Optical Proximity Effect Correction Method, Device, Storage Medium and Electronic Device
By combining the Hopkins optical transfer matrix and offset prediction model, the problem of high computational complexity in traditional OPC under small feature sizes is solved, efficient optical proximity effect correction is achieved, and the yield rate of chip manufacturing is improved.
Patent Information
- Application Number
- CN202510706877.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-29
- Publication Date
- 2025-08-05
- Estimated Expiration
- 2045-05-29
AI Technical Summary
The traditional optical proximity effect correction method has high computational complexity and poor convergence under small feature sizes, which affects the yield rate of chip manufacturing.
The method of combining Hopkins optical transfer matrix and offset prediction model is adopted to calculate the Jacobian matrix by discretizing the mask boundary, and the offset prediction model is used to predict the boundary segment offset, and the boundary position is adjusted in combination with the Newton iterative method to generate a modified mask layout.
The calculation complexity of optical proximity effect correction is reduced, the optical imaging accuracy and convergence are improved, and the yield rate of chip manufacturing is improved.
Smart Images
Figure CN120215202B_ABST
Abstract
Description
Technical Field
[0001] The embodiments of the present application relate to the field of photolithography technology, and specifically to an optical proximity effect correction method, device, storage medium, and electronic device. Background Art
[0002] As integrated circuit manufacturing processes advance, chip feature sizes continue to shrink, exacerbating challenges faced by lithography systems. Due to the low-pass filtering characteristics of lithography systems, the transfer of mask patterns onto silicon wafers is severely distorted, impacting chip manufacturing yields.
[0003] Optical Proximity Correction (OPC) is a method that compensates for distortion caused by optical system diffraction and process nonlinearity by modifying the mask layout. However, as feature sizes decrease, traditional OPC faces problems such as high computational complexity and poor convergence. Summary of the Invention
[0004] The embodiments of the present application provide an optical proximity effect correction method, device, storage medium, and electronic device, which can reduce the computational complexity of OPC.
[0005] In a first aspect, an embodiment of the present application provides a method for correcting an optical proximity effect, comprising:
[0006] Obtaining the mask layout to be corrected;
[0007] Discretizing the mask boundary of the mask layout to be corrected to obtain a plurality of first boundary segments;
[0008] Calculating the Jacobian matrix of each first boundary segment based on the Hopkins optical transfer matrix;
[0009] Inputting the Jacobian matrix into an offset prediction model to predict the offset of each first boundary segment;
[0010] The position of the first boundary segment is adjusted according to the offset to generate a corrected mask layout.
[0011] In the optical proximity effect correction method provided in an embodiment of the present application, adjusting the position of the first boundary segment according to the offset to generate a corrected mask layout includes:
[0012] Based on the offset, updating the position corresponding to the first boundary segment by Newton iteration method until a convergence condition is met;
[0013] When the convergence condition is reached, a corrected mask layout is generated.
[0014] In the optical proximity effect correction method provided in an embodiment of the present application, updating the position of the first boundary segment by the Newton iteration method based on the offset until a convergence condition is reached includes:
[0015] updating a position corresponding to the first boundary segment according to the offset;
[0016] updating the transmission spectrum of the mask to be corrected based on the updated first boundary segment;
[0017] Calculating a current exposure error according to the Hopkins optical transfer matrix and the updated transmission spectrum;
[0018] Based on the current exposure error, the position corresponding to the first boundary segment is updated by Newton iteration method until a convergence condition is met.
[0019] In the optical proximity effect correction method provided in an embodiment of the present application, updating the position of the first boundary segment by the Newton iteration method based on the current exposure error until a convergence condition is reached includes:
[0020] When the current exposure error is greater than a threshold, returning to the step of calculating the Jacobian matrix of each first boundary segment based on the Hopkins optical transfer matrix;
[0021] When the current exposure error is less than or equal to the threshold, it is determined that the convergence condition is met.
[0022] The optical proximity effect correction method provided in the embodiment of the present application further includes:
[0023] Construct the Hopkins optical transfer matrix;
[0024] Build an offset prediction model.
[0025] In the optical proximity effect correction method provided in the embodiment of the present application, the step of constructing the offset prediction model includes:
[0026] Obtain test mask layout;
[0027] Discretizing the mask boundary of the test mask layout to obtain a plurality of second boundary segments;
[0028] Calculating the Jacobian matrix and the Hessian matrix of each second boundary segment based on the Hopkins optical transfer matrix;
[0029] Calculate the true offset of each second boundary segment based on the Hessian matrix;
[0030] The actual offset is used as a training label and the Jacobian matrix is used as input data to train the preset Unet network model to generate an offset prediction model.
[0031] In the optical proximity effect correction method provided in the embodiment of the present application, the constructing of the Hopkins optical transfer matrix includes:
[0032] Obtain optical parameters of the lithography system;
[0033] According to the optical parameters, an optical imaging system is established using a Hopkins imaging model;
[0034] A Hopkins optical transfer matrix is calculated based on the optical imaging system.
[0035] In a second aspect, an embodiment of the present application provides an optical proximity effect correction device, comprising:
[0036] An acquisition unit, used for acquiring the mask pattern to be corrected;
[0037] A discretization unit is used to discretize the mask boundary of the mask layout to be corrected to obtain a plurality of first boundary segments;
[0038] a calculation unit, configured to calculate the Jacobian matrix of each first boundary segment based on the Hopkins optical transfer matrix;
[0039] a prediction unit, configured to input the Jacobian matrix into an offset prediction model to predict an offset of each first boundary segment;
[0040] The correction unit is used to adjust the position of the first boundary segment according to the offset to generate a corrected mask layout.
[0041] In a third aspect, the present application provides a storage medium storing a plurality of instructions, wherein the instructions are suitable for loading by a processor to execute any of the above-mentioned optical proximity effect correction methods.
[0042] In a fourth aspect, 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 the processor implements any of the above-described optical proximity effect correction methods when executing the computer program.
[0043] In summary, the optical proximity effect correction method provided in the embodiments of the present application includes obtaining a mask layout to be corrected; discretizing the mask boundaries of the mask layout to be corrected to obtain a plurality of first boundary segments; calculating the Jacobian matrix for each of the first boundary segments based on the Hopkins optical transfer matrix; inputting the Jacobian matrix into an offset prediction model to predict the offset of each first boundary segment; and adjusting the position of the corresponding first boundary segment based on the offset to generate a corrected mask layout. By integrating a physical model (the Hopkins optical transfer matrix) with a neural network model (the offset prediction model), this solution reduces the computational complexity of OPC while maintaining optical imaging accuracy. BRIEF DESCRIPTION OF THE DRAWINGS
[0044] 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 description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.
[0045] Figure 1 This is a schematic diagram of an application scenario of the optical proximity effect correction method provided in an embodiment of the present application.
[0046] Figure 2 4 is a flow chart of the optical proximity effect correction method provided in an embodiment of the present application.
[0047] Figure 3 It is a structural diagram of the offset prediction model provided in the embodiment of the present application.
[0048] Figure 4 Schematic diagram of the structure of the optical proximity effect correction device provided in an embodiment of the present application.
[0049] Figure 5 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0050] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
[0051] It should be noted that, in this document, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the 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 by their explanation in the specific embodiment or further combined with the context of the specific embodiment.
[0052] It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.
[0053] In the subsequent description, the use of suffixes such as "module", "component" or "unit" to represent elements is only for the purpose of facilitating the description of the present application and has no specific meaning. Therefore, "module", "component" or "unit" can be used interchangeably.
[0054] In the description of this application, it should be noted that the terms "upper," "lower," "left," "right," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate the description of this application and simplify the description. They are not intended to indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, terms such as "first" and "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.
[0055] OPC is a method that compensates for distortion caused by optical system diffraction and process nonlinearity by modifying the mask layout. However, as feature sizes decrease, traditional OPC faces problems such as high computational complexity and poor convergence.
[0056] Based on this, the embodiments of the present application provide an optical proximity effect correction method, device, storage medium and electronic device. Specifically, the optical proximity effect correction device can be integrated into an electronic device, and the electronic device can be a server or a terminal; wherein the terminal can include a mobile phone, a wearable smart 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.
[0057] For example, Figure 1 As shown, the electronic device can obtain a mask layout to be corrected; discretize the mask boundary of the mask layout to be corrected to obtain a plurality of first boundary segments; calculate the Jacobian matrix of each first boundary segment based on the Hopkins optical transfer matrix; input the Jacobian matrix into an offset prediction model to predict the offset of each first boundary segment; adjust the position of the corresponding first boundary segment according to the offset, and generate a corrected mask layout.
[0058] The following will describe the technical solutions of this application in detail through specific embodiments. It should be noted that the order of description of the following embodiments does not limit the priority order of the embodiments.
[0059] See also Figure 2 , Figure 2 : is a flow chart of the optical proximity effect correction method provided by an embodiment of the present application. The specific process of the optical proximity effect correction method can be as follows:
[0060] 101. Obtain the mask pattern to be corrected.
[0061] version Figure 1 Typically created using chip design software (such as Cadence, Mentor Graphics, Synopsys, etc.). In the semiconductor manufacturing industry, to ensure standardization and compatibility of design data, specific file format standards such as GDS (Graphic Data System) or OASIS (Open Artwork System Interchange Standard) are often used to save these layouts.
[0062] In this embodiment, the mask pattern to be corrected refers to a mask pattern whose current exposure error is greater than a threshold.
[0063] In some embodiments, a mask layout to be processed can be first acquired, and then Fourier transformed to obtain its transmission spectrum, i.e., the spectral distribution of the mask transmission coefficient. The current exposure intensity of the mask layout to be processed is then calculated based on the Hopkins optical transfer matrix and the transmission spectrum. Finally, the current exposure intensity is compared with the target exposure intensity to obtain the current exposure error. When the current exposure error is greater than a threshold, it can be determined that the mask layout to be processed is a mask layout to be corrected and requires OPC. When the current exposure error is less than or equal to the threshold, it can be determined that OPC is not required for the mask layout to be processed.
[0064] It should be noted that the Hopkins optical transfer matrix is constructed in advance, and the specific construction process will be described in detail in subsequent embodiments and will not be repeated here.
[0065] The target exposure intensity can be set according to actual conditions and is not limited in the embodiments of the present application.
[0066] 102. Discretize the mask boundary of the mask layout to be corrected to obtain a plurality of first boundary segments.
[0067] In some embodiments, the mask boundary of the mask layout to be corrected may be grid-segmented according to preset pixels, thereby obtaining a plurality of discretized first boundary segments.
[0068] 103. Based on the Hopkins optical transfer matrix, calculate the Jacobian matrix of each first boundary segment.
[0069] It can be understood that the Jacobian matrix can reflect the local impact of the position change of the corresponding boundary segment on the light intensity distribution.
[0070] The Hopkins optical transfer matrix is constructed in advance, and its specific construction process can be to obtain optical parameters of the lithography system; establish an optical imaging system using the Hopkins imaging model according to the optical parameters; and calculate the Hopkins optical transfer matrix based on the optical imaging system.
[0071] The optical parameters may include exposure wavelength, numerical aperture (NA), light source distribution, and polarization state.
[0072] Specifically, the optical parameters of the lithography system can be obtained first. Next, a pupil function is established based on these optical parameters to define the lens system's transmission characteristics for different spatial frequencies. The light source cross-correlation function is then calculated to describe the degree of correlation between different spectra. Finally, a complete optical imaging system is established through convolution of the mask transmission spectrum to predict the exposure intensity of the modified mask pattern on the silicon wafer.
[0073] In some embodiments, the exposure intensity can be expressed as:
[0074] (1).
[0075] In formula (1), is the exposure intensity; is the imaging position; is the optical cross transfer function; is the spatial sampling frequency; is the transmission spectrum of the mask.
[0076] in, In this formula, is the light source cross-correlation function, is the light function.
[0077] For optical imaging systems, After expansion, it can be expressed as:
[0078] (2).
[0079] In formula (2), is the imaging kernel of the optical imaging system (Hopkins optical transfer matrix), For the The weights corresponding to the kernels. is the total number of equivalent convolution kernels.
[0080] At this point, the current exposure intensity of the mask to be corrected can be expressed as:
[0081] (3).
[0082] It can be understood that Equations (1) to (3) are mathematical expressions of the construction process of the Hopkins optical transfer matrix.
[0083] OPC is to correct the offset of each boundary segment so that the cost function (exposure error) Minimum:
[0084] (4)
[0085] Where I(x, y, M) is the current exposure intensity of the mask to be corrected, and T(x, y) is the target exposure intensity.
[0086] The matrix form of several boundary segments of the mask M(f,g) to be corrected can be expressed as follows:
[0087] (5).
[0088] In formula (5), Represents the two-dimensional Fourier transform of the matrix elements.
[0089] Newton's iteration method can be expressed as: (6).
[0090] In formula (6), represents the correlation matrix of all boundary segments; is the number of iterations; is the Jacobian matrix, is the Hessian matrix, the superscript Represents matrix inversion.
[0091] in, (7).
[0092] (8).
[0093] In formula (7) and formula (8), Represents spatial pixels location, , ( Indicates the pixel position number in the x,y direction, Indicates the number of pixels in the x direction); represents the spatial edge position, ( represent the spatial numbers corresponding to the edges respectively).
[0094] It can be understood that by substituting Equations (7) and (8) into Equation (6), it is possible to iteratively update the position of the boundary segment and realize the OPC of the mask layout to be corrected.
[0095] 104. Input the Jacobian matrix into the offset prediction model to predict the offset of each first boundary segment.
[0096] It is understandable that the traditional Newton method requires calculating the inverse of the Hessian matrix. However, the Hessian matrix is a second-order partial derivative, which has a high computational complexity and limits the application of the Newton method.
[0097] Based on this, the embodiment of the present application uses the Jacobian matrix with lower computational complexity as input and the true offset corresponding to the Hessian matrix as the training label to train the preset Unet network model, thereby obtaining an offset prediction model that can predict the true offset corresponding to the Hessian matrix.
[0098] Specifically, the construction process of the offset prediction model can be: obtaining a test mask layout; discretizing the mask boundaries of the test mask layout to obtain a number of second boundary segments; calculating the Jacobian matrix and Hessian matrix of each second boundary segment based on the Hopkins optical transfer matrix; calculating the true offset of each second boundary segment based on the Hessian matrix; using the true offset as a training label and the Jacobian matrix as input data, training the preset Unet network model to generate an offset prediction model.
[0099] The size of the second boundary segment is the same as that of the first boundary segment.
[0100] Among them, the preset Unet network model can be as follows Figure 3 As shown in the figure, there are three descending layers, which are implemented by convolutional neural network (CNN); three up-converging layers (CNN), and each layer uses a fully connected network with sigmoid activation function for correction. The sigmoid activation function is:
[0101] .
[0102] It is understandable that after the training is completed, the Jacobian matrix of each first boundary segment is input into the offset prediction model to predict the offset of each first boundary segment.
[0103] 105. Adjust the position of the corresponding first boundary segment according to the offset to generate a corrected mask layout.
[0104] Specifically, the position of the corresponding first boundary segment can be updated based on the offset through the Newton iteration method until a convergence condition is reached; when the convergence condition is reached, a corrected mask layout is generated.
[0105] Among them, the convergence condition refers to the cost function is less than or equal to the threshold; or the number of iterations reaches the maximum number of iterations.
[0106] The step of “updating the position of the corresponding first boundary segment by the Newton iteration method based on the offset until the convergence condition is met” may specifically be:
[0107] Update the position of the corresponding first boundary segment according to the offset;
[0108] Based on the updated first boundary segment, updating the transmission spectrum of the mask to be corrected;
[0109] Calculate the current exposure error based on the Hopkins optical transfer matrix and the updated transmission spectrum;
[0110] Based on the current exposure error, the position of the corresponding first boundary segment is updated by the Newton iteration method until the convergence condition is met.
[0111] The specific calculation process of the current exposure error can be found in step 101 and will not be described in detail here.
[0112] It can be understood that when the current exposure error is greater than the threshold, the step of calculating the Jacobian matrix of each first boundary segment based on the Hopkins optical transfer matrix is returned to execution; when the current exposure error is less than or equal to the threshold, it is determined that the convergence condition is met.
[0113] In summary, the optical proximity effect correction method provided in the embodiments of the present application includes obtaining a mask layout to be corrected; discretizing the mask boundaries of the mask layout to be corrected to obtain a plurality of first boundary segments; calculating the Jacobian matrix of each first boundary segment based on the Hopkins optical transfer matrix; inputting the Jacobian matrix into an offset prediction model to predict the offset of each first boundary segment; and adjusting the position of the corresponding first boundary segment based on the offset to generate a corrected mask layout. Traditional OPC relies on the Newton iteration method to calculate the Hessian matrix, and its complexity increases exponentially with the number of boundary segments. This solution reduces computational complexity by directly predicting the offset corresponding to the Hessian matrix using an offset prediction model. In other words, by integrating a physical model (the Hopkins optical transfer matrix) with a neural network model (the offset prediction model), this solution overcomes the computational bottleneck of traditional OPC while ensuring optical imaging accuracy and reduces its computational complexity.
[0114] To facilitate better implementation of the optical proximity effect correction method provided in the embodiments of the present application, the embodiments of the present application also provide an optical proximity effect correction device. The meanings of the terms herein are the same as those in the aforementioned optical proximity effect correction method, and the specific implementation details can be referred to the description in the method embodiment.
[0115] See also Figure 4 , Figure 4 : is a schematic diagram of the structure of the optical proximity effect correction device provided by the embodiment of the present application. The optical proximity effect correction device may include an acquisition unit 201, a discrete unit 202, a calculation unit 203, a prediction unit 204 and a correction unit 205.
[0116] An acquisition unit 201 is used to acquire a mask pattern to be corrected;
[0117] The discretization unit 202 is used to discretize the mask boundary of the mask layout to be corrected to obtain a plurality of first boundary segments;
[0118] A calculation unit 203 is configured to calculate the Jacobian matrix of each first boundary segment based on the Hopkins optical transfer matrix;
[0119] The prediction unit 204 is configured to input the Jacobian matrix into the offset prediction model to predict the offset of each first boundary segment;
[0120] The correction unit 201 is configured to adjust the position of the corresponding first boundary segment according to the offset to generate a corrected mask layout.
[0121] The specific implementation of each of the above units can be found in the above-mentioned embodiment of the optical proximity effect correction method, and will not be described in detail here.
[0122] In summary, the optical proximity effect correction device provided in the embodiments of the present application can obtain a mask layout to be corrected via an acquisition unit 201; a discretization unit 202 discretizes the mask boundaries of the mask layout to be corrected to obtain a plurality of first boundary segments; a calculation unit 203 calculates the Jacobian matrix for each first boundary segment based on the Hopkins optical transfer matrix; a prediction unit 204 inputs the Jacobian matrix into an offset prediction model to predict the offset of each first boundary segment; and a correction unit 201 adjusts the position of the corresponding first boundary segment based on the offset to generate a corrected mask layout. By integrating a physical model (the Hopkins optical transfer matrix) with a neural network model (the offset prediction model), this solution overcomes the computational bottleneck of traditional OPC while ensuring optical imaging accuracy and reducing its computational complexity.
[0123] The embodiment of the present application further provides an electronic device, in which the optical proximity effect correction device of the embodiment of the present application can be integrated, such as Figure 5 , which shows a schematic diagram of the structure of the electronic device involved in the embodiment of the present application, specifically:
[0124] The electronic device may include one or more processing core processors 301 and one or more computer readable storage media memories 302 and other components. Those skilled in the art will understand that Figure 5 The electronic device structure shown in the figure does not constitute a limitation of the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently.
[0125] The processor 301 is the control center of the electronic device. It connects the various parts of the entire electronic device using various interfaces and lines. By running or executing the software programs and / or this application stored in the memory 302, and calling the data stored in the memory 302, it performs 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, wherein the application processor mainly handles the operation of storage media, user interface and application programs, etc., and the modem processor mainly handles wireless communication. It is understandable that the above-mentioned modem processor may not be integrated into the processor 301.
[0126] The memory 302 can be used to store software programs and the present application. The processor 301 executes various functional applications and data processing by running the software programs and the present application stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store operating storage media, applications required for at least one function, etc.; the data storage area may store data created based on the use of the electronic device, etc. In addition, the memory 302 may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a 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.
[0127] Although not shown, the electronic device may further include a display unit, an input unit, a power supply, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the electronic device will load 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 will run the application programs stored in the memory 302 to implement various functions as follows:
[0128] Obtaining the mask layout to be corrected;
[0129] Discretizing the mask boundary of the mask layout to be corrected to obtain a plurality of first boundary segments;
[0130] Based on the Hopkins optical transfer matrix, the Jacobian matrix of each first boundary segment is calculated;
[0131] Inputting the Jacobian matrix into the offset prediction model to predict the offset of each first boundary segment;
[0132] The position of the corresponding first boundary segment is adjusted according to the offset to generate a corrected mask layout.
[0133] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.
[0134] To this end, an embodiment of the present application provides a storage medium storing a plurality of instructions that can be loaded by a processor to execute the steps of any of the methods provided in the embodiments of the present application. For example, the instructions can execute the following steps:
[0135] Obtaining the mask layout to be corrected;
[0136] Discretizing the mask boundary of the mask layout to be corrected to obtain a plurality of first boundary segments;
[0137] Based on the Hopkins optical transfer matrix, the Jacobian matrix of each first boundary segment is calculated;
[0138] Inputting the Jacobian matrix into the offset prediction model to predict the offset of each first boundary segment;
[0139] The position of the corresponding first boundary segment is adjusted according to the offset to generate a corrected mask layout.
[0140] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.
[0141] The storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.
[0142] Since the instructions stored in the storage medium can execute the steps in any method provided in the embodiments of the present application, the beneficial effects that can be achieved by any method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.
[0143] The optical proximity effect correction method, device, storage medium and electronic device provided by the present application are respectively introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods 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 technical personnel in this field, based on the idea of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.
Claims
1. A method for correcting an optical proximity effect, characterized in that: include: Obtaining the mask layout to be corrected; Discretizing the mask boundary of the mask layout to be corrected to obtain a plurality of first boundary segments; Calculating the Jacobian matrix of each first boundary segment based on the Hopkins optical transfer matrix; Inputting the Jacobian matrix into an offset prediction model to predict the offset of each first boundary segment; updating a position corresponding to the first boundary segment according to the offset; updating the transmission spectrum of the mask to be corrected based on the updated first boundary segment; Calculating a current exposure error according to the Hopkins optical transfer matrix and the updated transmission spectrum; Based on the current exposure error, updating the position corresponding to the first boundary segment by Newton iteration method until a convergence condition is met; When the convergence condition is reached, a corrected mask layout is generated.
2. The optical proximity effect correction method according to claim 1, wherein: The updating of the position of the first boundary segment corresponding to the current exposure error by using the Newton iteration method until a convergence condition is met includes: When the current exposure error is greater than a threshold, returning to the step of calculating the Jacobian matrix of each first boundary segment based on the Hopkins optical transfer matrix; When the current exposure error is less than or equal to the threshold, it is determined that the convergence condition is met.
3. The optical proximity effect correction method according to claim 1, wherein: Also includes: Construct the Hopkins optical transfer matrix; Build an offset prediction model.
4. The optical proximity effect correction method according to claim 3, wherein: The constructing of the offset prediction model includes: Obtain test mask layout; Discretizing the mask boundary of the test mask layout to obtain a plurality of second boundary segments; Calculating the Jacobian matrix and the Hessian matrix of each second boundary segment based on the Hopkins optical transfer matrix; Calculate the true offset of each second boundary segment based on the Hessian matrix; The actual offset is used as a training label and the Jacobian matrix is used as input data to train the preset Unet network model to generate an offset prediction model.
5. The optical proximity effect correction method according to claim 3, wherein: The constructing of the Hopkins optical transfer matrix includes: Obtain optical parameters of the lithography system; According to the optical parameters, an optical imaging system is established using a Hopkins imaging model; A Hopkins optical transfer matrix is calculated based on the optical imaging system.
6. An optical proximity effect correction device, characterized in that: include: An acquisition unit, used for acquiring the mask pattern to be corrected; A discretization unit is used to discretize the mask boundary of the mask layout to be corrected to obtain a plurality of first boundary segments; a calculation unit, configured to calculate the Jacobian matrix of each first boundary segment based on the Hopkins optical transfer matrix; a prediction unit, configured to input the Jacobian matrix into an offset prediction model to predict an offset of each first boundary segment; a correction unit, configured to update a position corresponding to the first boundary segment according to the offset; and update the transmission spectrum of the mask to be corrected based on the updated first boundary segment; Calculating a current exposure error according to the Hopkins optical transfer matrix and the updated transmission spectrum; Based on the current exposure error, updating the position corresponding to the first boundary segment by Newton iteration method until a convergence condition is met; When the convergence condition is reached, a corrected mask layout is generated.
7. 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 optical proximity effect correction method according to any one of claims 1 to 5.
8. 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 when the processor executes the computer program, the method for correcting the optical proximity effect according to any one of claims 1 to 5 is implemented.