Lithographic Aberration Calibration Method, Device, Storage Medium and Electronic Device

Abstract

The present application discloses a lithography aberration calibration method, apparatus, storage medium and electronic device. Among them, the lithography aberration calibration method includes obtaining an isolated mask image; based on the isolated mask image, using simulation software to generate an initial simulation image training set for lithography imaging; performing Fourier transform and frequency domain truncation on the simulation images in the initial simulation image training set to obtain a target simulation image training set; constructing a linear regression matrix between the target simulation image training set and Zernike coefficients; calculating the Zernike coefficients of the measured lithography image based on the linear regression matrix and the least squares method to calibrate the lithography aberration. This solution can improve the efficiency of lithography aberration calibration.

Description

Technical Field

[0001] Embodiments of the present application relate to the field of lithography technology, and in particular, to a lithography aberration calibration method, device, storage medium, and electronic device. Background Art

[0002] Lithography technology is one of the core processes for achieving nano-scale feature pattern transfer in semiconductor manufacturing, and its accuracy directly affects the performance and yield of chips. During the lithography process, aberration of the optical system is one of the main causes of imaging errors. Aberrations mainly include spherical aberration, coma, astigmatism, field curvature, etc., which will cause deviations between the pattern projected onto the silicon wafer and the desired pattern, thereby affecting the final manufacturing accuracy.

[0003] Currently, optical lens optimization or online wave aberration calibration is usually used to reduce the impact of aberration on imaging quality. However, due to the extremely high accuracy requirements for aberration calibration in advanced nodes (such as 7nm and 5nm processes), high-order residual aberrations cannot be completely eliminated during the manufacturing process of optical lenses. And the online wave aberration calibration method requires multiple adjustments and measurements, which is time-consuming and has insufficient accuracy in detecting high-order aberrations. Summary of the Invention

[0004] Embodiments of the present application provide a lithography aberration calibration method, device, storage medium, and electronic device, which can improve the efficiency of lithography aberration calibration.

[0005] In a first aspect, embodiments of the present application provide a lithography aberration calibration method, including:

[0006] Obtain an isolated mask image;

[0007] Based on the isolated mask image, use simulation software to generate an initial simulation image training set for lithography imaging;

[0008] Perform Fourier transform and frequency domain truncation on the simulation images in the initial simulation image training set to obtain a target simulation image training set;

[0009] Construct a linear regression matrix between the target simulation image training set and Zernike coefficients;

[0010] Based on the linear regression matrix and the least squares method, calculate the Zernike coefficients of the measured lithography image to calibrate the lithography aberration.

[0011] In the lithography aberration calibration method provided by the embodiments of the present application, the step of using simulation software to generate an initial simulation image training set for lithography imaging based on the isolated mask image includes:

[0012] Set lithography conditions and a set of Zernike coefficients, where the set of Zernike coefficients includes several groups of Zernike coefficients;

[0013] Calculate the planar light intensity distribution diagrams of each group of the Zernike coefficients of the isolated mask image under the lithography conditions through simulation software;

[0014] Use several of the planar light intensity distribution diagrams as the initial simulation image training set.

[0015] In the lithography aberration calibration method provided by the embodiments of the present application, the Fourier transform and frequency domain truncation of the simulation images in the initial simulation image training set to obtain a target simulation image training set includes:

[0016] Use Fourier transform to convert each of the planar light intensity distribution diagrams in the initial simulation image training set from a spatial domain image to a frequency domain image;

[0017] Perform frequency domain truncation on several of the frequency domain images to obtain a target simulation image training set.

[0018] In the lithography aberration calibration method provided by the embodiments of the present application, the frequency domain truncation of several of the frequency domain images to obtain a target simulation image training set includes:

[0019] Truncate each of the frequency domain images according to the effective frequency range of the optical system;

[0020] Based on the inverse Fourier transform, convert the truncated frequency domain images into spatial domain images;

[0021] Use several of the spatial domain images as the target simulation image training set.

[0022] In the lithography aberration calibration method provided by the embodiments of the present application, the construction of the linear regression matrix of the target simulation image training set and the Zernike coefficients includes:

[0023] Adopt the principal component analysis method to extract the eigenvalues and eigenvectors of the target simulation image training set;

[0024] Based on the sorting of the magnitudes of several of the eigenvalues, perform feature extraction on several of the eigenvectors to obtain target eigenvectors;

[0025] Construct the linear regression matrix of the target eigenvectors and the Zernike coefficients.

[0026] In the lithography aberration calibration method provided by the embodiments of the present application, the adoption of the principal component analysis method to extract the eigenvalues and eigenvectors of the target simulation image training set includes:

[0027] Represent the target simulation image training set as a data matrix;

[0028] Calculate the covariance matrix of the data matrix;

[0029] Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues and eigenvectors.

[0030] In the lithography aberration calibration method provided by the embodiments of the present application, the construction of the linear regression matrix between the target eigenvector and the Zernike coefficient includes:

[0031] Represent a number of the target eigenvectors as an eigenmatrix;

[0032] Project the data matrix onto the eigenmatrix to form a principal component matrix;

[0033] Calculate the linear regression matrix based on the Zernike coefficient and the principal component matrix.

[0034] In a second aspect, an embodiment of the present application provides a lithography aberration calibration device, including:

[0035] An acquisition unit, configured to acquire an isolated mask image;

[0036] A simulation unit, configured to generate an initial simulation image training set of lithography imaging based on the isolated mask image by using simulation software;

[0037] A conversion unit, configured to perform Fourier transform and frequency domain truncation on the simulation images in the initial simulation image training set to obtain a target simulation image training set;

[0038] A construction unit, configured to construct a linear regression matrix between the target simulation image training set and the Zernike coefficient;

[0039] An actual measurement unit, configured to calculate the Zernike coefficient of the measured lithography image based on the linear regression matrix and the least squares method to calibrate the lithography aberration.

[0040] In a third aspect, the present application provides a storage medium, which stores multiple instructions, and the instructions are suitable for being loaded by a processor to execute the lithography aberration calibration method described in any one of the above.

[0041] In a fourth aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the lithography aberration calibration method described in any one of the above is implemented.

[0042] In summary, the lithographic aberration calibration method provided by the embodiments of the present application includes obtaining an isolated mask image; generating an initial simulation image training set of lithographic imaging using simulation software based on the isolated mask image; performing Fourier transform and frequency domain truncation on the simulation images in the initial simulation image training set to obtain a target simulation image training set; constructing a linear regression matrix between the target simulation image training set and Zernike coefficients; and calculating the Zernike coefficients of the measured lithographic image based on the linear regression matrix and the least squares method to calibrate the lithographic aberration. This solution can reduce the amount of data analysis while retaining actual effective data through frequency domain truncation, thereby improving the efficiency of lithographic aberration calibration. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] To more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0044] Figure 1 FIG. is a schematic diagram of an application scenario of the lithographic aberration calibration method provided by the embodiments of the present application.

[0045] Figure 2 FIG. is a schematic flowchart of the lithographic aberration calibration method provided by the embodiments of the present application.

[0046] Figure 3 FIG. is a schematic structural diagram of the lithographic aberration calibration device provided by the embodiments of the present application.

[0047] Figure 4 FIG. is a schematic structural diagram of the electronic device provided by the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] Here, the exemplary embodiments will be described in detail, and the examples are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are only examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0049] It should be noted that in this text, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising such element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined based on their explanations in the specific embodiments or further in combination with the context of the specific embodiments.

[0050] It should be understood that the specific embodiments described herein are merely for explaining the present application and are not used to limit the present application.

[0051] In the following description, the suffixes such as "module", "component" or "unit" used to represent elements are only for the convenience of explaining the present application and have no specific meaning in themselves. Therefore, "module", "component" or "unit" can be used interchangeably.

[0052] In the description of the present application, it should be noted that the orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present application. In addition, terms such as "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0053] Currently, optical lens optimization or online wavefront aberration calibration is usually used to reduce the impact of aberration on imaging quality. However, due to the extremely high precision requirements for wavefront aberration calibration in advanced nodes (such as 7nm and 5nm processes), high-order residual aberrations cannot be completely eliminated during the optical lens manufacturing process. And the online wavefront aberration calibration method requires multiple adjustments and measurements, which is time-consuming and has insufficient precision in high-order aberration detection.

[0054] Based on this, the embodiments of the present application provide a lithographic aberration calibration method, apparatus, storage medium, and electronic device. Specifically, the lithographic aberration calibration apparatus can be integrated into an electronic device, which can be a server or a terminal device such as a mobile phone, a wearable intelligent device, a tablet computer, a laptop computer, and a personal computer (PC). Among them, the terminal can include a mobile phone, a wearable intelligent device, a tablet computer, a laptop computer, and a personal computer (PC), etc.; the server can be a single server or a server cluster composed of multiple servers, and can be a physical server or a virtual server.

[0055] For example, as Figure 1 shown, the electronic device can obtain an isolated mask image; based on the isolated mask image, use simulation software to generate an initial simulation image training set for lithographic imaging; perform Fourier transform and frequency domain truncation on the simulation images in the initial simulation image training set to obtain a target simulation image training set; construct a linear regression matrix between the target simulation image training set and the Zernike coefficients; calculate the Zernike coefficients of the measured lithographic image based on the linear regression matrix and the least squares method to calibrate the lithographic aberration.

[0056] The technical solutions shown in the present application will be described in detail below through specific embodiments. It should be noted that the description order of the following embodiments does not limit the priority order of the embodiments.

[0057] Please refer to Figure 2 , Figure 2 which is a schematic flowchart of the lithographic aberration calibration method provided by the embodiments of the present application. The specific process of the lithographic aberration calibration method can be as follows:

[0058] 101. Obtain an isolated mask image.

[0059] Aberrations of the optical system (such as spherical aberration, coma, astigmatism, etc.) will affect the quality of the lithographic image. By obtaining a series of isolated mask patterns, the influence of different aberrations on the imaging result can be maximally captured, so as to obtain higher accuracy in simulation and frequency domain analysis. This helps to optimize the design of the lithographic system and improve the imaging quality.

[0060] In some embodiments, by selecting isolated mask images with specific spatial frequencies, diffraction orders, and direction distributions, it can help simulate the response of the optical system under different conditions to adapt to different optical aberrations and simulation requirements.

[0061] Specifically, a complete mask image can be obtained first, and then a part of the image in the complete mask image can be selected as the isolated mask image according to certain conditions. Among them, the number of complete mask images can be set according to the actual situation, and the embodiments of the present application do not limit it.

[0062] In some embodiments, the selection conditions may include a sampling range, a pitch, a critical dimension (CD), etc. For isolated mask images with different pitches, their representations in the frequency domain correspond to different diffraction orders, and isolated mask images with different CDs correspond to amplitude changes in the diffraction orders.

[0063] For example, a suitable sampling range is selected on the X-axis and Y-axis of the complete mask image. Then, based on this sampling range, isolated mask images with different periods and sizes are selected on the X-axis and Y-axis respectively. It should be noted that the sampling range, period, and size can be set according to the actual situation, and the embodiments of the present application do not limit them.

[0064] 102. Based on the isolated mask images, use simulation software to generate an initial simulation image training set for lithography imaging.

[0065] Specifically, the lithography conditions and a set of Zernike coefficients can be set first. The set of Zernike coefficients includes several groups of Zernike coefficients. Then, through the simulation software, calculate the planar light intensity distribution diagrams of each group of Zernike coefficients for the isolated mask images under the lithography conditions, and use the several planar light intensity distribution diagrams as the initial simulation image training set.

[0066] Among them, the lithography conditions may include illumination conditions, optical parameters, imaging film parameters, etc. For example, light source polarization, partial coherence factor , numerical aperture NA, wavelength, refractive index absorption rate of the photoresist, etc.

[0067] The Zernike coefficients are used to describe different optical aberrations (such as spherical aberration, astigmatism, etc.). Common Zernike coefficients include Z4, Z7, Z8, Z9, Z10. Among them, Z4 is spherical aberration, which affects the focal position. Z7 and Z8 are coma aberrations, which affect the trailing and uniformity of imaging. Z9 and Z10 are astigmatism, which affect the symmetry of imaging.

[0068] To comprehensively simulate various aberrations in the optical system, it is necessary to calculate Zernike coefficients of different orders and combinations. Taking 37th-order Zernike coefficients as an example, about 700 groups of different Zernike coefficients can be formed. Each group of Zernike coefficients will affect the imaging effect, and each combination corresponds to a different aberration mode. These Zernike coefficients can be used to generate a series of different optical system states, and then obtain a variety of imaging images.

[0069] 103. Perform Fourier transform and frequency domain truncation on the simulation images in the initial simulation image training set to obtain the target simulation image training set.

[0070] Among them, the simulation image is an image in the spatial domain. Specifically, the Fourier transform can be used to convert each planar light intensity distribution diagram in the initial simulation image training set from an image in the spatial domain to an image in the frequency domain; several frequency-domain images are truncated in the frequency domain to obtain a target simulation image training set.

[0071] In some embodiments, truncating several frequency-domain images in the frequency domain to obtain a target simulation image training set can specifically be: truncating each frequency-domain image according to the effective frequency range of the optical system; based on the inverse Fourier transform, converting the truncated frequency-domain image into an image in the spatial domain; and using several spatial-domain images as the target simulation image training set.

[0072] Among them, the effective frequency range can be . is the coherence factor, which is used to describe the coherence of the light source.

[0073] In some embodiments, converting the planar light intensity distribution diagram from an image in the spatial domain to an image in the frequency domain can be as follows:

[0074]

[0075] In one embodiment, converting the truncated frequency-domain image into an image in the spatial domain can be as follows:

[0076]

[0077] In the above formula, is the planar light intensity distribution diagram. is the frequency-domain image. D is the sampling range, is the effective frequency range. j represents the imaginary number in Euler's formula, which characterizes the phase change. f and g respectively represent the frequency values of the Fourier transform in the x-axis direction and the y-axis direction.

[0078] As can be seen from the above, since the frequency-domain image has been data-truncated, the data volume of the spatial-domain image has also been reduced accordingly, thereby effectively reducing the data calculation amount and storage amount.

[0079] In one embodiment, converting the truncated frequency-domain image into an image in the spatial domain can specifically be as follows:

[0080]

[0081] In the above formula, is the frequency-domain coordinate, is the pupil function, is the mask function, is coefficient, is equation. m represents the number of frequency-domain images, and n represents The number of coefficients, .

[0082] 104. Construct a linear regression matrix for the target simulation image training set and the Zernike coefficients.

[0083] In some embodiments, step 104 is specifically as follows:

[0084] S1. Use the Principal Component Analysis (PCA) to extract the eigenvalues and eigenvectors of the target simulation image training set.

[0085] Specifically, the target simulation image training set can be first represented as a data matrix, then the covariance matrix of the data matrix is calculated, and finally the eigenvalue decomposition is performed on the covariance matrix to obtain the eigenvalues and eigenvectors.

[0086] S2. Based on the sorting of the magnitudes of several eigenvalues, perform feature extraction on several eigenvectors to obtain the target eigenvector.

[0087] For example, the eigenvectors corresponding to the first several eigenvalues can be selected in descending order of eigenvalues as the target eigenvector.

[0088] S3. Construct a linear regression matrix for the target eigenvector and the Zernike coefficients.

[0089] It can be understood that the linear regression matrix of the target eigenvector and the Zernike coefficients is the linear regression matrix of the spatial domain image and the Zernike coefficients.

[0090] Specifically, several target eigenvectors can be represented as a feature matrix; the data matrix is projected onto the feature matrix to form a principal component matrix; and the linear regression matrix is calculated based on the Zernike coefficients and the principal component matrix.

[0091] It should be noted that when calculating the linear regression matrix, the least squares method can be used to fit the Zernike coefficients and the principal component matrix to obtain the linear regression matrix.

[0092] After PCA, the features of each spatial domain image are simplified to several principal components (eigenvalues), greatly reducing the dimension and complexity of the data. Among them, the Zernike coefficients in this embodiment are the Zernike coefficients set in step 102.

[0093] 105. Calculate the Zernike coefficients of the measured lithography image based on the linear regression matrix and the least squares method to calibrate the lithography aberration.

[0094] Specifically, the measured lithography image can be first subjected to Fourier transform and frequency domain truncation to obtain the measured spatial domain image, and then the least squares method is used to fit the measured spatial domain image and the linear regression matrix, so as to obtain the Zernike coefficients of the measured lithography image.

[0095] After obtaining the Zernike coefficients of the measured lithography image, the optical elements (such as lens groups) or light source parameters (such as numerical aperture NA, illumination conditions) of the lithography machine can be adjusted according to different components of the Zernike coefficients. For specific aberration types (such as spherical aberration, coma), the corresponding compensation device can be adjusted automatically or manually. It is also possible to perform feedback control on the measured Zernike coefficients to adjust the state of the optical system to ensure the best imaging quality under different process conditions.

[0096] In summary, the lithography aberration calibration method provided by the embodiments of the present application includes obtaining an isolated mask image; based on the isolated mask image, using simulation software to generate an initial simulation image training set for lithography imaging; performing Fourier transform and frequency domain truncation on the simulation images in the initial simulation image training set to obtain a target simulation image training set; constructing a linear regression matrix between the target simulation image training set and the Zernike coefficients; and calculating the Zernike coefficients of the measured lithography image based on the linear regression matrix and the least squares method to calibrate the lithography aberration. This solution can reduce the amount of data analysis while retaining the actual effective data through frequency domain truncation, thereby improving the efficiency of lithography aberration calibration.

[0097] To facilitate better implementation of the lithography aberration calibration method provided by the embodiments of the present application, the embodiments of the present application also provide a lithography aberration calibration device. The meanings of the terms herein are the same as those in the above lithography aberration calibration method, and the specific implementation details can refer to the descriptions in the method embodiments.

[0098] Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of the lithography aberration calibration device provided by the embodiments of the present application. The lithography aberration calibration device may include an acquisition unit 201, a simulation unit 202, a conversion unit 203, a construction unit 204, and a measurement unit 205. Among them,

[0099] The acquisition unit 201 is used to acquire an isolated mask image;

[0100] The simulation unit 202 is used to generate an initial simulation image training set for lithography imaging based on the isolated mask image by using simulation software;

[0101] The conversion unit 203 is used to perform Fourier transform and frequency domain truncation on the simulation images in the initial simulation image training set to obtain a target simulation image training set;

[0102] A construction unit 204 for constructing a linear regression matrix between a target simulation image training set and Zernike coefficients;

[0103] An actual measurement unit 205 for calculating the Zernike coefficients of an actual measured lithography image based on the linear regression matrix and the least squares method to calibrate lithography aberration.

[0104] For the specific implementation manners of each of the above units, reference may be made to the embodiments of the above lithography aberration calibration method, which will not be elaborated herein one by one.

[0105] In summary, the lithography aberration calibration device provided in the embodiment of the present application can obtain an isolated mask image through the acquisition unit 201; the simulation unit 202 generates an initial simulation image training set of lithography imaging based on the isolated mask image by using simulation software; the conversion unit 203 performs Fourier transform and frequency domain truncation on the simulation images in the initial simulation image training set to obtain a target simulation image training set; the construction unit 204 constructs a linear regression matrix between the target simulation image training set and Zernike coefficients; the actual measurement unit 205 calculates the Zernike coefficients of the actual measured lithography image based on the linear regression matrix and the least squares method to calibrate lithography aberration. This solution can reduce the data analysis amount while retaining actual effective data through frequency domain truncation, thereby improving the efficiency of lithography aberration calibration.

[0106] The embodiment of the present application further provides an electronic device, in which the lithography aberration calibration device of the embodiment of the present application can be integrated, as Figure 4 shown, which shows a schematic structural diagram of the electronic device involved in the embodiment of the present application. Specifically:

[0107] The electronic device may include a processor 301 with one or more processing cores and a memory 302 with one or more computer-readable storage media and other components. Those skilled in the art can understand that Figure 4 the structural diagram of the electronic device shown in does not constitute a limitation on the electronic device, and it may include more or fewer components than shown, or combine certain components, or have different component arrangements. Among them:

[0108] The processor 301 is the control center of the electronic device, connecting various parts of the entire electronic device through various interfaces and circuits. By running or executing software programs stored in the memory 302 and / or this application, and by invoking the data stored in the memory 302, it executes various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor. Among them, the application processor mainly processes operating storage media, user interfaces, application programs, etc., and the modem processor mainly processes wireless communications. It can be understood that the above-mentioned modem processor may not be integrated into the processor 301 either.

[0109] The memory 302 can be used to store software programs and this application. The processor 301 executes various functional applications and data processing by running the software programs and this application stored in the memory 302. The memory 302 mainly includes a program storage area and a data storage area. Among them, the program storage area can store operating storage media, application programs required for at least one function, etc.; the data storage area can store data created according to the use of the electronic device. In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Correspondingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0110] Although not shown, the electronic device may also include a display unit, an input unit, a power supply, etc., which will not be elaborated here. Specifically in this embodiment, the processor 301 in the electronic device will, according to the following instructions, load the executable files corresponding to the processes of one or more application programs into the memory 302, and the processor 301 will run the application programs stored in the memory 302 to implement various functions as follows:

[0111] Obtain an isolated mask image;

[0112] Based on the isolated mask image, use simulation software to generate an initial simulation image training set for lithographic imaging;

[0113] Perform Fourier transform and frequency-domain truncation on the simulation images in the initial simulation image training set to obtain a target simulation image training set;

[0114] Construct a linear regression matrix between the target simulation image training set and the Zernike coefficients;

[0115] Calculate the Zernike coefficients of the measured lithographic image based on the linear regression matrix and the least squares method to calibrate the lithographic aberration.

[0116] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by controlling relevant hardware through instructions. The instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0117] For this reason, an embodiment of the present application provides a storage medium, in which multiple instructions are stored. The instructions can be loaded by a processor to execute the steps in any one of the methods provided by the embodiments of the present application. For example, the instructions can execute the following steps:

[0118] Obtain an isolated mask image;

[0119] Based on the isolated mask image, use simulation software to generate an initial simulation image training set for lithographic imaging;

[0120] Perform Fourier transform and frequency domain truncation on the simulation images in the initial simulation image training set to obtain a target simulation image training set;

[0121] Construct a linear regression matrix between the target simulation image training set and Zernike coefficients;

[0122] Based on the linear regression matrix and the least squares method, calculate the Zernike coefficients of the measured lithographic image to calibrate lithographic aberration.

[0123] For the specific implementation of each of the above operations, reference can be made to the previous embodiments and will not be elaborated here.

[0124] Among them, the storage medium may include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc.

[0125] Since the instructions stored in the storage medium can execute the steps in any one of the methods provided by the embodiments of the present application, the beneficial effects that can be achieved by any one of the methods provided by the embodiments of the present application can be realized. For details, refer to the previous embodiments and will not be elaborated here.

[0126] The lithographic aberration calibration method, device, storage medium, and electronic device provided by the present application have been introduced in detail above. Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for calibrating photolithography aberrations, characterized in that: include: Obtaining an isolated mask image; Setting photolithography conditions and a Zernike coefficient group, wherein the Zernike coefficient group includes several groups of Zernike coefficients; Calculate the plane light intensity distribution diagram of each group of the Zernike coefficients of the isolated mask image under the photolithography conditions by simulation software; Using the plurality of plane light intensity distribution maps as an initial simulation image training set; Using Fourier transform, converting each of the plane light intensity distribution diagrams in the initial simulation image training set from a spatial domain image to a frequency domain image; Each frequency domain image is truncated according to the effective frequency range of the optical system, and the effective frequency range is ,in, is the coherence factor, which is used to describe the coherence of the light source; Based on inverse Fourier transform, converting the truncated frequency domain image into a spatial domain image; Using a plurality of the spatial domain images as a target simulation image training set; The principal component analysis method is used to extract the eigenvalues ​​and eigenvectors of the target simulation image training set; Based on the order of the eigenvalues, feature extraction is performed on the eigenvectors to obtain a target feature vector; Constructing a linear regression matrix of the target feature vector and the Zernike coefficients; Performing Fourier transformation and frequency domain truncation on the measured photolithography image to obtain a measured spatial domain image; The measured spatial domain image and the linear regression matrix are fitted by the least square method to obtain the Zernike coefficients of the measured lithography image, and the lithography aberration is calibrated based on the Zernike coefficients of the measured lithography image.

2. The lithography aberration calibration method according to claim 1, characterized in that: The method of extracting the eigenvalues ​​and eigenvectors of the target simulation image training set by using the principal component analysis method includes: Representing the target simulation image training set as a data matrix; Calculating a covariance matrix of the data matrix; Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors.

3. The photolithography aberration calibration method according to claim 2, characterized in that: The step of constructing a linear regression matrix of the target feature vector and the Zernike coefficient comprises: Representing a plurality of said target feature vectors as feature matrices; Projecting the data matrix onto the feature matrix to form a principal component matrix; The linear regression matrix is ​​calculated based on the Zernike coefficients and the principal component matrix.

4. A photolithography aberration calibration device, characterized in that: include: An acquisition unit, used for acquiring an isolated mask image; A simulation unit is used to set photolithography conditions and a Zernike coefficient group, wherein the Zernike coefficient group includes a plurality of Zernike coefficient groups; calculate a plane light intensity distribution diagram of each group of the Zernike coefficients of the isolated mask image under the photolithography conditions by means of simulation software; and use the plurality of the plane light intensity distribution diagrams as an initial simulation image training set; A conversion unit is used to convert each of the plane light intensity distribution diagrams in the initial simulation image training set from a spatial domain image to a frequency domain image by using Fourier transform; and truncate each of the frequency domain images according to the effective frequency range of the optical system, wherein the effective frequency range is ,in, is a coherence factor used to describe the coherence of the light source; based on inverse Fourier transform, the truncated frequency domain image is converted into a spatial domain image; and a number of the spatial domain images are used as a target simulation image training set; A construction unit is used to extract the eigenvalues ​​and eigenvectors of the target simulation image training set by using the principal component analysis method; based on the size sorting of the eigenvalues, perform feature extraction on the eigenvectors to obtain a target eigenvector; and construct a linear regression matrix of the target eigenvector and the Zernike coefficient; The measuring unit is used to perform Fourier transform and frequency domain truncation on the measured lithography image to obtain a measured spatial domain image; use the least squares method to fit the measured spatial domain image and the linear regression matrix to obtain the Zernike coefficients of the measured lithography image, and calibrate the lithography aberration based on the Zernike coefficients of the measured lithography image.

5. A storage medium, characterized in that: The storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the lithography aberration calibration method according to any one of claims 1 to 3.

6. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the lithography aberration calibration method according to any one of claims 1 to 3 when executing the computer program.

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