SRAF placement method and device, storage medium and electronic equipment

Abstract

The invention discloses an SRAF placement method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining the graphic features and environment features of a target graph in a through hole layer; generating cross features according to the graphic features and the environment features; constructing a multi-dimensional information matrix according to the graphic features, the environment features and the cross features; inputting the multi-dimensional information matrix into an SRAF parameter prediction model to obtain an SRAF placement parameter; and placing the SRAF according to the SRAF placement parameter. According to the scheme, the precision of SRAF placement can be improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of photolithography technology, and specifically to a SRAF placement method, device, storage medium and electronic device. Background Art

[0002] As semiconductor manufacturing processes continue to develop towards smaller process nodes, the challenges facing lithography technology are increasing. Especially in high-resolution lithography, the imaging accuracy of the target pattern has a crucial impact on the performance of the final device. However, due to the diffraction effect of the optical system and the fluctuation of process parameters, traditional lithography methods often find it difficult to achieve the expected pattern edge accuracy, thus affecting the performance and yield of the chip.

[0003] In order to solve the above problems, sub-resolution assist features (SRAF) technology was introduced into mask design. SRAF improves the interference and diffraction effects in the lithography imaging process by adding auxiliary graphics around the target graphics, and enhances the contrast and line width control of the graphics edge. However, traditional SRAF placement methods usually rely on fixed design rules or experience-based heuristic algorithms, which have the problems of being unable to accurately express the coupling relationship between graphics, local optimality, and poor versatility, making it difficult to achieve high-precision auxiliary graphics placement. Summary of the invention

[0004] The embodiments of the present application provide a SRAF placement method, device, storage medium, and electronic device, which can improve the accuracy of SRAF placement.

[0005] In a first aspect, an embodiment of the present application provides a SRAF placement method, including: Obtaining graphic features and environmental features of the target graphic in the through-hole layer; generating a cross feature according to the graphic feature and the environmental feature; Constructing a multidimensional information matrix according to the graphic features, the environmental features and the cross features; Inputting the multidimensional information matrix into a SRAF parameter prediction model to obtain SRAF placement parameters; The SRAF is placed according to the SRAF placement parameters.

[0006] In the SRAF placement method provided in the embodiment of the present application, the graphic features include critical dimensions, first graphic spacing, second graphic spacing, and edge offset, and the environmental features include adjacent edge spacing and local density.

[0007] In the SRAF placement method provided in the embodiment of the present application, generating a cross feature according to the graphic feature and the environmental feature includes: The graphic feature and the environmental feature are combined by using a mathematical operation method to generate a cross feature.

[0008] In the SRAF placement method provided in the embodiment of the present application, the use of a mathematical operation method to combine the graphic feature and the environmental feature to generate a cross feature includes: multiplying the critical dimension by the edge offset to obtain a first intersection feature; Dividing the first pattern spacing by the adjacent edge spacing to obtain a second intersection feature; The second pattern spacing is multiplied by the local density to obtain a third intersection feature.

[0009] In the SRAF placement method provided in the embodiment of the present application, the multi-dimensional information matrix is ​​constructed according to the graphic features, the environmental features and the cross features, including: Converting the graphic feature, the environment feature and the cross feature into column vectors respectively; Performing standardization processing on each of the column vectors; The standardized column vectors are serially connected into a multi-dimensional information matrix in a preset order.

[0010] In the SRAF placement method provided in the embodiment of the present application, the SRAF parameter prediction model is a multivariate linear regression model.

[0011] The SRAF placement method provided in the embodiment of the present application also includes: Construct SRAF parameter prediction model.

[0012] In a second aspect, an embodiment of the present application provides a SRAF placement device, including: A feature acquisition unit, used to acquire graphic features and environmental features of a target graphic in a through-hole layer; A feature generating unit, used for generating a cross feature according to the graphic feature and the environmental feature; A matrix construction unit, used for constructing a multi-dimensional information matrix according to the graphic features, the environmental features and the cross features; A parameter prediction unit, used for inputting the multi-dimensional information matrix into a SRAF parameter prediction model to obtain SRAF placement parameters; The SRAF placement unit is used to place the SRAF according to the SRAF placement parameters.

[0013] 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 SRAF placement methods.

[0014] 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 SRAF placement methods when executing the computer program.

[0015] In summary, the SRAF placement method provided in the embodiment of the present application includes obtaining the graphic features and environmental features of the target graphic in the through-hole layer; generating cross features according to the graphic features and the environmental features; constructing a multidimensional information matrix according to the graphic features, the environmental features and the cross features; inputting the multidimensional information matrix into the SRAF parameter prediction model to obtain SRAF placement parameters; and placing the SRAF according to the SRAF placement parameters. This solution constructs a multidimensional information matrix by fusing graphic features, environmental features and cross features, and introduces a multivariate linear regression model for SRAF parameter prediction, thereby solving the problem that the coupling relationship between graphics cannot be accurately expressed in the traditional SRAF placement process, making the SRAF placement more in line with actual imaging conditions and improving the accuracy of SRAF placement. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. 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.

[0017] Figure 1 It is a flowchart of the SRAF placement method provided in an embodiment of the present application.

[0018] Figure 2 It is a structural schematic diagram of the SRAF placement device provided in an embodiment of the present application.

[0019] Figure 3 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0021] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes 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 "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including 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.

[0022] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0023] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0024] In the description of the present application, it should be noted that the terms "upper", "lower", "left", "right", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are 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 cannot be understood as limiting the present application. In addition, terms such as "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0025] Traditional SRAF placement methods usually rely on fixed design rules or experience-based heuristic algorithms, which have the problems of being unable to accurately express the coupling relationship between graphics, local optimality, and poor versatility, making it difficult to achieve high-precision, automated auxiliary graphic placement.

[0026] Based on this, an embodiment of the present application provides a SRAF placement method, device, storage medium and electronic device. Specifically, the SRAF placement device can be integrated in an electronic device, and the electronic device can be a server or a terminal or other device; 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.

[0027] 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 is not intended to limit the priority order of the embodiments.

[0028] See also Figure 1 , Figure 1 : is a flow chart of the SRAF placement method provided in an embodiment of the present application. The specific flow of the SRAF placement method may be as follows: 101. Obtain graphic features and environmental features of the target graphic in the through-hole layer.

[0029] First, you can select a through-hole layer (such as a VIA layer) as the target layer from the integrated circuit layout data, extract the basic properties of the target graphic through graphic recognition and data analysis tools, and obtain its graphic features. The graphic features include but are not limited to the critical dimension (CD), first graphic pitch, second graphic pitch, and edge offset (Offset) of the target graphic. The first graphic pitch refers to the graphic pitch Pitchx in the x-direction, and the second graphic pitch refers to the graphic pitch Pitchy in the y-direction.

[0030] Then, based on the spatial retrieval and sliding window method, a certain range of neighborhood windows (e.g. 1μm × 1μm) is set around the target graphics, and the layout of other graphics in the window is counted to obtain environmental features. The environmental features include the spacing between adjacent edges and local density, which can reflect the structural environment of the target graphics in the local area.

[0031] In some embodiments, after obtaining the graphic features and the environmental features, the graphic features and the environmental features may be standardized. Since the dimensions of each feature are different, it is necessary to standardize each feature so that all features are at the same scale to avoid a variable dominating the model training due to a large value range.

[0032] 102. Generate cross features based on graphic features and environmental features.

[0033] The graphic features and the environmental features may be combined using mathematical operation methods to generate cross features, so as to explore the coupling relationship between the two types of features.

[0034] Specifically, the critical dimension and the edge offset may be multiplied to obtain a first intersection feature, which may reflect the combined effect of the critical dimension and the edge offset.

[0035] The first pattern spacing is divided by the adjacent edge spacing to obtain a second intersection feature, which can reflect the relative relationship between the target pattern and the surrounding patterns.

[0036] The second pattern spacing is multiplied by the local density to obtain a third intersection feature, which can be used to capture the influence of the local environment on the lithography effect.

[0037] 103. Construct a multidimensional information matrix based on graphic features, environmental features and cross-features.

[0038] Graphic features, environmental features, and cross-features can be constructed together into a set of multidimensional information matrices. Each row in the multidimensional information matrix represents a comprehensive feature vector of a target graphic, and each column corresponds to a feature dimension. To enhance the consistency and processability of the data, all feature data are standardized to make them suitable for the input requirements of the machine learning model.

[0039] In some embodiments, the graphic features, environmental features and cross features can be converted into column vectors respectively; each column vector can be standardized; and the standardized column vectors can be connected in series into a multi-dimensional information matrix in a preset order.

[0040] The preset sequence can be set according to actual conditions and is not limited in this embodiment.

[0041] 104. Input the multi-dimensional information matrix into the SRAF parameter prediction model to obtain the SRAF placement parameters.

[0042] The SRAF parameter prediction model is a multivariate linear regression model. Before step 101, the SRAF parameter prediction model may also be constructed.

[0043] In some embodiments, representative target graphic samples and their optimal SRAF parameter configurations can be first collected, and their graphic features, environmental features, and cross features can be extracted to form a multi-dimensional feature vector of the training sample. Then, the SRAF parameters are used as model output labels to construct a supervised learning data set. The linear mapping relationship between the input features and the target output is modeled by the least squares method to obtain the SRAF parameter prediction model.

[0044] Specifically, the following mapping relationship can be established using a multivariate linear regression model: .

[0045] Here, Y is a vector that refers to all the sraf parameters that need to be predicted (e.g. [sraf size, sraf offset, sraf placement]). to Refers to all the aforementioned basic features, such as graphic features, environmental features, and cross-features. is the intercept, to is the regression coefficient of each feature, is the error term.

[0046] Then, using the least squares formula: The regression coefficient is solved to minimize the mean square error between the predicted value and the actual value, and the SRAF parameter prediction model is obtained.

[0047] The training and validation process of the SRAF parameter prediction model can be as follows: (1) Dataset division: The collected dataset is divided into training set, validation set, and test set to ensure that the model can generalize well on new data.

[0048] (2) Model training: Use the training set to fit the model and calculate the regression coefficients using the least squares method. Use cross-validation (such as K-fold cross-validation) for optimization to reduce the risk of overfitting.

[0049] (3) Model evaluation: Mean Squared Error (MSE): Measures the average squared difference between the model’s predictions and the actual values.

[0050] Coefficient of determination (R²): evaluates the model's ability to explain the variation in each sraf parameter.

[0051] Analyze the residual distribution and check whether it conforms to the normal distribution to ensure that the model assumptions are basically established.

[0052] 105. Place the SRAF according to the SRAF placement parameters.

[0053] In some embodiments, a layout tool interface may be called to place an SRAF at a compliant position outside the target graphic according to SRAF placement parameters to complete the auxiliary graphic layout.

[0054] For example, for each target graphic, its contour boundary information can be extracted; then, the position coordinates of the SRAF relative to the edge of the target graphic are determined based on the SRAF offset output by the SRAF parameter prediction model; then, the corresponding auxiliary graphics are generated based on the output SRAF size and position; finally, the layout legality of the placed SRAF is checked, and the legal SRAF is written into the final layout for subsequent lithography simulation and layout verification.

[0055] In summary, the SRAF placement method provided in the embodiment of the present application includes obtaining the graphic features and environmental features of the target graphic in the through-hole layer; generating cross features according to the graphic features and environmental features; constructing a multidimensional information matrix according to the graphic features, environmental features and cross features; inputting the multidimensional information matrix into the SRAF parameter prediction model to obtain the SRAF placement parameters; and placing the SRAF according to the SRAF placement parameters. This solution constructs a multidimensional information matrix by fusing graphic features, environmental features and cross features, and introduces a multivariate linear regression model for SRAF parameter prediction, thereby solving the problem that the coupling relationship between graphics cannot be accurately expressed in the traditional SRAF placement process, making the SRAF placement more in line with the actual imaging conditions and improving the accuracy of SRAF placement. In addition, by extracting the graphic features of the target graphic and its surrounding environment features, generating cross features, and constructing a multidimensional information matrix, the changing trend of the target graphic layout environment can be fully characterized. Compared with the traditional rule-based SRAF placement method, this scheme can dynamically predict more appropriate SRAF placement parameters according to the context of the target graphic, thereby reducing the impact of lithography process changes on critical dimensions while ensuring resolution improvement, achieving a narrowing of the process window and improving graphic imaging consistency.

[0056] In order to better implement the SRAF placement method provided in the embodiment of the present application, the embodiment of the present application also provides a SRAF placement device, wherein the meanings of the terms are the same as those in the above SRAF placement method, and the specific implementation details can refer to the description in the method embodiment.

[0057] See also Figure 2 , Figure 2 201 is a schematic diagram of the structure of the SRAF placement device provided in the embodiment of the present application. The SRAF placement device may include a feature acquisition unit 201, a feature generation unit 202, a matrix construction unit 203, a parameter prediction unit 204 and a SRAF placement unit 205. A feature acquisition unit 201 is used to acquire the graphic features and environmental features of the target graphic in the through-hole layer; A feature generating unit 202, configured to generate a cross feature according to the graphic feature and the environment feature; A matrix construction unit 203, used to construct a multi-dimensional information matrix according to the graphic features, environmental features and cross features; A parameter prediction unit 204, used to input the multi-dimensional information matrix into the SRAF parameter prediction model to obtain SRAF placement parameters; The SRAF placement unit 205 is used to place the SRAF according to the SRAF placement parameters.

[0058] The specific implementation of each of the above units can refer to the above-mentioned embodiment of the SRAF placement method, which will not be described one by one here.

[0059] In summary, the SRAF placement device provided in the embodiment of the present application can obtain the graphic features and environmental features of the target graphic in the through-hole layer through the feature acquisition unit 201; the feature generation unit 202 generates cross features according to the graphic features and environmental features; the matrix construction unit 203 constructs a multidimensional information matrix according to the graphic features, environmental features and cross features; the parameter prediction unit 204 inputs the multidimensional information matrix into the SRAF parameter prediction model to obtain the SRAF placement parameters; the SRAF placement unit 205 places the SRAF according to the SRAF placement parameters. This solution constructs a multidimensional information matrix by fusing graphic features, environmental features and cross features, and introduces a multivariate linear regression model for SRAF parameter prediction, which solves the problem that the coupling relationship between graphics cannot be accurately expressed in the traditional SRAF placement process, makes the SRAF placement more in line with the actual imaging conditions, and improves the accuracy of SRAF placement.

[0060] The present application also provides an electronic device, in which the SRAF placement device of the present application can be integrated, such as Figure 3 As shown, it shows a schematic diagram of the structure of the electronic device involved in the embodiment of the present application, specifically: The electronic device may include one or more processors 301 of processing cores and one or more computer-readable storage media memories 302 and other components. Those skilled in the art will appreciate that Figure 3 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. The processor 301 is the control center of the electronic device, which uses various interfaces and lines to connect various parts of the entire electronic device, and executes various functions of the electronic device and processes data by running or executing the software program and / or the present application stored in the memory 302, and calling the data stored in the memory 302, so as to monitor 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 processes the operation storage medium, user interface and application program, etc., and the modem processor mainly processes wireless communication. It is understandable that the above-mentioned modem processor may not be integrated into the processor 301.

[0061] 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 an operating storage medium, an application required for at least one function, etc.; the data storage area may store data created according to 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 devices. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0062] 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 herein. 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, thereby realizing various functions, as follows: Obtaining graphic features and environmental features of the target graphic in the through-hole layer; Generate cross features according to graphic features and environmental features; Construct a multi-dimensional information matrix based on graphic features, environmental features and cross-features; The multi-dimensional information matrix is ​​input into the SRAF parameter prediction model to obtain the SRAF placement parameters; The SRAF is placed according to the SRAF placement parameters.

[0063] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed 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.

[0064] To this end, an embodiment of the present application provides a storage medium in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the steps in any method provided in the embodiment of the present application. For example, the instructions can execute the following steps: Obtaining graphic features and environmental features of the target graphic in the through-hole layer; Generate cross features according to graphic features and environmental features; Construct a multi-dimensional information matrix based on graphic features, environmental features and cross-features; The multi-dimensional information matrix is ​​input into the SRAF parameter prediction model to obtain the SRAF placement parameters; The SRAF is placed according to the SRAF placement parameters.

[0065] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0066] The storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0067] 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.

[0068] The SRAF placement 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, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A SRAF placement method, characterized in that: include: Obtaining graphic features and environmental features of the target graphic in the through-hole layer; generating a cross feature according to the graphic feature and the environmental feature; Constructing a multidimensional information matrix according to the graphic features, the environmental features and the cross features; Inputting the multidimensional information matrix into a SRAF parameter prediction model to obtain SRAF placement parameters; The SRAF is placed according to the SRAF placement parameters.

2. The SRAF placement method according to claim 1, characterized in that: The graphic features include critical dimensions, first graphic spacing, second graphic spacing, and edge offset, and the environmental features include adjacent edge spacing and local density.

3. The SRAF placement method according to claim 2, characterized in that: The generating of the intersection feature according to the graphic feature and the environment feature comprises: The graphic feature and the environmental feature are combined by using a mathematical operation method to generate a cross feature.

4. The SRAF placement method according to claim 3, characterized in that: The step of combining the graphic feature and the environmental feature by a mathematical operation method to generate a cross feature includes: multiplying the critical dimension by the edge offset to obtain a first intersection feature; Dividing the first pattern spacing by the adjacent edge spacing to obtain a second intersection feature; The second pattern spacing is multiplied by the local density to obtain a third intersection feature.

5. The SRAF placement method according to claim 2, wherein: The constructing of a multidimensional information matrix according to the graphic features, the environmental features and the cross features comprises: Converting the graphic feature, the environment feature and the cross feature into column vectors respectively; Performing standardization processing on each of the column vectors; The standardized column vectors are serially connected into a multi-dimensional information matrix in a preset order.

6. The SRAF placement method according to claim 1, wherein: The SRAF parameter prediction model is a multivariate linear regression model.

7. The SRAF placement method according to claim 6, characterized in that: Also includes: Construct SRAF parameter prediction model.

8. A SRAF placement device, characterized in that: include: A feature acquisition unit, used to acquire graphic features and environmental features of a target graphic in a through-hole layer; A feature generating unit, used for generating a cross feature according to the graphic feature and the environmental feature; A matrix construction unit, used for constructing a multi-dimensional information matrix according to the graphic features, the environmental features and the cross features; A parameter prediction unit, used for inputting the multi-dimensional information matrix into a SRAF parameter prediction model to obtain SRAF placement parameters; The SRAF placement unit is used to place the SRAF according to the SRAF placement parameters.

9. 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 SRAF placement method according to any one of claims 1 to 7.

10. 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 SRAF placement method according to any one of claims 1 to 7 is implemented.

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