Methods, apparatus, equipment and storage media for processing simulated photolithography patterns
By performing gradient calculations and bitriangular interpolation on the simulated lithography model, the resolution of the simulated lithography pattern is improved, solving the problems of low resolution and complex calculations in the existing technology, and providing high-precision lithography process guidance.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HUAXINCHENG (HANGZHOU) TECH CO LTD
- Filing Date
- 2026-05-18
- Publication Date
- 2026-06-26
AI Technical Summary
Existing simulated lithography models have low resolution and complex calculations when obtaining simulated lithography patterns, making it difficult to achieve high-precision lithography process guidance.
By using a simulated lithography model to obtain the light intensity data of the initial lithography pattern, gradient calculations are performed to determine the dynamic parameters of the bitriangular interpolation operation. Combined with the bitriangular interpolation operation function, the light intensity data is interpolated to improve the resolution of the simulated lithography pattern.
It enables the acquisition of accurate and reliable high-resolution simulated lithography patterns while avoiding excessive computation, supporting the research and guidance of lithography processes.
Smart Images
Figure CN122284231A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of semiconductor manufacturing technology, and in particular to a method, apparatus, device, and computer-readable storage medium for processing simulated photolithography patterns. Background Technology
[0002] Photolithography is a crucial process in semiconductor manufacturing, and computational lithography (CLI) is a technique that uses computers to simulate and model the photolithography process. It plays a vital role in theoretically exploring the process and guiding the optimization of process parameters. Simulated photolithography models are important simulation tools, but due to limitations in computing power, the resolution of simulated photolithography patterns obtained from these models is not high. Currently, higher-resolution simulated photolithography patterns can be obtained by using linear interpolation based on the light intensity data of each pixel in the simulated pattern, but this method still results in low accuracy. Alternatively, a high-resolution simulated photolithography pattern can be obtained by fitting a function to the light intensity data of each pixel obtained through simulation. While this method yields higher accuracy, it often involves more complex calculations and a larger computational load.
[0003] Therefore, how to easily and reliably obtain high-precision simulated lithography patterns is of great significance for guiding and exploring semiconductor lithography processes. Summary of the Invention
[0004] The purpose of this invention is to provide a method, apparatus, device, and computer-readable storage medium for processing simulated photolithography patterns. This method can obtain accurate and reliable high-resolution simulated photolithography images without excessive computation, providing a reliable theoretical basis for the research of semiconductor photolithography processes and facilitating the guidance of photolithography processes.
[0005] To solve the above technical problems, the present invention provides a method for processing simulated photolithography patterns, comprising:
[0006] The light intensity data of each pixel in the initial simulated lithography pattern is obtained using a simulated lithography model.
[0007] Gradient calculation is performed on the light intensity data corresponding to the pixels in the neighborhood of the interpolation point to obtain the light intensity gradient;
[0008] The dynamic parameters for the bitriangular interpolation operation are determined based on the light intensity gradient.
[0009] The interpolated light intensity data of the point to be interpolated is determined based on the dynamic parameters and the bi-triple interpolation operation function.
[0010] Based on the interpolated light intensity data of each interpolation point and the initial simulated lithography pattern, the simulated lithography pattern after interpolation is obtained.
[0011] In one optional embodiment of this application, gradient calculation is performed on the light intensity data corresponding to pixels within the neighborhood of the interpolation point to obtain the light intensity gradient, including:
[0012] The edge detection operator is used to calculate the light intensity data of each pixel within a set neighborhood of the interpolation point to obtain the horizontal and vertical gradients.
[0013] The dynamic parameters for the bitriangular interpolation operation are determined based on the light intensity gradient, including:
[0014] Tensor feature operations are performed on the horizontal gradient and the vertical gradient to obtain the first tensor feature value of each pixel in the first neighborhood range and the second tensor feature value of each pixel in the second neighborhood range of the point to be interpolated; wherein the first neighborhood range is smaller than the second neighborhood range.
[0015] Based on the first tensor feature value and the second tensor feature value, a coherence calculation is performed, and the dynamic parameters are determined based on the coherence data obtained from the calculation.
[0016] In an optional embodiment of this application, tensor feature operations are performed on the horizontal gradient and the vertical gradient to obtain the first tensor feature value of each pixel within a first neighborhood of the point to be interpolated and the second tensor feature value of each pixel within a second neighborhood, including:
[0017] Within the first neighborhood of the point to be interpolated, the horizontal gradient and the vertical gradient of each pixel are calculated according to the first tensor component operation formula. Perform tensor feature operations to obtain the first tensor feature value; wherein... The eigenvalue of the first tensor; The first neighboring region of the point to be interpolated is respectively the first... The horizontal and vertical gradients of each pixel; The first Gaussian kernel weight; The total number of pixels within the first neighborhood of the point to be interpolated; the first neighborhood is a 3×3 neighborhood.
[0018] Within the second neighborhood of the point to be interpolated, the horizontal and vertical gradients of each pixel are calculated according to the second tensor component operation formula. Perform tensor feature operations to obtain the first tensor feature value; wherein... The eigenvalues of the second tensor; The first neighboring region of the point to be interpolated is respectively the first... The horizontal and vertical gradients of each pixel; The second Gaussian kernel weight; The number of pixels in the second neighborhood of the point to be interpolated is the total number of pixels in the second neighborhood; the second neighborhood is a 7×7 neighborhood.
[0019] In an optional embodiment of this application, coherence calculation is performed based on the first tensor feature value and the second tensor feature value, and the dynamic parameters are determined based on the coherence data obtained from the calculation, including:
[0020] Based on the first tensor eigenvalue, the second tensor eigenvalue, and the coherence formula The coherence data is obtained through calculation; among them, For the coherence data, The first tensor eigenvalue, The eigenvalues of the second tensor;
[0021] An adaptive function based on the coherence data and dynamic parameters Determine the dynamic parameters; wherein, These are dynamic parameters; The noise suppression threshold. This is the saturation threshold.
[0022] In an optional embodiment of this application, determining the interpolated light intensity data of the point to be interpolated based on the dynamic parameters and the bitriangular interpolation function includes:
[0023] Based on the dynamic parameters and interpolation basis functions and bitrigonometric interpolation functions Determine the interpolated light intensity data of the point to be interpolated; wherein, The interpolated light intensity data; The interpolation point The local coordinates within the neighborhood are The light intensity data of each pixel; These are dynamic parameters; For variable A changing function; These represent the horizontal and vertical distances between the interpolation point and the nearest pixel, respectively. The spacing between two adjacent pixels in the horizontal direction and the spacing between two adjacent pixels in the vertical direction.
[0024] A processing apparatus for simulating photolithographic patterns, comprising:
[0025] The pattern simulation module is used to obtain the light intensity data of each pixel in the initial simulated lithography pattern using a simulated lithography model.
[0026] The gradient calculation module is used to perform gradient calculations on the light intensity data corresponding to the pixels in the neighborhood of the interpolation point to obtain the light intensity gradient.
[0027] The parameter calculation module is used to determine the dynamic parameters of the bitriangular interpolation operation based on the light intensity gradient.
[0028] An interpolation module is used to determine the interpolated light intensity data of the point to be interpolated based on the dynamic parameters and the bi-triple interpolation function.
[0029] The pattern acquisition module is used to obtain the interpolated simulated lithography pattern based on the interpolated light intensity data of each interpolation point and the initial simulated lithography pattern.
[0030] In one optional embodiment of this application, the gradient calculation module is specifically used to calculate the light intensity data of each pixel within a set neighborhood of the interpolation point using an edge detection operator to obtain the horizontal gradient and the vertical gradient.
[0031] The parameter calculation module is specifically used to perform tensor feature calculation on the horizontal gradient and the vertical gradient to obtain the first tensor feature value of each pixel in the first neighborhood range and the second tensor feature value of each pixel in the second neighborhood range of the interpolation point; wherein, the first neighborhood range is smaller than the second neighborhood range; based on the first tensor feature value and the second tensor feature value, coherence calculation is performed, and based on the coherence data obtained from the calculation, the dynamic parameters are determined.
[0032] In an optional embodiment of this application, the interpolation module is specifically used to perform interpolation operations based on the dynamic parameters and the interpolation basis function. and bitrigonometric interpolation functions Determine the interpolated light intensity data of the point to be interpolated; wherein, The interpolated light intensity data; The interpolation point The local coordinates within the neighborhood are The light intensity data of each pixel; These are dynamic parameters; For variable A changing function; These represent the horizontal and vertical distances between the interpolation point and the nearest pixel, respectively. The spacing between two adjacent pixels in the horizontal direction and the spacing between two adjacent pixels in the vertical direction.
[0033] A processing device for simulating photolithographic patterns, comprising:
[0034] Memory, used to store computer programs;
[0035] A processor for executing the computer program to implement the steps of the method for processing simulated photolithography patterns as described in any of the preceding claims.
[0036] A computer-readable storage medium storing a computer program that is executed to implement the steps of the method for processing simulated photolithography patterns as described in any of the preceding claims.
[0037] The present invention provides a method, apparatus, device, and computer-readable storage medium for processing simulated photolithography patterns. The method for processing simulated photolithography patterns may include obtaining light intensity data of each pixel in an initial simulated photolithography pattern using a simulated photolithography model; performing gradient calculations on the light intensity data corresponding to pixels within the neighborhood of the interpolation point to obtain a light intensity gradient; determining dynamic parameters for bi-triplex interpolation based on the light intensity gradient; determining the interpolated light intensity data of the interpolation point to be interpolated based on the dynamic parameters and the bi-triplex interpolation function; and obtaining the interpolated simulated photolithography pattern based on the interpolated light intensity data of each interpolation point and the initial simulated photolithography pattern.
[0038] In this application, after obtaining the initial simulated lithography pattern using a simulated lithography model, a bitriangular interpolation function is used to interpolate more points between pixels. Compared to conventional linear interpolation, the bitriangular interpolation function used in this application can improve the accuracy and reliability of the interpolation results to a certain extent without excessively increasing the computational load. Simultaneously, the parameter coefficients in the bitriangular interpolation function are determined based on the light intensity gradient of each pixel within the neighborhood of the point to be interpolated. This means that the bitriangular interpolation function fully considers the light intensity gradient of its neighboring pixels when determining the interpolated light intensity data of the point to be interpolated, allowing the interpolation coefficients to be adjusted according to the light intensity gradient, further ensuring the accuracy and reliability of the final interpolated light intensity data. Therefore, this application can achieve accurate and reliable interpolation of the simulated lithography image simulated by the simulated lithography model, improving the resolution of the simulated lithography pattern while avoiding overly complex calculations. Attached Figure Description
[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0040] Figure 1 A flowchart illustrating the method for processing simulated photolithographic patterns provided in this application embodiment;
[0041] Figure 2 This is a schematic diagram illustrating the relative positions between the point to be interpolated and the pixels within its neighborhood, provided in an embodiment of this application.
[0042] Figure 3 This is a structural block diagram of the apparatus for processing simulated photolithographic patterns provided in an embodiment of this application. Detailed Implementation
[0043] The core of this invention is to provide a method, apparatus, device, and computer-readable storage medium for processing simulated photolithography patterns, which can easily and reliably obtain high-resolution simulated photolithography patterns, providing support for the research and process guidance of photolithography technology.
[0044] To enable those skilled in the art to better understand the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0045] like Figure 1 As shown, Figure 1 This is a flowchart illustrating the method for processing simulated photolithographic patterns provided in an embodiment of this application.
[0046] In one specific embodiment of this application, the method for processing the simulated photolithography pattern may include:
[0047] S1: Light intensity data of each pixel in the initial simulated lithography pattern obtained using the simulated lithography model.
[0048] The simulated lithography model in this embodiment is a tool model used to simulate the lithography process. The simulated lithography model can automatically simulate the obtained simulated lithography pattern based on the set mask parameters and other lithography parameters. The light intensity data corresponding to each pixel in the simulated lithography pattern is a parameter that characterizes the degree of etching removal on the etched substrate. The larger the light intensity data, the deeper the etching removal at that location point and the greater the thickness of the removed material.
[0049] However, limited by computing power, this simulated lithography model cannot directly simulate and calculate the light intensity data corresponding to every point on the substrate. Instead, it divides the substrate into several grid cells of a certain size, and determines the light intensity data corresponding to each grid cell through simulation. That is, the light intensity data of all points in the area covered by the same grid cell is the same, thus obtaining the simulated lithography pattern. Obviously, the resolution of such a simulated lithography pattern is relatively low. Therefore, each grid cell can be regarded as a pixel, and the real coordinates of the pixel on the simulated lithography pattern are the center point of its grid cell. The light intensity data corresponding to the pixel is the light intensity data of the grid cell. On this basis, more points are inserted between adjacent pixels and their corresponding light intensity data is determined, thereby increasing the resolution of the simulated lithography pattern.
[0050] S2: Perform gradient calculation on the light intensity data corresponding to the pixels in the neighborhood of the interpolation point to obtain the light intensity gradient.
[0051] In practical applications, gradient calculations can be performed on all pixels within the interpolation region to obtain the light intensity gradients of all pixels in the interpolation region. However, for the interpolation of a single point, only the light intensity data corresponding to its neighboring pixels needs to be obtained, i.e., the light intensity gradients of pixels within the range adjacent to the point to be interpolated. The size of the neighboring region of the point to be interpolated depends on the subsequent interpolation method. For example, it can be a 3×3 neighborhood (i.e., including three pixels in both the horizontal and vertical directions), a 4×4 neighborhood, or a larger neighborhood centered on the point to be interpolated, where pixels are used together to determine the interpolated light intensity data. This application does not specifically limit this.
[0052] Optionally, in this embodiment, edge detection operators such as Sobel or Prewitt can be used to obtain the light intensity gradient corresponding to each pixel. The light intensity gradient of each pixel includes a horizontal gradient along the horizontal direction and a vertical gradient along the vertical direction.
[0053] S3: Determine the dynamic parameters for bitriangular interpolation based on the light intensity gradient.
[0054] It should be noted that the basic form of the bi-triangular interpolation function used in determining the interpolated light intensity data of the interpolation point is as follows:
[0055] ;in, For interpolated light intensity data; Points to be interpolated The local coordinates within the neighborhood are The light intensity data of each pixel.
[0056] Based on this, These are the weighting coefficients, and these weighting coefficients satisfy the interpolation basis function: ;in The variables are the interpolation basis functions; These are the dynamic parameters determined based on the light intensity gradient. Therefore, once the dynamic parameters are determined, the weighting coefficients in the bi-triplex interpolation function corresponding to the interpolation point are determined based on the relative positional relationship between the interpolation point and each pixel, as well as these dynamic parameters. This allows for the calculation of interpolated light intensity data.
[0057] The process of determining the dynamic parameters in this embodiment may further include:
[0058] S31: Perform tensor feature operations on the horizontal and vertical gradients to obtain the first tensor feature value of each pixel within the first neighborhood of the point to be interpolated and the second tensor feature value of each pixel within the second neighborhood; wherein the first neighborhood is smaller than the second neighborhood.
[0059] S32: Perform coherence calculation based on the first tensor eigenvalue and the second tensor eigenvalue, and determine the dynamic parameters based on the coherence data obtained from the calculation.
[0060] In this embodiment, a first tensor feature value is obtained by performing small-scale tensor analysis on the light intensity gradient of each pixel within a small neighborhood of the interpolation point, and a second tensor feature value is obtained by performing large-scale tensor analysis on the light intensity gradient of each pixel within a large neighborhood. The magnitudes of the first and second tensor feature values can jointly characterize the image sharpness characteristics of the region where the interpolation point is located. Therefore, dynamic parameters are determined based on the first and second tensor feature values, thereby realizing bitriangular interpolation operation to obtain interpolated light intensity data, which can improve the accuracy and reliability of the interpolated light intensity data.
[0061] Further, optionally, the process of determining the first tensor eigenvalue and the second tensor eigenvalue may include:
[0062] S311: Within the first neighborhood of the interpolation point, calculate the horizontal and vertical gradients of each pixel according to the first tensor component operation formula. Perform tensor feature operations to obtain the first tensor feature value; where, The first tensor eigenvalue; The first neighborhood of the point to be interpolated is the first... The horizontal and vertical gradients of each pixel; The first Gaussian kernel weight; This represents the total number of pixels within the first neighborhood of the point to be interpolated; the first neighborhood is a 3×3 neighborhood.
[0063] S312 calculates the horizontal and vertical gradients of each pixel within the second neighborhood of the interpolation point according to the second tensor component operation formula. Perform tensor feature operations to obtain the first tensor feature value; where, The eigenvalues of the second tensor; The first neighborhood of the point to be interpolated is the first... The horizontal and vertical gradients of each pixel; The second Gaussian kernel weight; This represents the total number of pixels within the second neighborhood of the point to be interpolated; the second neighborhood is a 7×7 neighborhood.
[0064] Based on the above discussion, in this embodiment, the light intensity gradient of each pixel in the 3×3 small neighborhood of the interpolation point is Gaussian smoothed to obtain three tensor components. This allows us to capture fine lines and sharp edges; by performing eigenvalue analysis based on the three tensor components mentioned above, we can obtain the first tensor eigenvalue.
[0065] Similarly, by performing Gaussian smoothing on the light intensity gradient of each pixel within a large 7×7 neighborhood of the interpolation point, three tensor components can also be obtained. To extract a large contour with greater noise resistance; the second tensor feature value can be obtained by performing feature parsing based on the three tensor components.
[0066] Based on the first tensor eigenvalue and the second tensor eigenvalue mentioned above, it can be determined that:
[0067] when When the light intensity data changes drastically, the interpolation point is located at the edge of a straight line.
[0068] when When the interpolation point is located in a textured region with a complex light intensity data structure, the interpolation point is then in that region.
[0069] when When the interpolation point is in the smooth region of the light intensity data, then the point to be interpolated is located in the smooth region of the light intensity data.
[0070] Based on this, the process of determining dynamic parameters based on the first tensor eigenvalues and the second tensor eigenvalues may include:
[0071] S321: Based on the first tensor eigenvalue, the second tensor eigenvalue, and the coherence formula The coherence data is obtained through calculation; among them, For coherence data, The first tensor eigenvalue, The eigenvalues of the second tensor;
[0072] S322: Adaptive function based on coherence data and dynamic parameters Determine the dynamic parameters; among which, These are dynamic parameters; The noise suppression threshold. This is the saturation threshold.
[0073] Coherence data in this embodiment It characterizes the degree of consistency at the edges of coherence measurements; when When the value approaches 1, it indicates that the direction is a very clear straight line, and interpolation should be performed strictly along this direction. When the value is small, it indicates that the directions are chaotic, and the interpolation should be directed towards standard bicubic regression.
[0074] In addition, to avoid At that time, the coherence formula When the denominator approaches 0, a very small positive constant can be added to the denominator; that is, the coherence formula can be adjusted to... ;in That is, a positive number.
[0075] Based on this, for (Lower Threshold) is the noise suppression threshold; when the coherence data When the value is below this, the dynamic parameter is set to -0.5, which is also the usual value for this parameter in the conventional bitriplex interpolation algorithm, that is, edge enhancement is not triggered to avoid amplifying background noise.
[0076] for (Higher Threshold) is the saturation threshold; when the coherence data When the value is higher than this, the dynamic parameter is set to -1, which means that the interpolation point is interpolated according to the full load enhancement mode, and the gain is limited to prevent excessive black and white edges from being generated.
[0077] also and These are all constants that can be set based on practical experience, for example It can be 0.1. A value of 0.5 is acceptable; for scenarios where the overall simulated light intensity data distribution is not obvious and the goal is to improve sharpness, a smaller value can be used appropriately. and For simulations where the overall light intensity data distribution is relatively good, a larger value can be appropriately used. and .
[0078] S4: Determine the interpolated light intensity data of the point to be interpolated based on the dynamic parameters and the bi-triple interpolation operation function.
[0079] S5: Based on the interpolated light intensity data of each interpolation point and the initial simulated lithography pattern, obtain the simulated lithography pattern after interpolation.
[0080] After determining the dynamic parameters based on the above discussion, we then combine the expression of the bitriangular interpolation function. It can be determined that the bitriangular interpolation function is simply a weighted summation process;
[0081] in, Points to be interpolated The local coordinates within the neighborhood are The light intensity data of each pixel; such as Figure 2 As shown, Figure 2 Each dashed box in the diagram represents a grid cell. Figure 2 The image shows a region formed by a 3×3 grid cell, that is... Figure 2 China and Israel Taking a neighborhood range of 3×3 centered on the point to be interpolated as an example, then in The local coordinates of each pixel within the neighborhood are determined by its position within that neighborhood, specifically its horizontal and vertical rank. For example, the coordinates of the three horizontal pixels in the first row would be (1,1), (1,2), and (1,3). The local coordinates of each pixel can be determined in a similar manner. Furthermore, this... It can be regarded as separately using The weight function for the variables; Specifically, this refers to the interpolation basis function. ;like Figure 2 As shown These represent the horizontal and vertical spacing between the interpolation point and the nearest pixel, respectively. The spacing between two adjacent pixels in the horizontal direction and the spacing between two adjacent pixels in the vertical direction.
[0082] Based on this, after determining the dynamic parameters, by further combining the relative distances between the interpolation point and its nearest neighbor, the weight coefficients of each item in the bi-triplex interpolation operation process can be determined according to the above interpolation basis function. Finally, based on the weight coefficients and the light intensity data of each pixel in the neighborhood of the interpolation point, the interpolated light intensity data of the interpolation point can be determined by substituting them into the above bi-triplex interpolation operation function.
[0083] In practical applications, the area to be interpolated can be pre-divided into several blocks, and the weight coefficients for bi-tri-interpolation operations of each interpolation point in the block can be determined and stored. Finally, the processor performs parallel computation on the interpolated light intensity data of each interpolation point in each block. After the interpolated light intensity data of each interpolation point is completed, each interpolation point can form a high-resolution simulated photolithography pattern together with the original pixels.
[0084] In summary, after obtaining the initial simulated lithography pattern using a simulated lithography model, this application employs a bitriangular interpolation function to interpolate more points between pixels. Compared to conventional linear interpolation, the bitriangular interpolation function used in this application can improve the accuracy and reliability of the interpolation results to a certain extent without excessively increasing the computational load. Simultaneously, based on the light intensity gradient of each pixel within the neighborhood of the point to be interpolated, the parameter coefficients in the bitriangular interpolation function are determined. This means that the bitriangular interpolation function fully considers the light intensity gradient of its neighboring pixels during the determination of the interpolated light intensity data of the point to be interpolated, allowing the interpolation coefficients to be adjusted according to the light intensity gradient, further ensuring the accuracy and reliability of the final interpolated light intensity data. Therefore, this application can achieve accurate and reliable interpolation of the simulated lithography image simulated by the simulated lithography model, improving the resolution of the simulated lithography pattern while avoiding overly complex calculations.
[0085] The following describes the processing apparatus for simulated photolithography patterns provided in the embodiments of the present invention. The processing apparatus for simulated photolithography patterns described below and the processing method for simulated photolithography patterns described above can be referred to in correspondence with each other.
[0086] Figure 3 This is a structural block diagram of the apparatus for processing simulated photolithographic patterns provided in the embodiments of this application, with reference to... Figure 3 The apparatus for processing simulated photolithographic patterns may include:
[0087] The pattern simulation module 100 is used to obtain the light intensity data of each pixel in the initial simulated lithography pattern using a simulated lithography model.
[0088] The gradient calculation module 200 is used to perform gradient calculation on the light intensity data corresponding to the pixels in the neighborhood of the interpolation point to obtain the light intensity gradient.
[0089] The parameter calculation module 300 is used to determine the dynamic parameters of the bi-triple interpolation operation based on the light intensity gradient.
[0090] Interpolation module 400 is used to determine the interpolated light intensity data of the point to be interpolated based on dynamic parameters and bi-triangular interpolation function;
[0091] The pattern acquisition module 500 is used to obtain the interpolated simulated lithography pattern based on the interpolated light intensity data of each interpolation point and the initial simulated lithography pattern.
[0092] In one optional embodiment of this application, the gradient calculation module 200 is specifically used to calculate the light intensity data of each pixel within a set neighborhood of the interpolation point using an edge detection operator to obtain the horizontal gradient and the vertical gradient.
[0093] The parameter calculation module 300 is specifically used to perform tensor feature calculation on the horizontal gradient and the vertical gradient to obtain the first tensor feature value of each pixel in the first neighborhood range and the second tensor feature value of each pixel in the second neighborhood range of the point to be interpolated; wherein the first neighborhood range is smaller than the second neighborhood range; based on the first tensor feature value and the second tensor feature value, coherence calculation is performed, and dynamic parameters are determined based on the coherence data obtained from the calculation.
[0094] In an optional embodiment of this application, the parameter calculation module 300 is specifically used to calculate the horizontal and vertical gradients of each pixel within the first neighborhood of the interpolation point, according to the first tensor component calculation formula. Perform tensor feature operations to obtain the first tensor feature value; where, The first tensor eigenvalue; The first neighborhood of the point to be interpolated is the first... The horizontal and vertical gradients of each pixel; The first Gaussian kernel weight; The total number of pixels in the first neighborhood of the point to be interpolated; the first neighborhood is a 3×3 neighborhood; the horizontal and vertical gradients of each pixel in the second neighborhood of the point to be interpolated, calculated according to the second tensor component operation formula. Perform tensor feature operations to obtain the first tensor feature value; where, The eigenvalues of the second tensor; The first neighborhood of the point to be interpolated is the first... The horizontal and vertical gradients of each pixel; The second Gaussian kernel weight; This represents the total number of pixels within the second neighborhood of the point to be interpolated; the second neighborhood is a 7×7 neighborhood.
[0095] In an optional embodiment of this application, the parameter calculation module 300 is specifically used to calculate the parameters based on the first tensor eigenvalue, the second tensor eigenvalue, and the coherence formula. The coherence data is obtained through calculation; among them, For coherence data, The first tensor eigenvalue, The second tensor eigenvalue; an adaptive function based on coherence data and dynamic parameters. Determine the dynamic parameters; among which, These are dynamic parameters; The noise suppression threshold. This is the saturation threshold.
[0096] In an optional embodiment of this application, the interpolation calculation module 400 is specifically used to calculate based on dynamic parameters and interpolation basis functions. and bitrigonometric interpolation functions Determine the interpolated light intensity data for the points to be interpolated; among which, For interpolated light intensity data; Points to be interpolated The local coordinates within the neighborhood are The light intensity data of each pixel; These are dynamic parameters; For variable A changing function; These represent the horizontal and vertical spacing between the interpolation point and the nearest pixel, respectively. The spacing between two adjacent pixels in the horizontal direction and the spacing between two adjacent pixels in the vertical direction.
[0097] The simulated photolithography pattern processing apparatus of this embodiment is used to implement the aforementioned simulated photolithography pattern processing method. Therefore, the specific implementation of the simulated photolithography pattern processing apparatus can be found in the embodiment section of the simulated photolithography pattern processing method above. The specific implementation can be referred to the description of the corresponding embodiments, and will not be repeated here.
[0098] This application also provides a processing apparatus for simulating photolithographic patterns, including:
[0099] Memory, used to store computer programs;
[0100] A processor for executing the computer program to implement the steps of the method for processing simulated photolithography patterns as described in any of the preceding claims.
[0101] The steps of the simulated photolithography pattern processing method executed by the processor may include:
[0102] The light intensity data of each pixel in the initial simulated lithography pattern is obtained using a simulated lithography model; gradient calculation is performed on the light intensity data corresponding to the pixels in the neighborhood of the interpolation point to obtain the light intensity gradient; dynamic parameters for bi-triplex interpolation are determined based on the light intensity gradient; interpolated light intensity data of the interpolation point to be interpolated are determined based on the dynamic parameters and the bi-triplex interpolation function; and the interpolated simulated lithography pattern is obtained based on the interpolated light intensity data of each interpolation point and the initial simulated lithography pattern.
[0103] This application also provides an embodiment of a computer-readable storage medium storing a computer program that is executed to implement the steps of the processing method for simulating photolithography patterns as described in any of the preceding claims.
[0104] The computer-readable storage medium in this embodiment may include random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, register, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.
[0105] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that the elements inherent in a process, method, article, or apparatus that includes a list of elements are included. 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 said element. Additionally, portions of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of corresponding technical solutions in the prior art have not been described in detail to avoid excessive elaboration.
[0106] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. It should be noted that those skilled in the art can make several improvements and modifications to the present invention without departing from the principles of the present invention, and these improvements and modifications also fall within the protection scope of the present invention.
Claims
1. A method for processing simulated photolithographic patterns, characterized in that, include: The light intensity data of each pixel in the initial simulated lithography pattern is obtained using a simulated lithography model. Gradient calculation is performed on the light intensity data corresponding to the pixels in the neighborhood of the interpolation point to obtain the light intensity gradient; The dynamic parameters for the bitriangular interpolation operation are determined based on the light intensity gradient. The interpolated light intensity data of the point to be interpolated is determined based on the dynamic parameters and the bi-triple interpolation operation function. Based on the interpolated light intensity data of each interpolation point and the initial simulated lithography pattern, the simulated lithography pattern after interpolation is obtained.
2. The method for processing simulated photolithographic patterns as described in claim 1, characterized in that, Gradient calculations are performed on the light intensity data corresponding to pixels within the neighborhood of the interpolation point to obtain the light intensity gradient, including: The edge detection operator is used to calculate the light intensity data of each pixel within a set neighborhood of the interpolation point to obtain the horizontal and vertical gradients. The dynamic parameters for the bitriangular interpolation operation are determined based on the light intensity gradient, including: Tensor feature operations are performed on the horizontal gradient and the vertical gradient to obtain the first tensor feature value of each pixel in the first neighborhood range and the second tensor feature value of each pixel in the second neighborhood range of the point to be interpolated; wherein the first neighborhood range is smaller than the second neighborhood range. Based on the first tensor feature value and the second tensor feature value, a coherence calculation is performed, and the dynamic parameters are determined based on the coherence data obtained from the calculation.
3. The method for processing simulated photolithographic patterns as described in claim 2, characterized in that, Perform tensor feature operations on the horizontal and vertical gradients to obtain the first tensor feature values of each pixel within the first neighborhood of the point to be interpolated and the second tensor feature values of each pixel within the second neighborhood, including: Within the first neighborhood of the point to be interpolated, the horizontal gradient and the vertical gradient of each pixel are calculated according to the first tensor component operation formula. Perform tensor feature operations to obtain the first tensor feature value; wherein... The eigenvalue of the first tensor; The first neighboring region of the point to be interpolated is respectively the first... The horizontal and vertical gradients of each pixel; The first Gaussian kernel weight; The total number of pixels within the first neighborhood of the point to be interpolated; the first neighborhood is a 3×3 neighborhood. Within the second neighborhood of the point to be interpolated, the horizontal and vertical gradients of each pixel are calculated according to the second tensor component operation formula. Perform tensor feature operations to obtain the first tensor feature value; wherein... The eigenvalues of the second tensor; The first neighboring region of the point to be interpolated is respectively the first... The horizontal and vertical gradients of each pixel; The second Gaussian kernel weight; The number of pixels in the second neighborhood of the point to be interpolated is the total number of pixels in the second neighborhood; the second neighborhood is a 7×7 neighborhood.
4. The method for processing simulated photolithographic patterns as described in claim 2, characterized in that, Based on the first tensor eigenvalue and the second tensor eigenvalue, a coherence calculation is performed. Based on the coherence data obtained from the calculation, the dynamic parameters are determined, including: Based on the first tensor eigenvalue, the second tensor eigenvalue, and the coherence formula The coherence data is obtained through calculation; among them, For the coherence data, The first tensor eigenvalue, The eigenvalues of the second tensor; An adaptive function based on the coherence data and dynamic parameters Determine the dynamic parameters; wherein, These are dynamic parameters; The noise suppression threshold, This is the saturation threshold.
5. The method for processing simulated photolithographic patterns as described in any one of claims 1 to 4, characterized in that, The interpolated light intensity data of the point to be interpolated is determined based on the dynamic parameters and the bi-triangular interpolation function, including: Based on the dynamic parameters and interpolation basis functions and bitrigonometric interpolation functions Determine the interpolated light intensity data of the point to be interpolated; wherein, The interpolated light intensity data; The interpolation point The local coordinates within the neighborhood are The light intensity data of each pixel; These are dynamic parameters; For variable A changing function; These represent the horizontal and vertical distances between the interpolation point and the nearest pixel, respectively. These represent the spacing between two adjacent pixels in the horizontal direction and the spacing between two adjacent pixels in the vertical direction, respectively.
6. A processing apparatus for simulating photolithographic patterns, characterized in that, include: The pattern simulation module is used to obtain the light intensity data of each pixel in the initial simulated lithography pattern using a simulated lithography model. The gradient calculation module is used to perform gradient calculations on the light intensity data corresponding to the pixels in the neighborhood of the interpolation point to obtain the light intensity gradient. The parameter calculation module is used to determine the dynamic parameters of the bitriangular interpolation operation based on the light intensity gradient. An interpolation module is used to determine the interpolated light intensity data of the point to be interpolated based on the dynamic parameters and the bi-triple interpolation function. The pattern acquisition module is used to obtain the interpolated simulated lithography pattern based on the interpolated light intensity data of each interpolation point and the initial simulated lithography pattern.
7. The processing apparatus for simulating photolithographic patterns as described in claim 6, characterized in that, The gradient calculation module is specifically used to use the edge detection operator to calculate the light intensity data of each pixel within a set neighborhood of the interpolation point to obtain the horizontal gradient and the vertical gradient. The parameter calculation module is specifically used to perform tensor feature calculation on the horizontal gradient and the vertical gradient to obtain the first tensor feature value of each pixel in the first neighborhood range and the second tensor feature value of each pixel in the second neighborhood range of the interpolation point; wherein, the first neighborhood range is smaller than the second neighborhood range; based on the first tensor feature value and the second tensor feature value, coherence calculation is performed, and based on the coherence data obtained from the calculation, the dynamic parameters are determined.
8. The apparatus for processing simulated photolithographic patterns as described in claim 6 or 7, characterized in that, The interpolation module is specifically used to perform interpolation operations based on the dynamic parameters and the interpolation basis function. and bitrigonometric interpolation functions Determine the interpolated light intensity data of the point to be interpolated; wherein, The interpolated light intensity data; The interpolation point The local coordinates within the neighborhood are The light intensity data of each pixel; These are dynamic parameters; For variable A changing function; These represent the horizontal and vertical distances between the interpolation point and the nearest pixel, respectively. The spacing between two adjacent pixels in the horizontal direction and the spacing between two adjacent pixels in the vertical direction.
9. A processing device for simulating photolithographic patterns, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the method for processing simulated photolithographic patterns as described in any one of claims 1 to 5.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is executed to implement the steps of the method for processing simulated photolithography patterns as described in any one of claims 1 to 5.