obtain a rotated mask detection image and a rotated ideal image.

[0091] S22: Determine a point spread function from the rotated mask detection image to the rotated ideal image.

[0092] In applications, when the restoration effect at more angles needs to be enhanced, the mask detection image and the ideal image of the target pattern can be rotated to obtain mask detection images and ideal images at different angles relative to the target pattern. Deconvolution is then used to calculate the point spread function from the rotated mask detection image to the rotated ideal image.

[0093] Figure 6 This is an example diagram of a multi-angle pattern provided by an embodiment of the present application. Figure 6 As shown, patterns at multiple angles are obtained.

[0094] This embodiment rotates the mask detection image and the ideal image of each group of target patterns to obtain a rotated mask detection image and a rotated ideal image; determines the point spread function from the rotated mask detection image to the rotated ideal image, and obtains the point spread functions at different angles, which has a better recovery effect on the patterns of the samples to be detected in different directions.

[0095] In one embodiment, the target pattern is obtained by selecting a spatial frequency of an extreme ultraviolet mask detection system or a spatial frequency at which a cross-section contrast of the target line density pattern is greater than a preset contrast.

[0096] In a possible implementation, the selection may be made based on the highest spatial frequency calculated by the Rayleigh criterion in the EUV mask detection system, or based on the highest spatial frequency when the cross-line contrast of the target line density pattern is greater than a preset contrast.

[0097] By selecting the target pattern according to the spatial frequency of the extreme ultraviolet mask detection system or the spatial frequency when the cross-section contrast of the target line density pattern is greater than the preset contrast, the spatial frequency range of the mask detection is better enhanced to achieve a better mask detection effect.

[0098] The line density of each group of standard patterns is different, which means that the resolution of each group of standard patterns is different, which has a better recovery effect on patterns with different resolutions and reduces the difficulty of detection.

[0099] In one embodiment, the overall offset between the target pattern in the ideal image and the target pattern in the mask detection image is less than a first predetermined offset, and the relative offset between each two elbow patterns in the target pattern in the mask detection image is less than a second predetermined offset. This ensures accurate calculation of the point spread function from the mask detection image to the ideal image.

[0100] For example, the overall offset between the target pattern in the ideal image and the target pattern in the mask detection image is less than 30%, and the relative offset between each two elbow patterns in the target pattern in the mask detection image is less than 3%.

[0101] The overall offset is the offset between the target pattern in the mask detection image and the target pattern in the ideal image. Figure 7 This is an example diagram of the overall offset provided by an embodiment of the present application. Figure 7 As shown, the target pattern in the mask detection image is offset from the target pattern in the ideal image as a whole.

[0102] The relative offset is an offset between two elbow patterns in the target pattern of the mask detection image. Figure 8 This is an example diagram of relative offset provided by an embodiment of the present application. Figure 8 As shown, there is an offset between the two elbow patterns in the target pattern of the mask detection image.

[0103] In application, if the overall offset between the target pattern in the ideal image and the target pattern in the mask detection image is not less than the first preset offset or the relative offset between the two elbow patterns in the target pattern of the mask detection image is not less than the second preset offset, it is necessary to reconstruct the ideal image based on the real pattern of the target pattern and re-perform mask detection on the target pattern to obtain the mask detection image after processing.

[0104] Figure 9 FIG. 1 is a flow chart of a mask detection method provided by an embodiment of the present application. Figure 9 As shown, the method includes:

[0105] S31: After the EUV mask detection system performs mask detection on the pattern of the sample to be detected, image data of the pattern of the sample to be detected is obtained.

[0106] In application, after the pattern of the sample to be inspected is placed near the center of the field of view of the extreme ultraviolet mask inspection system, the extreme ultraviolet mask inspection system performs mask inspection on the pattern of the sample to be inspected to obtain image data of the pattern of the sample to be inspected.

[0107] S32: performing interpolation and zero-padding processing on the image data of the pattern of the sample to be detected to obtain a mask detection image to be enhanced.

[0108] In the application, the area where the pattern is located in the image data of the sample to be detected is cropped, and then interpolation processing is performed in the area where the pattern is located in the image data of the sample to be detected, and zero padding processing is performed outside the area where the pattern is located to obtain the mask detection image to be enhanced.

[0109] S33: Using the target point spread function of the extreme ultraviolet mask detection system, perform convolution calculation on the mask detection image to be enhanced to obtain an enhanced mask detection image.

[0110] The target point spread function is determined through the steps in the above-mentioned various method embodiments.

[0111] In this application, the mask detection image to be enhanced is convolved with the target point spread function. The convolution formula is Jˋ = Iˋ * x, where Jˋ is the enhanced mask detection image, Iˋ is the mask detection image to be enhanced, and x is the target point spread function. The resulting enhanced mask detection image has high resolution.

[0112] This embodiment obtains a mask detection image to be enhanced by interpolating and zero-padding the image data of the pattern of the sample to be detected, and uses the target point spread function of the extreme ultraviolet mask detection system to process the mask detection image to be enhanced to obtain an enhanced mask detection image, thereby obtaining a high-resolution enhanced mask detection image and achieving resolution enhancement of the image data of the pattern of the sample to be detected.

[0113] In one embodiment, the angular direction of the mask detection image to be enhanced is orthogonal to the angular direction corresponding to the target point spread function.

[0114] In applications, the mask detection image to be enhanced can be rotated to a direction orthogonal to the direction corresponding to the target point spread function, which can reduce the requirement for angle response.

[0115] For example, when a target point spread function is determined based on a target pattern including an elbow pattern at two angles, the target point spread function corresponds to two angular directions, and the angular direction of the mask detection image to be enhanced is rotated so that it is orthogonal to one of the two angular directions corresponding to the target point spread function. When a target point spread function is determined based on a target pattern including an elbow pattern at four angles, the target point spread function corresponds to four angular directions, and the angular direction of the mask detection image to be enhanced is rotated so that it is orthogonal to one of the four angular directions corresponding to the target point spread function.

[0116] Figure 10 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present application. Figure 10 As shown, the electronic device 1 of this embodiment includes: at least one processor 10 ( Figure 10 Only one is shown), a memory 11 and a computer program 12 stored in the memory 11 and executable on the at least one processor 10, wherein the processor 10 implements the steps of any of the above-mentioned groups of method embodiments when executing the computer program 12.

[0117] The electronic device 1 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 1 may include, but is not limited to, a processor 10 and a memory 11. Those skilled in the art will understand that Figure 10 This is merely an example of the electronic device 1 and does not constitute a limitation on the electronic device 1 . The electronic device 1 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 1 may also include input and output devices, network access devices, etc.

[0118] The processor 10 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0119] In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a hard disk or memory of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Furthermore, the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 11 may also be used to temporarily store data that has been output or is to be output.

[0120] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0121] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each group of units can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0122] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned groups of method embodiments can be implemented.

[0123] An embodiment of the present application provides a computer program product. When the computer program product is run on an electronic device, the electronic device can implement the steps in the above-mentioned groups of method embodiments when executing the computer program product.

[0124] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process of the above-mentioned method embodiment by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each group of method embodiments described above. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, mobile hard drive, magnetic disk, or optical disk. In some jurisdictions, based on legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0125] In the above embodiments, the description of each group of embodiments has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0126] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0127] In the embodiments provided in this application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0128] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0129] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A calibration method for an extreme ultraviolet mask detection system, comprising: After the extreme ultraviolet mask detection system performs mask detection on each group of target patterns, determining a mask detection image of each group of the target patterns, wherein the target patterns are standard patterns selected based on spatial frequency in a standard sample, the standard sample includes multiple groups of the standard patterns, each group of the standard patterns includes at least two elbow patterns, and the directions of the elbow patterns are different; Acquire an ideal image of each group of the target patterns, where the position of the target pattern in the ideal image matches the position of the target pattern in the mask detection image, wherein the ideal image is constructed based on a real pattern of the standard pattern, where the real pattern is a standard pattern designed according to processing accuracy or an image obtained by mask detection; For each group of target patterns, calculating a point spread function from the mask detection image to the ideal image by deconvolution; A target point spread function of the extreme ultraviolet mask detection system is determined according to the point spread functions of each group of target patterns.

2. The method according to claim 1, wherein after performing mask detection on each group of target patterns in the extreme ultraviolet mask detection system, determining a mask detection image of each group of target patterns comprises: After the extreme ultraviolet mask detection system performs mask detection on each group of target patterns, image data of each group of target patterns is obtained; For each group of image data, the area where the target pattern is located in the image data is intercepted. After obtaining the target area, interpolation processing is performed on the target area, and zero padding processing is performed on the outside of the target area to obtain the mask detection image.

3. The method according to claim 1, wherein calculating the point spread function from the mask detection image to the ideal image by deconvolution comprises: performing noise reduction processing on the mask detection image to obtain a noise-reduced mask detection image; Performing Fourier transformation on the denoised mask detection image and the ideal image to obtain a transformed mask detection image and a transformed ideal image; Dividing the transformed mask detection image and the transformed ideal image to obtain a division result; Performing inverse Fourier transform on the division result to obtain the point spread function.

4. The method according to any one of claims 1 to 3, characterized in that before determining the target point spread function of the extreme ultraviolet mask inspection system based on the point spread functions of each group of target patterns, the method further comprises: For each group of target patterns, rotating the mask detection image and the ideal image of the target pattern to obtain a rotated mask detection image and a rotated ideal image; A point spread function is determined from the rotated mask detection image to the rotated ideal image.

5. The method according to claim 4, wherein the target pattern is selected based on the spatial frequency of the extreme ultraviolet mask detection system or the spatial frequency at which the cross-section contrast of the target line density pattern is greater than a preset contrast; The line density of each set of standard patterns is different.

6. The method according to claim 5 is characterized in that the overall offset between the target pattern in the ideal image and the target pattern in the mask detection image is less than a first preset offset, and the relative offset between each two elbow-shaped patterns in the target pattern of the mask detection image is less than a second preset offset.

7. A mask inspection method based on an extreme ultraviolet mask inspection system, comprising: After the extreme ultraviolet mask detection system performs mask detection on the pattern of the sample to be detected, obtaining image data of the pattern of the sample to be detected; Performing interpolation and zero-padding processing on the image data of the pattern of the sample to be detected to obtain a mask detection image to be enhanced; The target point spread function of the extreme ultraviolet mask detection system is used to perform convolution calculation on the mask detection image to be enhanced to obtain an enhanced mask detection image, wherein the target point spread function is determined by the method according to any one of claims 1 to 6. 8 . The method according to claim 7 , wherein the angular direction of the mask detection image to be enhanced is orthogonal to the angular direction corresponding to the target point spread function.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 6 or 7 to 8 when executing the computer program.

10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method according to any one of claims 1 to 6 or 7 to 8.

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