Mask pattern generation method and device, storage medium and electronic device

Abstract

The application discloses a mask pattern generation method and device, a storage medium and an electronic device. The mask pattern generation method comprises the following steps: constructing a frequency domain imaging kernel of an optical system, and obtaining an original design pattern; calculating a target mask spectrum distribution diagram under the optical system according to the frequency domain imaging kernel and the original design pattern; obtaining a target binary mask pattern matched with the target mask spectrum distribution diagram by using a first LevelSet function based on the original design pattern; performing contour extraction on the target binary mask pattern to obtain a curve pattern; and performing Manhattan processing on the curve pattern to generate a target mask pattern. The present application can improve the frequency domain matching accuracy of the mask pattern.

Description

Mask pattern generation method, apparatus, storage medium and electronic equipment Technical Field

[0001] This application relates to the field of photolithography technology, specifically to a method, apparatus, storage medium, and electronic device for generating mask patterns. Background Technology

[0002] As semiconductor integrated circuit manufacturing processes continue to shrink to smaller feature sizes, the crucial role of photolithography in chip manufacturing becomes increasingly prominent. Currently used deep ultraviolet (DUV) and extreme ultraviolet (EUV) lithography techniques project design patterns onto the silicon wafer surface using a mask, forming circuit patterns with micron- or even nanometer-level precision. However, as process nodes continue to shrink, the optical imaging error between traditional mask patterns and chip designs is increasing, leading to problems such as pattern distortion, alignment deviations, and reduced resolution becoming increasingly prominent.

[0003] To compensate for diffraction and diffraction interference effects in optical systems, the industry widely employs Resolution Enhancement Technology (RET) and Optical Proximity Correction (OPC) methods to pre-correct the mask pattern. A typical OPC workflow includes the following steps: First, simulate exposure of the design pattern (e.g., using a Hopkins imaging model or a polarization imaging model) to calculate the optical response of the uncorrected mask on the photoresist; then, compare it with the target pattern, and correct the mask shape based on the deviation through geometric transformations (e.g., edge offset, endpoint extension, linewidth adjustment, etc.); finally, obtain the mask profile that most closely approximates the photoresist pattern with the design pattern based on an iterative optimization strategy.

[0004] However, most current correction methods make local adjustments in the spatial domain, making it difficult to achieve the best match from the perspective of the overall frequency domain distribution. As the feature size continues to approach the lithographic limit, the shortcomings of traditional OPC in frequency domain accuracy become increasingly apparent. Summary of the Invention

[0005] This application provides a mask pattern generation method, apparatus, storage medium, and electronic device, which can improve the frequency domain matching accuracy of mask patterns.

[0006] In a first aspect, embodiments of this application provide a method for generating a mask pattern, including:

[0007] Construct the frequency domain imaging kernel of the optical system and obtain the original design pattern;

[0008] Based on the frequency domain imaging kernel and the original design pattern, the target mask spectral distribution map under the optical system is calculated;

[0009] Based on the original design pattern, the first LevelSet function is used to optimize and obtain a target binary mask pattern that matches the target mask spectrum distribution map;

[0010] Contour extraction is performed on the target binarized mask pattern to obtain a curve pattern;

[0011] The curve pattern is subjected to Manhattanization to generate the target mask pattern.

[0012] In the mask pattern generation method provided in this application embodiment, the step of calculating the target mask spectral distribution map under the optical system based on the frequency domain imaging kernel and the original design pattern includes:

[0013] Substituting the frequency domain imaging kernel into the sigmoid function, a photoresist image of the wafer surface is obtained;

[0014] Using the original design pattern as the target, a first cost function is constructed based on the photoresist image;

[0015] The first cost function is solved iteratively using Newton's method to obtain the target mask spectrum distribution under the optical system.

[0016] In the mask pattern generation method provided in this application embodiment, the step of optimizing the original design pattern using the first LevelSet function to obtain a target binary mask pattern that matches the target mask spectral distribution map includes:

[0017] Using the boundaries of the original design pattern as initial conditions, construct the first LevelSet function;

[0018] The current binary mask pattern is obtained based on the first LevelSet function;

[0019] The current binarized mask pattern is iteratively optimized to generate the target binarized mask pattern.

[0020] In the mask pattern generation method provided in this application embodiment, the step of iteratively optimizing the current binarized mask pattern to generate a target binarized mask pattern includes:

[0021] A second cost function is constructed based on the current binarized mask pattern and the target mask spectral distribution map;

[0022] Calculate the update rate of the first LevelSet function based on the second cost function;

[0023] Based on the update speed, the first LevelSet function is updated in the spatial domain using forward Euler difference, and the process returns to the step of obtaining the current binary mask pattern based on the first LevelSet function until the convergence condition is met, thus obtaining the target binary mask pattern.

[0024] In the mask pattern generation method provided in this application embodiment, the step of performing Manhattanization on the curve pattern to generate the target mask pattern includes:

[0025] The outline of the curved pattern is approximated as a polygonal outline containing only horizontal or vertical sides to generate an initial Manhattan pattern.

[0026] Generate a target mask pattern based on the initial Manhattan pattern.

[0027] In the mask pattern generation method provided in this application embodiment, the step of constructing a frequency domain imaging kernel based on an optical system specifically includes:

[0028] in, The image exposure intensity is the frequency domain imaging kernel of the optical system; For imaging position; For the first The frequency domain expansion of an optical imaging kernel function; The corresponding weights; This is a spectrum distribution diagram of the mask; This represents the total number of optical imaging kernel functions; It is the inverse Fourier transform operator.

[0029] In the mask pattern generation method provided in this application embodiment, the step of substituting the frequency domain imaging kernel into the sigmoid function to obtain the photoresist image on the wafer surface specifically involves:

[0030]

[0031] in, This is a photoresist image of the wafer surface. For the frequency domain imaging kernel of the optical system, These are control parameters used to determine the exposure intensity in the photoresist.

[0032] Secondly, embodiments of this application provide a mask pattern generation apparatus, comprising:

[0033] The building unit is used to construct the frequency domain imaging kernel of the optical system and obtain the original design pattern;

[0034] The calculation unit is used to calculate the target mask spectrum distribution map under the optical system based on the frequency domain imaging kernel and the original design pattern;

[0035] A matching unit is used to optimize and obtain a target binary mask pattern that matches the target mask spectrum distribution map based on the original design pattern using the first LevelSet function.

[0036] An extraction unit is used to extract the contour of the target binary mask pattern to obtain a curve pattern;

[0037] The generation unit is used to perform Manhattanization on the curve pattern to generate a target mask pattern.

[0038] Thirdly, this application provides a storage medium storing a plurality of instructions adapted for loading by a processor to execute the mask pattern generation method described in any of the preceding claims.

[0039] Fourthly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the mask pattern generation method described in any of the preceding claims.

[0040] In summary, the mask pattern generation method provided in this application includes constructing a frequency domain imaging kernel of an optical system and obtaining an original design pattern; calculating a target mask spectral distribution map under the optical system based on the frequency domain imaging kernel and the original design pattern; optimizing a target binary mask pattern matching the target mask spectral distribution map using a first LevelSet function based on the original design pattern; extracting the contour of the target binary mask pattern to obtain a curve pattern; and performing Manhattanization on the curve pattern to generate the target mask pattern. This solution can improve the frequency domain matching accuracy of the mask pattern. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 is a schematic diagram of an application scenario of the mask pattern generation method provided in the embodiments of this application.

[0043] Figure 2 is a flowchart illustrating the mask pattern generation method provided in an embodiment of this application.

[0044] Figure 3 is a schematic diagram of the mask pattern generation device provided in the embodiment of this application.

[0045] Figure 4 is a schematic diagram of the structure of the electronic device provided in an embodiment of this application. Detailed Implementation

[0046] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0047] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.

[0048] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0049] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.

[0050] In the description of this application, it should be noted that the terms "upper," "lower," "left," "right," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. In addition, terms such as "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0051] Most current correction methods make local adjustments in the spatial domain, making it difficult to achieve optimal matching from the perspective of the overall frequency domain distribution. As the feature size continues to approach the lithography limit, the shortcomings of traditional OPC in frequency domain accuracy become increasingly apparent.

[0052] Based on this, embodiments of this application provide a mask pattern generation method, apparatus, storage medium, and electronic device. Specifically, the mask pattern generation apparatus can be integrated into an electronic device, which can be a server or a terminal, etc. The terminal can include mobile phones, wearable smart devices, tablet computers, laptops, and personal computers (PCs), 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.

[0053] For example, as shown in Figure 1, the electronic device can acquire the original design pattern and construct a frequency domain imaging kernel based on the optical system; calculate the target mask spectrum distribution map under the optical system according to the frequency domain imaging kernel and the original design pattern; optimize the target binarized mask pattern matching the target mask spectrum distribution map using the first LevelSet function based on the original design pattern; extract the contour of the target binarized mask pattern to obtain the curve pattern; perform Manhattanization on the curve pattern to generate the target mask pattern.

[0054] The technical solutions shown in this application will be described in detail below through specific embodiments. It should be noted that the order of description of the following embodiments is not intended to limit the priority of the embodiments.

[0055] Please refer to Figure 2, which is a schematic flowchart of the mask pattern generation method provided in an embodiment of this application. The specific flow of the mask pattern generation method is as follows:

[0056] 101. Construct the frequency domain imaging kernel of the optical system and obtain the original design pattern.

[0057] Specifically, the Hopkins imaging theory can be used to model the optical system and obtain the frequency domain imaging kernel of the optical system.

[0058] For example, when the mask spectral distribution is When the normalized image exposure intensity I(x,y) is, it can be expressed as:

[0059] (1)

[0060] In Equation 1, The image exposure intensity is the frequency domain imaging kernel of the optical system; For imaging position; For the first The frequency domain expansion of an optical imaging kernel function; The corresponding weights; This represents the total number of optical imaging kernel functions; It is the inverse Fourier transform operator.

[0061] Original design pattern refers to the binary target graphic drawn by the designer in CAD software and stored in the form of layout file (such as GDSII, OASIS, etc.) in the process of integrated circuit or photolithography. It is the circuit pattern or micro / nano structure that is expected to be exposed and formed on the wafer.

[0062] 102. Based on the frequency domain imaging kernel and the original design pattern, the target mask spectrum distribution under the optical system is calculated.

[0063] In some embodiments, step 102 may include the following steps:

[0064] (1) Substitute the frequency domain imaging kernel into the sigmoid function to obtain the photoresist image on the wafer surface.

[0065] By approximating the photoresist thresholding response of the frequency domain imaging kernel I(x,y) using a Sigmoid function (photoresist thresholding model), the photoresist image on the wafer surface can be obtained. This can be represented as:

[0066] (2)

[0067] In Equation 2, These are control parameters used to determine the exposure intensity in the photoresist.

[0068] (2) Using the original design pattern as the target, construct the first cost function based on the photoresist image.

[0069] In some embodiments, it can be represented as:

[0070] (3)

[0071] In Equation 3, The target photoresist image corresponding to the original design pattern.

[0072] (3) The first cost function is solved iteratively using the Newton method to obtain the target mask spectrum distribution under the optical system.

[0073] The specific iterative process of Newton's method can be found in existing technologies, and will not be elaborated upon in this embodiment. It should be noted that the convergence condition for this iterative solution is that the absolute value of the difference between two consecutive first cost functions is less than or equal to a preset threshold, or the maximum number of iterations is reached.

[0074] It is understandable that the target mask spectrum distribution map is the "optimal mask spectrum distribution map", which can minimize the difference from the original design pattern in the imaging system.

[0075] 103. Based on the original design pattern, the target binarized mask pattern matching the target mask spectrum distribution map is obtained by using the first LevelSet function.

[0076] In some embodiments, step 103 may include:

[0077] (1) Construct the first LevelSet function with the boundary of the original design pattern as the initial condition.

[0078] Specifically, the boundaries in the original design pattern can be extracted, and then the signed distance function (SDF) from each point (x, y) to the boundary can be calculated to obtain the initial first LevelSet function. It can be represented as:

[0079] (4)

[0080] In Equation 4, the formula is as follows: Indicates the edge of the mask pattern; Indicates the interior of the mask pattern; Indicates the outside of the mask pattern; Represents any point in space The minimum distance to the edge of the mask pattern. Understandably, during initialization, the "mask pattern" is the same as the original design pattern.

[0081] (2) Obtain the current binary mask pattern based on the first LevelSet function.

[0082] Based on the first LevelSet function This allows us to obtain the current binarized mask pattern. It can be represented as:

[0083] (5)

[0084] In Equation 5, This indicates the light-blocking value of the mask; This indicates the light transmittance value of the mask.

[0085] (3) Iteratively optimize the current binarized mask pattern to generate the target binarized mask pattern.

[0086] For example, a second cost function can be constructed based on the current binarized mask pattern and the target mask spectral distribution map, as follows:

[0087] The second cost function It can be represented as:

[0088] (6)

[0089] In Equation 6, This indicates that a Fourier transform is performed on the current binary mask pattern; denoted by Frobenius norm for complex matrices.

[0090] Then, based on the second cost function, the update rate of the first LevelSet function is calculated.

[0091] Specifically, the update rate of the first LevelSet function is calculated based on the second cost function. It can be represented as:

[0092] (7)

[0093] In Equation 7, This represents the gradient operator. In this embodiment, numerical difference is used instead of the gradient operator.

[0094] Finally, based on the update speed, the first LevelSet function is updated in the spatial domain using forward Euler difference, and the process returns to the step of obtaining the current binary mask pattern based on the first LevelSet function until the convergence condition is met, thus obtaining the target binary mask pattern.

[0095] Among them, based on the update speed, the first LevelSet function is updated in the spatial domain using forward Euler difference, which can be expressed as:

[0096] (8)

[0097] In this embodiment, forward Euler difference is used instead of time partial derivative to obtain the pixel value at the next time step of the first LevelSet function. The convergence condition is that the difference between the spectrum of the current binarized mask pattern and the spectrum distribution of the target mask satisfies the error threshold or reaches the maximum number of iterations.

[0098] 104. Extract the contour of the target binarized mask pattern to obtain the curve pattern.

[0099] Once the first LevelSet function converges, it can be used The contour of the target binary mask pattern is extracted using contour lines with a value of 0, resulting in a set of continuous and smooth curve patterns. These curve patterns match the target mask's spectral distribution well in terms of frequency spectrum, but do not yet meet the manufacturing requirements for Manhattanization (only horizontal or vertical edges).

[0100] Therefore, after obtaining the curve pattern, it needs to be Manhattanized and further optimized under the imaging system to ensure that the final pattern conforms to the process rules and meets the exposure accuracy requirements.

[0101] 105. Perform Manhattanization on the curve pattern to generate the target mask pattern.

[0102] First, the outline of the curved pattern can be approximated as a polygonal outline containing only horizontal or vertical sides to generate an initial Manhattan pattern. A common method involves projecting the curve segment onto a grid and rounding it to the nearest horizontal or vertical direction, resulting in a stepped polygon. This initial Manhattan pattern only meets the geometric requirements of the process rules, but its spectrum may deviate from the target mask's spectrum distribution, requiring further optimization. Therefore, the target mask pattern can then be generated based on this initial Manhattan pattern.

[0103] Specifically, a second LevelSet function can be introduced, whose zero level set corresponds to the boundary of the initial Manhattan pattern, and constrains its outline to contain only horizontal and vertical edges.

[0104] That is, a second LevelSet function can be constructed based on the initial Manhattan pattern. Then, a new binarized mask pattern is obtained according to Equation 5. Substituting these values ​​into Equations 1 and 2 yields the photoresist image on the wafer surface. Finally, the cost function is calculated using Equation 3 and used in the second LevelSet function. Specifically, it can be expressed as follows:

[0105] (9)

[0106] Iterative calculations are performed according to formulas (5)->(6)->(7)->(8) until the difference between the spectrum of the new binarized mask pattern and the spectrum distribution of the target mask meets the error threshold or the maximum number of iterations is reached, thus obtaining the Manhattanized target mask pattern.

[0107] In some embodiments, the target mask pattern can also be substituted into formula (3) to obtain the cost function of the target mask pattern, which is used to evaluate design and manufacturability. Verify the effectiveness of the LevelSet function.

[0108] This approach addresses the shortcomings of traditional OPC methods, which only perform local geometric corrections in the spatial domain and struggle to account for higher-order diffraction and interference effects, by performing global optimization at the frequency domain level. This improves the frequency domain matching accuracy of the mask pattern. Specifically, the original design pattern is first simulated using the Hopkins imaging model and the photoresist thresholding model. Newton's method is then used to accurately solve for the target mask's spectral distribution, allowing the mask's spectral distribution to directly reflect the energy distribution required to reproduce the design pattern under the current optical system. In this way, subsequent LevelSet iterations only need to continuously approximate the target mask's spectral distribution in the frequency domain, simultaneously absorbing the effects of higher-order diffraction terms and coherent noise on imaging, fundamentally reducing image distortion and resolution loss.

[0109] Subsequently, the entire curve weights can be continuously optimized in the global spectrum sense during the iteration of the second LevelSet function, avoiding the drawback of traditional methods where energy in other frequency bands is destroyed when corrections are made in certain regions. Then, Manhattanization constraints are introduced to constrain the contour of the curve pattern to horizontal and vertical edges within the same LevelSet framework, realizing an integrated design of "spectrum matching + Manhattan". This ensures that the final mask not only meets the extreme resolution requirements of advanced process nodes, but also strictly complies with the manufacturability requirements of horizontal / vertical edges in mask manufacturing.

[0110] In summary, the mask pattern generation method provided in this application includes constructing a frequency domain imaging kernel of an optical system and obtaining an original design pattern; calculating the target mask spectral distribution map under the optical system based on the frequency domain imaging kernel and the original design pattern; optimizing the target binarized mask pattern to match the target mask spectral distribution map using a first LevelSet function based on the original design pattern; extracting the contour of the target binarized mask pattern to obtain a curve pattern; and performing Manhattanization on the curve pattern to generate the target mask pattern. This solution can improve the frequency domain matching accuracy of the mask pattern.

[0111] To facilitate better implementation of the mask pattern generation method provided in this application, this application also provides a mask pattern generation apparatus. The meanings of the terms used are the same as in the mask pattern generation method described above, and specific implementation details can be found in the descriptions within the method embodiments.

[0112] Please refer to Figure 3, which is a schematic diagram of the mask pattern generation apparatus provided in an embodiment of this application. The mask pattern generation apparatus may include a construction unit 201, a calculation unit 202, a matching unit 203, an extraction unit 204, and a generation unit 205.

[0113] The construction unit 201 is used to construct the frequency domain imaging kernel of the optical system and obtain the original design pattern;

[0114] The computing unit 202 is used to calculate the target mask spectrum distribution map under the optical system based on the frequency domain imaging kernel and the original design pattern.

[0115] Matching unit 203 is used to optimize the target binary mask pattern based on the original design pattern using the first LevelSet function to obtain the target mask spectrum distribution map.

[0116] Extraction unit 204 is used to extract the contour of the target binary mask pattern to obtain a curve pattern;

[0117] The generation unit 205 is used to perform Manhattanization on the curve pattern to generate the target mask pattern.

[0118] For specific implementation methods of each of the above units, please refer to the embodiments of the mask pattern generation method described above, which will not be repeated here.

[0119] In summary, the mask pattern generation apparatus provided in this application embodiment can construct the frequency domain imaging kernel of the optical system by the construction unit 201 and obtain the original design pattern; the calculation unit 202 calculates the target mask spectrum distribution map under the optical system based on the frequency domain imaging kernel and the original design pattern; the matching unit 203 optimizes the target binary mask pattern matching the target mask spectrum distribution map based on the original design pattern using the first LevelSet function; the extraction unit 204 extracts the contour of the target binary mask pattern to obtain a curve pattern; and the generation unit 205 performs Manhattanization processing on the curve pattern to generate the target mask pattern.

[0120] This application also provides an electronic device that may integrate the mask pattern generating device of this application, as shown in FIG4, which illustrates the structural schematic diagram of the electronic device involved in this application. Specifically:

[0121] The electronic device may include components such as a processor 301 with one or more processing cores and a memory 302 with one or more computer-readable storage media. Those skilled in the art will understand that the electronic device structure shown in FIG4 does not constitute a limitation on the electronic device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein:

[0122] The processor 301 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs stored in the memory 302 and / or this application, and by calling data stored in the memory 302, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operation of the storage medium, user interface, and application programs, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 301.

[0123] 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 may mainly include a program storage area and a data storage area. The program storage area may store applications required for operating the storage medium and at least one function; the data storage area may store data created based on 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, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0124] Although not shown, the electronic device may also include a display unit, an input unit, and a power supply, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the electronic device loads the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 runs the application programs stored in the memory 302 to realize various functions, as follows:

[0125] Obtain the original design pattern and construct a frequency domain imaging kernel based on the optical system;

[0126] Based on the frequency domain imaging kernel and the original design pattern, the target mask spectral distribution map under the optical system is calculated;

[0127] Based on the original design pattern, the first LevelSet function is used to optimize and obtain the target binarized mask pattern that matches the target mask spectrum distribution map.

[0128] Contour extraction is performed on the target binarized mask pattern to obtain the curve pattern;

[0129] The curved pattern is Manhattanized to generate the target mask pattern.

[0130] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0131] Therefore, embodiments of this application provide a storage medium storing a plurality of instructions that can be loaded by a processor to execute steps in any of the methods provided in embodiments of this application. For example, the instructions can execute the following steps:

[0132] Obtain the original design pattern and construct a frequency domain imaging kernel based on the optical system;

[0133] Based on the frequency domain imaging kernel and the original design pattern, the target mask spectral distribution map under the optical system is calculated;

[0134] Based on the original design pattern, the first LevelSet function is used to optimize and obtain the target binarized mask pattern that matches the target mask spectrum distribution map.

[0135] Contour extraction is performed on the target binarized mask pattern to obtain the curve pattern;

[0136] The curved pattern is Manhattanized to generate the target mask pattern.

[0137] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0138] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0139] Since the instructions stored in the storage medium can execute the steps of any method provided in the embodiments of this application, the beneficial effects that any method provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0140] The mask pattern generation method, apparatus, storage medium, and electronic device provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for generating a mask pattern, characterized in that, include: Obtain the original design pattern; and construct a frequency domain imaging kernel based on the optical system, specifically: in, The image exposure intensity is the frequency domain imaging kernel of the optical system; For imaging position; For the first The frequency domain expansion of an optical imaging kernel function; The corresponding weights; This is a spectrum distribution diagram of the mask; This represents the total number of optical imaging kernel functions; The inverse Fourier transform operator is used; substituting the frequency domain imaging kernel into the sigmoid function yields the photoresist image on the wafer surface, specifically: ;in, This is a photoresist image of the wafer surface. For the frequency domain imaging kernel of the optical system, To control parameters used to determine the exposure intensity in the photoresist; taking the original design pattern as the target, a first cost function is constructed based on the photoresist image; the first cost function is iteratively solved using Newton's method to obtain the target mask spectrum distribution map under the optical system; based on the original design pattern, a target binary mask pattern matching the target mask spectrum distribution map is obtained using the first LevelSet function; the target binary mask pattern is contour extracted to obtain a curve pattern; the curve pattern is Manhattanized to generate the target mask pattern.

2. The mask pattern generation method as described in claim 1, characterized in that, The step of optimizing the target binary mask pattern based on the original design pattern using the first LevelSet function to match the target mask spectrum distribution map includes: constructing the first LevelSet function with the boundary of the original design pattern as the initial condition; obtaining the current binary mask pattern based on the first LevelSet function; and iteratively optimizing the current binary mask pattern to generate the target binary mask pattern.

3. The mask pattern generation method as described in claim 2, characterized in that, The iterative optimization of the current binarized mask pattern to generate the target binarized mask pattern includes: constructing a second cost function based on the current binarized mask pattern and the target mask spectral distribution map; calculating the update rate of the first LevelSet function based on the second cost function; updating the first LevelSet function in the spatial domain using forward Euler difference based on the update rate, and returning to execute the step of obtaining the current binarized mask pattern based on the first LevelSet function until the convergence condition is met, thereby obtaining the target binarized mask pattern.

4. The mask pattern generation method as described in claim 1, characterized in that, The step of performing Manhattanization on the curve pattern to generate a target mask pattern includes: approximating the outline of the curve pattern as a polygonal outline containing only horizontal or vertical sides to generate an initial Manhattan pattern; and generating a target mask pattern based on the initial Manhattan pattern.

5. A mask pattern generating apparatus, characterized in that, include: The building unit is used to acquire the original design pattern; and a frequency domain imaging kernel is constructed based on the optical system, specifically: in, The image exposure intensity is the frequency domain imaging kernel of the optical system; For imaging position; For the first The frequency domain expansion of an optical imaging kernel function; The corresponding weights; This is a spectrum distribution diagram of the mask; This represents the total number of optical imaging kernel functions; The inverse Fourier transform operator; the computational unit, used to substitute the frequency domain imaging kernel into the sigmoid function to obtain the photoresist image on the wafer surface, specifically: ;in, This is a photoresist image of the wafer surface. For the frequency domain imaging kernel of the optical system, The system comprises the following components: a control parameter used to determine the exposure intensity in the photoresist; a first cost function constructed based on the photoresist image with the original design pattern as the target; an iterative solution of the first cost function using Newton's method to obtain the target mask spectrum distribution map under the optical system; a matching unit used to optimize the original design pattern using a first LevelSet function to obtain a target binary mask pattern matching the target mask spectrum distribution map; an extraction unit used to extract the contour of the target binary mask pattern to obtain a curve pattern; and a generation unit used to perform Manhattanization processing on the curve pattern to generate the target mask pattern.

6. A storage medium, characterized in that, The storage medium stores a plurality of instructions, which are adapted for loading by a processor to execute the mask pattern generation method according to any one of claims 1-4.

7. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the mask pattern generation method as described in any one of claims 1-4.

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