A kind of inverse lithography inversion method, device, equipment and storage medium

Abstract

The present application relates to the field of integrated circuit manufacturing, in particular to a kind of inverse lithography inversion method, device, equipment and storage medium, by receiving chip pattern to be handled;Determine the corner point of the chip pattern to be handled;According to the preset rounding parameter, the corner point is rounded and handled, and the rounding transformation pattern is generated in the inside of the chip pattern to be handled;The chip pattern to be handled including the rounding transformation pattern is rasterized, and each grid is valued, and the mask target image distribution is obtained;According to the mask target image distribution, determine the mask pixel distribution;The mask pixel distribution is input into the preset optical model, and the mask best pattern distribution is obtained after multiple iterations.The space intensity in the present application is converted into sigmod image by means of sigmod function in optical simulation, the convergence efficiency of iteration process is improved, and the power consumption and the calculation time are also reduced, and then the exposure quality of integrated circuit chip is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of integrated circuit manufacturing, in particular to an inverse lithography inversion method, device, equipment and storage medium. BACKGROUND

[0002] With the continuous reduction of the key size of integrated circuits, the traditional optical proximity correction (OPC) gradually cannot meet the mask pattern calibration requirements, and the inverse lithography technique (ILT) gradually plays a more important role. ILT is a method of inversely calculating the required pattern on the mask based on the pattern to be realized on the silicon wafer. The exposed image on the wafer surface after lithography is set as the ideal imaging result, and the mask image is inversely calculated according to the transformation model of the imaging system space image.

[0003] ILT starts from the wafer exposure image, and reversely derives the optimal mask pattern, greatly improving the flexibility and accuracy of optimization to meet the stringent requirements of advanced processes for pattern precision. However, at present, ILT realizes the spectrum calibration of the mask pattern by decomposing the mask pattern into multiple degrees of freedom of pixel values, which has high computational complexity and huge amount of calculation, bringing great challenges to optimization calculation. In particular, ILT is a typical large-scale optimization problem, and the optimization process is multi-solution and ill-posed. Currently, iterative solution algorithms based on conjugate gradient method, minimum residual method, etc. are used to obtain the pixel-level light mask pattern distribution. However, the optimization results of these iterative methods strictly depend on the initial value of iteration, and different initial values of iteration usually obtain local minimum values, which cannot meet the best mask pattern design requirements.

[0004] Therefore, how to improve the calculation efficiency of inverse lithography inversion calculation of mask pattern, reduce the occupation of computing power, and at the same time improve the convergence efficiency in the iteration process, and then improve the exposure quality and yield of integrated circuit chips, is a problem to be solved by those skilled in the art. SUMMARY

[0005] The purpose of the present application is to provide an inverse lithography inversion method, device, equipment and storage medium to solve the problems of low calculation efficiency, high occupation of computing power, and poor convergence efficiency in the iteration process of inverse lithography inversion calculation of mask pattern in the prior art, and thus the exposure quality and yield of integrated circuit chips are reduced.

[0006] To solve the above technical problems, the present application provides an inverse lithography inversion method, comprising:

[0007] receiving a chip pattern to be processed;

[0008] determining the corner points of the chip pattern to be processed;

[0009] According to a preset rounding parameter, the corner point is rounded to generate a rounding transformation pattern inside the chip pattern to be processed;

[0010] The chip pattern to be processed including the rounding transformation pattern is rasterized, and each grid is assigned a value to obtain a mask target image distribution; wherein the value corresponding to the grid completely overlapping with the rounding transformation pattern and the value corresponding to the grid completely not overlapping with the rounding transformation pattern are different, and the value corresponding to the grid partially overlapping with the rounding transformation pattern is the area ratio of the rounding transformation pattern in the grid;

[0011] According to the mask target image distribution, a mask pixel distribution is determined;

[0012] The mask pixel distribution is input into a preset optical model, and a mask optimal pattern distribution is obtained through multiple iterations; in each iteration, the optical model simulates the mask pixel distribution through a preset optical kernel function to obtain a corresponding spatial intensity, and then converts the spatial intensity into a sigmod image by using a sigmod function, and compares the sigmod image with the mask target image distribution until the difference between the sigmod image and the mask target image distribution is minimized; when the difference between the sigmod image and the mask target image distribution is minimized, the mask pixel distribution corresponding to the sigmod image is taken as the mask optimal pattern distribution.

[0013] Optionally, in the inverse lithography inversion method, the difference between the sigmod image and the mask target image distribution is minimized when the function value of the loss function of formula (1) is minimized:

[0014] ; (1)

[0015] Wherein, N is the total number of grids, T(x, y) is the mask target image distribution, t i is the value corresponding to the i-th grid in the mask target image distribution, IS(x, y) is the sigmod image, is i is the value corresponding to the i-th grid in the sigmod image.

[0016] Optionally, in the inverse lithography inversion method, the mask pixel distribution is input into a preset optical model, and a mask optimal pattern distribution is obtained through multiple iterations, including:

[0017] The mask pixel distribution is input into a preset optical model, and a mask optimal pattern distribution is obtained through multiple iterations by using the loss function to calculate the gradient of the mask pixel distribution through an Adam algorithm.

[0018] Optionally, in the inverse lithography method, the mask pixel distribution is determined according to the mask target image distribution, comprising:

[0019] The values in the grid which does not overlap with the round-corner transformation pattern in the mask target image distribution are replaced by random values to obtain the mask pixel distribution.

[0020] Optionally, in the inverse lithography method, the mask target image distribution is obtained by gridding the chip pattern to be processed which includes the round-corner transformation pattern and assigning values to each grid, comprising:

[0021] The chip pattern to be processed which includes the round-corner transformation pattern is gridded and values are assigned to each grid to obtain the mask target image distribution; wherein the value corresponding to the grid which does not overlap with the round-corner transformation pattern is 0 and the value corresponding to the grid which completely overlaps with the round-corner transformation pattern is 1.

[0022] Correspondingly, the values in the grid which does not overlap with the round-corner transformation pattern in the mask target image distribution are replaced by random values to obtain the mask pixel distribution, comprising:

[0023] The values in the grid which does not overlap with the round-corner transformation pattern in the mask target image distribution are replaced by random values between 0 and 1 to obtain the mask pixel distribution.

[0024] Optionally, in the inverse lithography method, the mask pixel distribution is input into a preset optical model and the mask optimal pattern distribution is obtained through multiple iterations, further comprising:

[0025] The mask pixel distribution is input into a preset optical model and the mask optimal pattern distribution is obtained through a preset first number of iterations; the mask pixel distribution corresponding to the sigmod image of the mask pixel distribution of the first number of iterations is taken as the mask optimal pattern distribution.

[0026] An inverse lithography device, comprising:

[0027] A receiving module for receiving a chip pattern to be processed;

[0028] An angle point module for determining an angle point of the chip pattern to be processed;

[0029] A round-cornering module for performing round-cornering processing on the angle point according to preset round-cornering parameters to generate a round-corner transformation pattern in the chip pattern to be processed;

[0030] a rastering module configured to rasterize a chip pattern to be processed including the round-corner conversion pattern, and assign a value to each grid to obtain a mask target image distribution; wherein the value corresponding to a grid completely overlapped with the round-corner conversion pattern is different from the value corresponding to a grid not overlapped with the round-corner conversion pattern, and the value corresponding to a grid partially overlapped with the round-corner conversion pattern is a proportion of an area of the round-corner conversion pattern in the grid;

[0031] an initial distribution module configured to determine a mask pixel distribution according to the mask target image distribution;

[0032] a model iteration module configured to input the mask pixel distribution into a preset optical model, and obtain a mask optimal pattern distribution through multiple iterations; in each iteration, the optical model simulates the mask pixel distribution through a preset optical kernel function to obtain a corresponding spatial intensity, converts the spatial intensity into a sigmod image using a sigmod function, and compares the sigmod image with the mask target image distribution until a difference between the sigmod image and the mask target image distribution is minimum, and when the difference between the sigmod image and the mask target image distribution is minimum, the sigmod image corresponds to the mask pixel distribution as the mask optimal pattern distribution.

[0033] Optionally, in the inverse lithography inversion device, the model iteration module comprises:

[0034] a loss function unit configured to determine that the difference between the sigmod image and the mask target image distribution is minimum when a function value in the following formula (1) is minimum:

[0035] ; (1)

[0036] wherein N is a total number of grids, T(x, y) is the mask target image distribution, t i is a value corresponding to an i-th grid in the mask target image distribution, IS(x, y) is the sigmod image, is i is a value corresponding to the i-th grid in the sigmod image.

[0037] An inverse lithography inversion device, comprising:

[0038] a memory configured to store a computer program;

[0039] a processor configured to implement steps of the inverse lithography inversion method when the computer program is executed.

[0040] A computer readable storage medium, the computer readable storage medium has a computer program stored thereon, the computer program is executed by a processor to implement the steps of the inverse lithography inversion method described above.

[0041] The inverse lithography inversion method provided by the application, by receiving a chip pattern to be processed; determining the corner point of the chip pattern to be processed; according to the preset rounding parameter, the corner point is rounded, and the rounding transformation pattern is generated inside the chip pattern to be processed; the chip pattern to be processed including the rounding transformation pattern is rasterized, and each grid is valued, and the mask target image distribution is obtained; wherein the value corresponding to the grid completely overlapping with the rounding transformation pattern and the value corresponding to the grid not completely overlapping with the rounding transformation pattern are different, and the value corresponding to the grid partially overlapping with the rounding transformation pattern is the area ratio of the rounding transformation pattern in the grid; according to the mask target image distribution, the mask pixel distribution is determined; the mask pixel distribution is input into the preset optical model, and the mask optimal pattern distribution is obtained through multiple iterations; in each iteration, the optical model simulates the mask pixel distribution through the preset optical kernel function, obtains the corresponding spatial intensity, converts the spatial intensity into a sigmod image by using a sigmod function, and compares the sigmod image with the mask target image distribution until the difference between the sigmod image and the mask target image distribution is minimum, when the difference between the sigmod image and the mask target image distribution is minimum, the mask pixel distribution corresponding to the sigmod image is taken as the mask optimal pattern distribution. In the application, the spatial intensity in optical simulation is converted into a sigmod image by means of a sigmod function, the difference between the value distribution of the grid in the sigmod image and the mask target image distribution presented in the form of a sigmod image is compared, the similarity between the simulation result and the target is judged, the convergence efficiency in the iteration process is greatly improved, the calculation power consumption and the calculation time are greatly reduced, and the exposure quality and the yield of the integrated circuit chip are improved. The application also provides an inverse lithography inversion device, equipment and storage medium with the above beneficial effects. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0043] Figure 1A flowchart of one embodiment of the inverse lithography method provided by the present application;

[0044] Figure 2 A chip pattern to be processed in one embodiment of the inverse lithography method provided by the present application;

[0045] Figure 3 A chip pattern to be processed in one embodiment of the inverse lithography method provided by the present application, which includes a round corner transformed pattern;

[0046] Figure 4 A structural schematic diagram of one embodiment of the inverse lithography device provided by the present application.

[0047] Reference signs:

[0048] 100-receiving module, 200-corner point module, 300-rounding module, 400-rasterization module, 500-initial distribution module, 600-model iteration module. DETAILED DESCRIPTION

[0049] In order for those skilled in the art to better understand the present application, the present application will be further described in detail below in combination with the drawings and specific embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0050] The core of the present application is to provide an inverse lithography method, a flowchart of one embodiment of which is shown in Figure 1 , which is referred to as embodiment one, which includes:

[0051] S101: receiving a chip pattern to be processed.

[0052] The chip pattern to be processed, i.e., the pattern finally needed to be printed on a wafer, can refer to the polygon in Figure 2 , Figure 2 which is the chip pattern to be processed in one specific embodiment.

[0053] S102: determining a corner point of the chip pattern to be processed.

[0054] As previously mentioned, the pattern currently needed to be printed on a wafer is usually a Manhattan pattern, and the Manhattan pattern is a pattern composed of horizontal and vertical lines, and the corner point is the top corner position of the Manhattan pattern.

[0055] S103: According to the preset corner rounding parameters, the corner points are rounded to generate a corner rounding transformation pattern inside the chip pattern to be processed.

[0056] The rounding parameters can be adjusted according to specific circumstances, and this invention does not impose any limitations on them. After the rounding process, the resulting rounded corner transformation pattern no longer has the apex corner formed by the intersection of two straight lines, but instead presents a curved edge at the original corner point. Figure 2 The rounded corner transformation pattern formed after the chip pattern to be processed in the image can be referenced. Figure 3 , Figure 3 The image still shows the chip pattern to be processed. This is because the rounded corner transformation pattern is just an edge variation of the chip pattern to be processed. The two patterns can actually be considered to be in the same position. The rounded corner transformation pattern after rounding is marked with a diagonal line area.

[0057] S104: The chip pattern to be processed, including the rounded corner transformation pattern, is rasterized, and a value is assigned to each grid to obtain the mask target image distribution; wherein, the value corresponding to the grid that completely overlaps with the rounded corner transformation pattern and the value corresponding to the grid that does not completely overlap with the rounded corner transformation pattern are different, and the value corresponding to the grid that partially overlaps with the rounded corner transformation pattern is the area ratio of the rounded corner transformation pattern in the grid.

[0058] In this step, it can be considered that the direct approach is... Figure 3 The image is rasterized, that is, the chip pattern to be processed, including the rounded corner transformation pattern, is divided into numerous grids, and values ​​are assigned according to whether the rounded corner transformation pattern exists in the grid and how many rounded corner transformation patterns there are. After the assignment, the resulting mask target image is essentially a grid array labeled with the corresponding values.

[0059] In this step, the value assigned to the grid that partially overlaps with the rounded corner transformation pattern is determined by the area ratio of the rounded corner transformation pattern in the grid. For example, if the rounded corner transformation pattern occupies half of the grid area, then the value corresponding to that grid is 1 / 2.

[0060] It should be noted that the rounding parameters mentioned above are based on the actual photolithography mask parameters and experimental results. Therefore, the rounding transformation pattern can be directly determined as the target pattern that the mask needs to achieve in order to realize the chip pattern to be processed. Correspondingly, the corresponding grid distribution becomes the target image distribution of the mask.

[0061] S105: Determine the mask pixel distribution based on the mask target image distribution.

[0062] After the target of iteration (i.e. the mask target image distribution) is determined, an initial distribution for iteration is also set. In the present application, the values in the grid are directly changed on the basis of the mask target image distribution, and the obtained grid distribution can be used as the initial image distribution for inputting the optical model, i.e. the mask pixel distribution is the initial distribution.

[0063] S106: input the mask pixel distribution into a preset optical model, and obtain a mask optimal pattern distribution through multiple iterations; in each iteration, the optical model simulates the mask pixel distribution through a preset optical kernel function to obtain a corresponding spatial intensity, and then converts the spatial intensity into a sigmod image by using a sigmod function, and compares the sigmod image with the mask target image distribution until the difference between the sigmod image and the mask target image distribution is minimum, and when the difference between the sigmod image and the mask target image distribution is minimum, the mask pixel distribution corresponding to the sigmod image is taken as the mask optimal pattern distribution.

[0064] In this step, the mask pixel distribution obtained in step S105 is input into the optical model for optical simulation, and the mask pixel distribution is iterated without stop, and it is expected that the completed iteration mask pixel distribution is the same as the mask target image distribution.

[0065] In this step, the mask pixel distribution needs to be iterated multiple times. In a specific single iteration process, the mask pixel distribution needs to be first converted into a spatial intensity through the following formula (2):

[0066] ; (2)

[0067] wherein I(x, y) is the spatial intensity, h k (x, y) is the kth optical kernel function, K is the number of effective truncated optical kernel functions, is a convolution operator, u k is the weight coefficient corresponding to the kth optical kernel function, and P(x, y) is the mask pixel distribution.

[0068] After the spatial intensity is obtained, the sigmod image is further obtained by using the sigmod function through the following formula (3):

[0069] ; (3)

[0070] wherein IS(x, y) is the sigmod image.

[0071] Specifically, the sigmod function can be represented by the following formula (4):

[0072] (4)

[0073] wherein a and t r are preset control parameters, which are used to determine the smoothness of the sigmod function.

[0074] As a preferred embodiment, the difference between the sigmod image and the mask target image distribution is determined to be the smallest when the function value of the loss function in the following formula (1) is the smallest:

[0075] (1)

[0076] wherein N is the total number of grids, T(x, y) is the mask target image distribution, t i is the value corresponding to the i-th grid in the mask target image distribution, IS(x, y) is the sigmod image, is i is the value corresponding to the i-th grid in the sigmod image.

[0077] In the present embodiment, the difference between the values of the grids at the corresponding positions in the mask target image distribution and the sigmod image is calculated, and the difference between the values of all the grids is taken as the function value of the loss function, so that the convergence speed is fast, the calculation efficiency is further improved, the calculation process is clear and direct, and the accuracy of the obtained result is higher.

[0078] Further, the mask pixel distribution is input into a preset optical model, and the mask optimal pattern distribution is obtained through multiple iterations, including:

[0079] The mask pixel distribution is input into a preset optical model, and the mask optimal pattern distribution is obtained through multiple iterations by using the loss function to calculate the gradient of the mask pixel distribution by means of the Adam algorithm.

[0080] In the present preferred embodiment, the gradient of the loss function with respect to the mask pixel distribution is calculated by means of the Adam algorithm (Adaptive moment method), so that the position of the minimum value of the loss function can be quickly determined according to the change in the gradient, and the fast convergence of the iteration process is completed.

[0081] In combination with the preferred embodiments described above, the gradient of the loss function with respect to the mask pixel distribution can be expressed as the following formula (5):

[0082] (5)

[0083] wherein T represents matrix transposition, e represents element-by-element calculation, and the obtained gradient Substitute the Adam to carry out the update to the grid, realize iteration, including the following formula (6), formula (7), formula (8), formula (9), formula (10):

[0084] ; (6)

[0085] ; (7)

[0086] ; (8)

[0087] ; (9)

[0088] ; (10)

[0089] Wherein, subscript t is the number of iterations, b1, b2 is the corresponding hyperparameter, a is the gradient update step, e is the error threshold (used to prevent the denominator from being 0), m, v, 、 All are recursive intermediate variables, no physical meaning. Substitute formula (1), formula (2) and formula (3) into formula (6)-(10), and the iteration update can be carried out until the difference between the sigmod image and the mask target image distribution is minimum.

[0090] Further, according to the mask target image distribution, the mask pixel distribution is determined, comprising:

[0091] The values in the grid completely not overlapping with the round corner transformation pattern in the mask target image distribution are replaced by random values to obtain the mask pixel distribution.

[0092] In the preferred embodiment, the method of replacing the values in the grid completely not overlapping with the round corner transformation pattern with random values is used to obtain the initial mask pixel distribution for inputting the optical model, which can greatly reduce the computing power consumption and shorten the acquisition time of the initial mask pixel distribution. At the same time, since only the values in the grid completely not overlapping with the round corner transformation pattern are changed, the number of iterations can also be reduced, the iteration time can be shortened, and the final simulation accuracy can be improved.

[0093] Further, the chip pattern to be processed including the round corner transformation pattern is rasterized, and each grid is assigned a value to obtain a mask target image distribution, comprising:

[0094] A1: rasterizing the chip pattern to be processed including the round corner transformation pattern, and assigning a value to each grid to obtain a mask target image distribution; wherein the value corresponding to the grid completely not overlapping with the round corner transformation pattern is 0, and the value corresponding to the grid completely overlapping with the round corner transformation pattern is 1.

[0095] Accordingly, the values in the grid that do not overlap with the rounded corner transformation pattern in the mask target image distribution are replaced by random values to obtain a mask pixel distribution, comprising:

[0096] A2: Replacing the values in the grid that do not overlap with the rounded corner transformation pattern in the mask target image distribution with random values between 0 and 1 to obtain a mask pixel distribution.

[0097] In the preferred embodiment, the value corresponding to the grid that does not overlap with the rounded corner transformation pattern is 0, and the value corresponding to the grid that completely overlaps with the rounded corner transformation pattern is 1. In addition, in the process of determining the mask pixel distribution, the values in the grid that do not overlap with the rounded corner transformation pattern are replaced by random values between 0 and 1, so that the values of all grids in the obtained mask pixel distribution and the values corresponding to all grids in the mask target image are values between 0 and 1, which is beneficial to the iteration of the Adam algorithm and can greatly improve the efficiency of the Adam iteration and further shorten the iteration time.

[0098] In another specific embodiment, the mask pixel distribution is input into a pre-set optical model, and a mask optimal pattern distribution is obtained through multiple iterations, further comprising:

[0099] The mask pixel distribution is input into a pre-set optical model, and a mask optimal pattern distribution is obtained through a pre-set first number of iterations; the mask pixel distribution corresponding to the sigmod image of the mask pixel distribution of the first number of iterations is taken as the mask optimal pattern distribution.

[0100] In the specific embodiment, an upper limit is set for the number of iterations, in other words, once the number of iterations exceeds the first number, even if the difference between the sigmod image and the mask target image distribution has not reached the minimum, the iteration will not continue, but the mask pixel distribution of the last iteration is directly output as the mask optimal pattern distribution, avoiding the simulation results of part of the patterns from dragging the inverse lithography of other chip patterns to be processed, and improving the working stability of the inverse lithography method.

[0101] The inverse lithography inversion method provided by the application comprises the following steps: receiving a chip pattern to be processed; determining a corner point of the chip pattern to be processed; performing a rounding treatment on the corner point according to a preset rounding parameter, and generating a rounding transformation pattern in the interior of the chip pattern to be processed; rasterizing the chip pattern to be processed including the rounding transformation pattern, and assigning a value to each grid to obtain a mask target image distribution; wherein the value corresponding to the grid completely overlapping with the rounding transformation pattern is different from the value corresponding to the grid not completely overlapping with the rounding transformation pattern, and the value corresponding to the grid partially overlapping with the rounding transformation pattern is the area ratio of the rounding transformation pattern in the grid; determining a mask pixel distribution according to the mask target image distribution; inputting the mask pixel distribution into a preset optical model, and obtaining a mask optimal pattern distribution through multiple iterations; in each iteration, the optical model simulates the mask pixel distribution through a preset optical kernel function to obtain a corresponding spatial intensity, converts the spatial intensity into a sigmod image by using a sigmod function, and compares the sigmod image with the mask target image distribution until the difference between the sigmod image and the mask target image distribution is minimum, and when the difference between the sigmod image and the mask target image distribution is minimum, the mask pixel distribution corresponding to the sigmod image is taken as the mask optimal pattern distribution. In the application, the spatial intensity in optical simulation is converted into a sigmod image by using a sigmod function, the difference between the value distribution of the grid in the sigmod image and the mask target image distribution in the form of the sigmod image is compared, the similarity between the simulation result and the target is judged, the convergence efficiency in the iteration process is greatly improved, the calculation power consumption and the calculation time are greatly reduced, and the exposure quality and the yield of the integrated circuit chip are improved.

[0102] The inverse lithography inversion device provided by the embodiment of the application will be introduced below, and the inverse lithography inversion device described below can be correspondingly referred to the inverse lithography inversion method described above.

[0103] Figure 4 The structure block diagram of the inverse lithography inversion device provided by the embodiment of the application is described with reference to Figure 4 The inverse lithography inversion device can comprise:

[0104] The receiving module 100 is configured to receive a chip pattern to be processed.

[0105] The corner point module 200 is configured to determine a corner point of the chip pattern to be processed.

[0106] The rounding module 300 is configured to perform a rounding treatment on the corner point according to a preset rounding parameter, and generate a rounding transformation pattern in the interior of the chip pattern to be processed.

[0107] The rasterization module 400 is configured to rasterize a chip pattern to be processed including the round-corner conversion pattern, and assign a value to each grid to obtain a mask target image distribution; wherein the value corresponding to a grid completely overlapping with the round-corner conversion pattern is different from the value corresponding to a grid not overlapping with the round-corner conversion pattern at all, and the value corresponding to a grid partially overlapping with the round-corner conversion pattern is a proportion of an area of the round-corner conversion pattern in the grid.

[0108] The initial distribution module 500 is configured to determine a mask pixel distribution according to the mask target image distribution.

[0109] The model iteration module 600 is configured to input the mask pixel distribution into a preset optical model, and obtain a mask optimal pattern distribution through multiple iterations; in each iteration, the optical model simulates the mask pixel distribution through a preset optical kernel function to obtain a corresponding spatial intensity, converts the spatial intensity into a sigmod image by using a sigmod function, and compares the sigmod image with the mask target image distribution until a difference between the sigmod image and the mask target image distribution is minimum, and when the difference between the sigmod image and the mask target image distribution is minimum, the mask pixel distribution corresponding to the sigmod image is taken as the mask optimal pattern distribution.

[0110] As a preferred implementation, the model iteration module 600 comprises:

[0111] The loss function unit is configured to determine that the difference between the sigmod image and the mask target image distribution is minimum when a function value in the following formula (1) is minimum:

[0112] (1)

[0113] wherein N is a total number of grids, T(x, y) is the mask target image distribution, t i is a value corresponding to an i-th grid in the mask target image distribution, IS(x, y) is the sigmod image, is i is a value corresponding to the i-th grid in the sigmod image.

[0114] As a preferred implementation, the model iteration module 600 comprises:

[0115] The Adam iteration unit is configured to input the mask pixel distribution into a preset optical model, and obtain a mask optimal pattern distribution through multiple iterations by using an Adam algorithm and a gradient of the mask pixel distribution with respect to the loss function.

[0116] As a preferred implementation, the initial distribution module 500 comprises:

[0117] The non-overlapping grid random replacement unit is configured to replace values in grids that are completely non-overlapping with the rounded corner transformation pattern in the mask target image distribution with random values to obtain a mask pixel distribution.

[0118] As a preferred implementation, the gridding module 400 comprises:

[0119] The zero-one grid filling unit is configured to grid the chip pattern to be processed including the rounded corner transformation pattern and assign values to each grid to obtain a mask target image distribution; values corresponding to grids that are completely non-overlapping with the rounded corner transformation pattern are 0, and values corresponding to grids that are completely overlapping with the rounded corner transformation pattern are 1.

[0120] Correspondingly, the initial distribution module 500 comprises:

[0121] The zero-one random replacement unit is configured to replace values in grids that are completely non-overlapping with the rounded corner transformation pattern in the mask target image distribution with random values between 0 and 1 to obtain a mask pixel distribution.

[0122] As a preferred implementation, the model iteration module 600 further comprises:

[0123] The iteration number threshold unit is configured to input the mask pixel distribution into a preset optical model, perform a preset first number of iterations to obtain a mask optimal pattern distribution, and take a mask pixel distribution corresponding to a sigmod image of the mask pixel distribution after the first number of iterations as the mask optimal pattern distribution.

[0124] The inverse lithography inversion method provided by the application comprises a receiving module 100 configured to receive a chip pattern to be processed; an angle point module 200 configured to determine an angle point of the chip pattern to be processed; a rounding module 300 configured to perform rounding processing on the angle point according to preset rounding parameters, and generate a rounding transformation pattern in the interior of the chip pattern to be processed; a gridding module 400 configured to perform gridding on the chip pattern to be processed including the rounding transformation pattern, and assign a value to each grid to obtain a mask target image distribution; wherein the value corresponding to a grid completely overlapping with the rounding transformation pattern is different from the value corresponding to a grid not overlapping with the rounding transformation pattern at all, and the value corresponding to a grid partially overlapping with the rounding transformation pattern is the area ratio of the rounding transformation pattern in the grid; an initial distribution module 500 configured to determine a mask pixel distribution according to the mask target image distribution; and a model iteration module 600 configured to input the mask pixel distribution into a preset optical model, and obtain a mask optimal pattern distribution through multiple iterations. In each iteration, the optical model simulates the mask pixel distribution through a preset optical kernel function to obtain a corresponding spatial intensity, converts the spatial intensity into a sigmod image by using a sigmod function, and compares the sigmod image with the mask target image distribution until the difference between the sigmod image and the mask target image distribution is minimized. When the difference between the sigmod image and the mask target image distribution is minimized, the mask pixel distribution corresponding to the sigmod image is taken as the mask optimal pattern distribution. In the application, the spatial intensity in optical simulation is converted into a sigmod image by using a sigmod function. The similarity between the simulation result and the target is judged by comparing the difference between the value distribution of the grid in the sigmod image and the mask target image distribution in the form of the sigmod image, which greatly improves the convergence efficiency in the iteration process, greatly reduces the computing power consumption and the calculation time, and further improves the exposure quality and the yield of the integrated circuit chip.

[0125] The inverse lithography inversion device of the embodiment is used to implement the inverse lithography inversion method described above, and therefore the specific embodiments in the inverse lithography inversion device can be seen from the embodiment part of the inverse lithography inversion method described above. For example, the receiving module 100, the angle point module 200, the rounding module 300, the gridding module 400, the initial distribution module 500, and the model iteration module 600 are respectively used to implement steps S101, S102, S103, S104, S105, and S106 in the inverse lithography inversion method described above. Therefore, the specific embodiments can be referred to the description of the corresponding embodiment part, and will not be described here.

[0126] The application further provides an inverse lithography inversion device, comprising:

[0127] a memory for storing a computer program;

[0128] a processor for implementing the steps of the inverse lithography method when executing the computer program. The inverse lithography method provided by the present application comprises the steps of: receiving a chip pattern to be processed; determining a corner point of the chip pattern to be processed; performing a rounding processing on the corner point according to a preset rounding parameter, to generate a rounding transformation pattern inside the chip pattern to be processed; rasterizing the chip pattern to be processed including the rounding transformation pattern, and assigning a value to each grid to obtain a mask target image distribution; wherein the value corresponding to a grid completely overlapping with the rounding transformation pattern is different from the value corresponding to a grid not overlapping with the rounding transformation pattern, and the value corresponding to a grid partially overlapping with the rounding transformation pattern is the area ratio of the rounding transformation pattern in the grid; determining a mask pixel distribution according to the mask target image distribution; inputting the mask pixel distribution into a preset optical model, and obtaining a mask optimal pattern distribution through multiple iterations; in each iteration, the optical model simulates the mask pixel distribution through a preset optical kernel function to obtain a corresponding spatial intensity, converts the spatial intensity into a sigmod image by using a sigmod function, and compares the sigmod image with the mask target image distribution until the difference between the sigmod image and the mask target image distribution is minimized, and when the difference between the sigmod image and the mask target image distribution is minimized, the mask pixel distribution corresponding to the sigmod image is taken as the mask optimal pattern distribution. In the present application, the spatial intensity in optical simulation is converted into a sigmod image by using a sigmod function, the difference between the value distribution of the grid in the sigmod image and the mask target image distribution presented in the form of a sigmod image is compared, the similarity between the simulation result and the target is judged, the convergence efficiency in the iteration process is greatly improved, the calculation power consumption and the calculation time are greatly reduced, and the exposure quality and the yield of the integrated circuit chip are improved.

[0129] The application further provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the inverse lithography inversion method. The inverse lithography inversion method provided by the application comprises the following steps: receiving a chip pattern to be processed; determining a corner point of the chip pattern to be processed; performing a rounding treatment on the corner point according to a preset rounding parameter, and generating a rounding transformation pattern in the inside of the chip pattern to be processed; rasterizing the chip pattern to be processed including the rounding transformation pattern, and assigning a value to each grid to obtain a mask target image distribution; wherein the value corresponding to the grid completely overlapping with the rounding transformation pattern is different from the value corresponding to the grid not completely overlapping with the rounding transformation pattern, and the value corresponding to the grid partially overlapping with the rounding transformation pattern is the area ratio of the rounding transformation pattern in the grid; determining a mask pixel distribution according to the mask target image distribution; inputting the mask pixel distribution into a preset optical model, and obtaining a mask optimal pattern distribution through multiple iterations; in each iteration, the optical model simulates the mask pixel distribution through a preset optical kernel function to obtain a corresponding spatial intensity, and then converts the spatial intensity into a sigmod image by using a sigmod function, and compares the sigmod image with the mask target image distribution until the difference between the sigmod image and the mask target image distribution is minimum, and when the difference between the sigmod image and the mask target image distribution is minimum, the mask pixel distribution corresponding to the sigmod image is taken as the mask optimal pattern distribution. In the application, the spatial intensity in optical simulation is converted into a sigmod image by using a sigmod function, the difference between the value distribution of the grid in the sigmod image and the mask target image distribution in the form of a sigmod image is compared, the similarity between the simulation result and the target is judged, the convergence efficiency in the iteration process is greatly improved, the calculation power consumption and the calculation time are greatly reduced, and the exposure quality and the yield of the integrated circuit chip are improved.

[0130] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts of each embodiment can be referred to each other.

[0131] It should be noted that, in the specification, the terms such as first and second, etc. are merely intended to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of additional identical elements in the process, method, article or device including the element.

[0132] The skilled person can further realize that the units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be realized in electronic hardware, computer software, or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in general terms in the above description. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0133] The steps of the methods or algorithms described in connection with the embodiments disclosed herein can be directly implemented in hardware, software modules executed by a processor, or a combination of both. The software modules can be placed in random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art.

[0134] The above provides a detailed introduction to the inverse lithography inversion method, device, equipment and storage medium provided by the present application. The principles and implementation modes of the present application are described in this paper by applying specific examples. The above description of the examples is only used to help understand the method of the present application and its core idea. It should be pointed out that for ordinary skilled persons in the art, without departing from the principles of the present application, some improvements and modifications can be made to the present application, and these improvements and modifications also fall within the protection scope of the present application.

Claims

1. A reverse photolithography inversion method, characterized in that, include: Receive the pattern of the chip to be processed; Determine the corner points of the chip pattern to be processed; According to the preset corner rounding parameters, the corner points are rounded to generate a corner rounding transformation pattern inside the chip pattern to be processed. The chip pattern to be processed, including the rounded corner transformation pattern, is rasterized, and a value is assigned to each grid to obtain the mask target image distribution; wherein, the value corresponding to the grid that completely overlaps with the rounded corner transformation pattern and the value corresponding to the grid that does not completely overlap with the rounded corner transformation pattern are different, and the value corresponding to the grid that partially overlaps with the rounded corner transformation pattern is the area ratio of the rounded corner transformation pattern in the grid. The mask pixel distribution is determined based on the distribution of the target image. The mask pixel distribution is input into a preset optical model, and the optimal mask pattern distribution is obtained through multiple iterations. In each iteration, the optical model simulates the mask pixel distribution using a preset optical kernel function to obtain the corresponding spatial intensity. Then, the spatial intensity is converted into a sigmoid image using the sigmoid function, and the sigmoid image is compared with the mask target image distribution until the difference between the sigmoid image and the mask target image distribution is minimized. When the difference between the sigmoid image and the mask target image distribution is minimized, the mask pixel distribution corresponding to the sigmoid image is taken as the optimal mask pattern distribution. Based on the distribution of the target image in the mask, the distribution of mask pixels is determined, including: In the mask target image distribution, the values ​​in the grids that do not overlap with the rounded corner transformation pattern are replaced with random values ​​to obtain the mask pixel distribution.

2. The reverse lithography inversion method as described in claim 1, characterized in that, The difference between the distributions of the sigmoid image and the mask target image is minimized when the function value F of the loss function in the following formula is minimized: ； Where N is the total number of grid cells, T(x, y) is the distribution of the mask target image, and t i IS(x, y) is the value corresponding to the i-th grid in the mask target image distribution, and IS(x, y) is the sigmoid image. i This is the value corresponding to the i-th raster in the sigmoid image.

3. The reverse lithography inversion method as described in claim 2, characterized in that, The mask pixel distribution is input into a preset optical model, and the optimal mask pattern distribution is obtained through multiple iterations, including: The mask pixel distribution is input into a preset optical model, and the optimal mask pattern distribution is obtained through multiple iterations using the Adam algorithm and the gradient of the mask pixel distribution with respect to the loss function.

4. The reverse lithography inversion method as described in claim 1, characterized in that, The chip pattern to be processed, including the rounded corner transformation pattern, is rasterized, and a value is assigned to each raster to obtain the mask target image distribution, including: The chip pattern to be processed, including the rounded corner transformation pattern, is rasterized, and each raster is assigned a value to obtain the mask target image distribution; wherein, the value corresponding to the raster that does not overlap with the rounded corner transformation pattern is 0, and the value corresponding to the raster that completely overlaps with the rounded corner transformation pattern is 1. Accordingly, the step of randomly replacing the values ​​in the grid cells that do not overlap with the rounded corner transformation pattern in the mask target image distribution to obtain the mask pixel distribution includes: In the mask target image distribution, the values ​​in the grids that do not overlap with the rounded corner transformation pattern are replaced with random values ​​between 0 and 1 to obtain the mask pixel distribution.

5. The reverse lithography inversion method as described in claim 1, characterized in that, The process of inputting the mask pixel distribution into a preset optical model and obtaining the optimal mask pattern distribution through multiple iterations also includes: The mask pixel distribution is input into a preset optical model, and the optimal mask pattern distribution is obtained after a preset first number of iterations; the mask pixel distribution corresponding to the sigmoid image of the mask pixel distribution of the first number of iterations is taken as the optimal mask pattern distribution.

6. A reverse lithography inversion apparatus, characterized in that, include: The receiving module is used to receive the chip pattern to be processed; A corner point module is used to determine the corner points of the chip pattern to be processed; The corner rounding module is used to round the corners according to preset corner rounding parameters and generate a corner rounding transformation pattern inside the chip pattern to be processed. A rasterization module is used to rasterize the chip pattern to be processed, including the rounded corner transformation pattern, and assign a value to each grid to obtain the mask target image distribution; wherein, the value corresponding to the grid that completely overlaps with the rounded corner transformation pattern and the value corresponding to the grid that does not completely overlap with the rounded corner transformation pattern are different, and the value corresponding to the grid that partially overlaps with the rounded corner transformation pattern is the area ratio of the rounded corner transformation pattern in the grid. An initial distribution module is used to determine the mask pixel distribution based on the distribution of the mask target image; The model iteration module is used to input the mask pixel distribution into a preset optical model and obtain the optimal mask pattern distribution through multiple iterations. In each iteration, the optical model simulates the mask pixel distribution using a preset optical kernel function to obtain the corresponding spatial intensity. Then, the spatial intensity is converted into a sigmoid image using a sigmoid function, and the sigmoid image is compared with the mask target image distribution until the difference between the sigmoid image and the mask target image distribution is minimized. When the difference between the sigmoid image and the mask target image distribution is minimized, the mask pixel distribution corresponding to the sigmoid image is taken as the optimal mask pattern distribution. The initial distribution module includes: The non-overlapping grid random substitution unit is used to randomly replace the values ​​in the grids that do not overlap with the rounded corner transformation pattern in the mask target image distribution to obtain the mask pixel distribution.

7. The reverse lithography inversion apparatus as described in claim 6, characterized in that, The model iteration module includes: The loss function unit is used to determine that the difference between the distribution of the sigmoid image and the mask target image is minimized when the function value F of the loss function in the following formula is minimized: ； Where N is the total number of grid cells, T(x, y) is the distribution of the mask target image, and t i IS(x, y) is the value corresponding to the i-th grid in the mask target image distribution, and IS(x, y) is the sigmoid image. i This is the value corresponding to the i-th raster in the sigmoid image.

8. A reverse lithography inversion device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the reverse lithography inversion method as described in any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the reverse lithography inversion method as described in any one of claims 1 to 5.

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