CN114488719B - OPC method based on three-dimensional feature reinforcement - Google Patents
OPC method based on three-dimensional feature reinforcement Download PDFInfo
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- G03F7/70433—Layout for increasing efficiency or for compensating imaging errors, e.g. layout of exposure fields for reducing focus errors; Use of mask features for increasing efficiency or for compensating imaging errors
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Abstract
The invention belongs to the technical field of optical proximity correction, and discloses an OPC method based on three-dimensional feature reinforcement. The invention solves the problem of large single calculation amount caused by the integral optimization of the existing optimization method through local optimization; the exposure dose distribution is optimized by a local optimization method aiming at three-dimensional height characteristics, so that the problem of missing of the height optimization in the existing proper matrix optimization method is solved; meanwhile, the optimal threshold value can be automatically found, and the problem that the gray gradient threshold value cannot be set manually is solved.
Description
Technical Field
The invention belongs to the technical field of optical proximity correction, and particularly relates to an OPC method based on three-dimensional feature reinforcement.
Background
Photolithography is an important process in many fields such as semiconductor integrated circuits, MEMS, micro-nano optics, and is a processing technique for etching or depositing a mask (e.g., silicon dioxide) on a surface of a semiconductor wafer to perform localized diffusion of impurities. The expensive equipment manufacturing and maintenance costs are about half of the total integrated circuit production costs. Among them, mask fabrication costs are enormous, and maskless lithography techniques such as electron beam lithography, ion beam lithography, and scanning laser lithography are increasingly emerging for cost reduction.
The scanning laser lithography system mainly comprises: a laser light source, a focus modulation system, a stepper system, a scanning system and a photoresist coated silicon wafer. Wherein, the laser beam is focused to the spot size of the target size, and the power of the laser beam is modulated while the surface of the photoresist is scanned (the exposure depth of the gray photoresist is increased along with the nonlinear increase of the exposure energy and is generally similar to an s-shaped curve), then the scanning is performed in the scanning direction with a set step length, and after one period of scanning is finished, the scanning is continued in the reverse direction by stepping once in the stepping direction.
In scanning lithographic imaging, the imaging needs to be optimized due to superposition of energy from other scan areas. In addition, as critical dimensions (Critical dimension, CD for short) continue to decrease, the feature size of scanning laser lithography is limited. In a scanning laser lithography system, the exposure dose at each location can be precisely controlled, so that the superimposed energy can be planned and compensated by simulating and calculating the distribution of the exposure dose, so that imaging resolution enhancement is achieved.
Optical proximity correction techniques (Optical proximity correction, OPC) for scanning laser lithography typically utilize a lithography machine and photoresist parameters to build a nonlinear numerical model, and use a nonlinear programming method or gradient-based optimization algorithm to find the optimal exposure dose distribution, thereby minimizing the differences between the desired pattern and the exposure pattern, mainly including Pattern Errors (PE) and Edge Placement Errors (EPE), and achieving optimization of the output pattern. In 3D thick film lithography, it is important for the optimization of the height, that the 3D structure contains not only dimensional accuracy in the horizontal plane but also dimensional accuracy in the height. The change in height is more susceptible to the influence of diffraction limit, so that the edge is unclear, the size of the excessive edge fillet is large, and finally the product quality is influenced, therefore, the height parameter of the 3D structure often plays a key role in the structure function.
The existing 3D photoetching optimization method based on the vector matrix still has the following defects although the whole calculation cost is reduced: (1) The optimization object is the whole optimization of the full exposure area, and the optimization of the pattern edge is processed together, so that the pattern edge (namely the junction of the target pattern and the etched part during photoetching) is not emphasized, and the optimization speed and the optimization precision cannot be further improved. (2) Lack of local optimization of the 3D lithographic z-axis (i.e., height direction) results in inadequate structural height feature optimization.
Disclosure of Invention
The invention solves the problems of lack of high-level feature optimization and improvement of optimization speed and precision in the prior art by providing the OPC method based on three-dimensional feature reinforcement.
The invention provides an OPC method based on three-dimensional feature reinforcement, which mainly comprises the following steps:
step S1, an optimized photoresist function model is obtained according to photoresist exposure data and a chemical reaction function of photoresist; converting the target pattern into a target pixelated pattern according to the parameters of the photoetching machine;
s2, carrying out edge extraction on the target pixelized pattern under two different gradient thresholds by adopting a Sobel operator to obtain a corresponding edge feature matrix, and taking the numerical value of the edge feature matrix as the numerical value of a target pattern matrix;
step S3, obtaining an initial imaging pattern matrix according to the parameters of the photoetching machine and the optimized photoresist function model;
s4, constructing a first price function and constraint conditions, and updating the values of the exposure dose distribution and the imaging pattern matrix according to the constraint conditions;
step S5, automatically updating the gradient threshold according to the updating judgment conditions;
s6, judging whether a cycle ending condition is met; if not, returning to the step S2; if so, the cycle is ended.
Preferably, the step S1 includes the following substeps:
step S11, establishing a second cost function according to photoresist exposure data obtained through actual measurement and combining a chemical reaction function of the photoresist;
the second cost function is expressed as:
wherein, H represents a second cost function, PR represents photoresist exposure data obtained by actual measurement, and Sig (DEG) represents a chemical reaction function of the photoresist;
step S12, optimizing the photoresist parameters by using an optimization algorithm according to the second cost function until the second cost function is minimum, and obtaining the optimal photoresist parameters, wherein the photoresist parameters comprise etching speed and etching threshold;
step S13, an optimized photoresist function model is obtained based on the optimal etching speed and the optimal etching threshold;
the optimized photoresist function model is expressed as:
wherein a represents an optimal etching rate, t r Representing an optimal etching threshold;
and S14, inputting parameters of the photoetching machine, and converting the target pattern into a target pixelated pattern.
Preferably, before the edge extraction by using the Sobel operator, the method further includes: and carrying out Gaussian smoothing processing on the target pixelated pattern.
Preferably, the gaussian smoothing convolution kernel is as follows:
| 0.075 | 0.124 | 0.075 |
| 0.124 | 0.204 | 0.124 |
| 0.075 | 0.124 | 0.075 |
and performing convolution operation on the Gaussian smoothing convolution kernel and the target pixelized pattern to realize Gaussian smoothing processing on the target pixelized pattern.
Preferably, the step S2 includes the following substeps:
s21, performing convolution operation on the Sobel operator and the target pixelized pattern to obtain a pattern gradient change diagram; the Sobel operator convolution kernel is as follows:
x direction:
y direction:
G＝|Gx|+|Gy|
wherein Gx represents the gradient size of each pixel in the x-axis direction obtained by convolving the target pixelized pattern with the Sobel operator convolution kernel in the x-direction, and Gy represents the gradient size of each pixel in the y-axis direction obtained by convolving the target pixelized pattern with the Sobel operator convolution kernel in the y-direction; g represents the height gradient of each pixel, and the value of G is the sum of absolute values of gradient values of each pixel in the x-axis direction and the y-axis direction;
step S22, based on the pattern gradient change diagram, according to two set gradient thresholds beta 1 、β 2 Respectively extracting two gradient distribution matrixes larger than gradient threshold as an edge feature matrix S z1 、S z2 ；
For each gradient threshold, if S ij More than or equal to beta, S Zij ＝S ij Otherwise, S Zij ＝0；
Wherein S is ij Coordinate values of the ith row and the jth column pixel points in the pattern gradient change diagram are represented by S zij Coordinate values of pixel points in the ith row and the jth column in the edge feature matrix are represented;
the value of the edge feature matrix is taken as the value of the target pattern matrix Z (x, y).
Preferably, the step S3 includes the following substeps:
step S31, according to the parameters of the photoetching machine, carrying out pixelation processing on the target pattern to obtain an exposure dose distribution matrix:
wherein E (x, y) represents an exposure dose distribution matrix, the initial value of the exposure dose distribution matrix is from the numerical value of the position of the corresponding pixel point of the target pattern matrix Z (x, y), and (x, y) represents the position coordinate of one exposure point, the position coordinate of a single exposure point is equal to the position coordinate of the corresponding pixel point, and θ represents an unconstrained optimization variable;
step S32, gaussian beam matrix is obtained:
wherein B (x, y) represents Gaussian beam matrix, P represents overall exposure power, ω 0 Is the radius of the laser spot at the focal plane;
step S33, obtaining an exposure energy distribution matrix according to the exposure dose distribution matrix and the Gaussian beam matrix:
wherein D (x, y) represents an exposure energy distribution matrix,representing a convolution symbol;
step S34, obtaining the initial imaging pattern matrix according to the exposure energy distribution matrix and the optimized photoresist function model:
wherein,representing an initial imaging pattern matrix.
Preferably, in the step S4, a first cost function and a constraint condition are constructed in combination with an optimization algorithm, and iterative optimization is performed on the exposure dose distribution matrix and the imaging pattern matrix:
wherein F is Z Representing pattern errors after imaging the photoresist, F E Representing the total output dose of the system, F representing the first cost function, Z (x, y) representing the target pattern matrix,representing an imaging pattern matrix, E (x, y) representing an exposure dose distribution matrix;
iterative optimization is carried out on an exposure dose distribution matrix:
where s represents the updated step size in the optimization algorithm.
Preferably, in step S5, the update determination condition packageIncludes a first condition and a second condition; the first condition is F 2 -F 1 > 0, said second condition is F 2 -F 1 ≤0；F 1 Expressed at gradient threshold beta 1 Next, a first cost function is constructed by using the edge feature matrix, F 2 Expressed at gradient threshold beta 2 A first cost function constructed by using the edge feature matrix;
if the first condition is met, automatically updating the gradient threshold according to the following principle:
β 2 ＝β 1 ，β 1 ＝β 1 -α(F 2 -F 1 )
if the second condition is met, automatically updating the gradient threshold according to the following principle:
β 1 ＝β 2 ，β 2 ＝β 2 +α(F 2 -F 1 )
where α represents the set step size.
Preferably, in step S6, if the cycle is completed, β is taken 1 And beta 2 Taking the smaller numerical value of the optimal gradient threshold as an optimal edge characteristic matrix, taking the edge characteristic matrix corresponding to the optimal gradient threshold as an optimal edge characteristic matrix, and taking the exposure dose distribution matrix and the exposure energy distribution matrix corresponding to the optimal edge characteristic matrix as global optimal distribution.
Preferably, in step S6, the cycle end condition is that the number of times of optimization is reached or the value of the first cost function is smaller than an optimization threshold.
One or more technical schemes provided by the invention have at least the following technical effects or advantages:
in the invention, an optimized photoresist function model is obtained according to photoresist exposure data and a chemical reaction function of the photoresist; converting the target pattern into a target pixelated pattern according to the parameters of the photoetching machine; performing edge extraction on the target pixelized pattern under two different gradient thresholds by adopting a Sobel operator to obtain a corresponding edge feature matrix, and taking the numerical value of the edge feature matrix as the numerical value of the target pattern matrix; obtaining an initial imaging pattern matrix according to the parameters of the photoetching machine and the optimized photoresist function model; constructing a first cost function and constraint conditions, and updating the values of the exposure dose distribution and the imaging pattern matrix according to the constraint conditions; automatically updating the gradient threshold according to the updating judgment conditions; after updating, judging whether the cycle ending condition is met; if not, returning to the step of edge extraction; if so, the cycle is ended. The traditional exposure dose distribution solving is to solve the whole pattern, namely the optimization object is to optimize the whole exposure area, so that the single calculation amount is large and the optimization speed is low. According to the method, the edge of the feature is solved, the Sobel operator is adopted to extract the edge matrix, and the edge matrix is optimized in a targeted mode, so that the optimization effect and the optimization speed can be improved. The invention optimizes the three-dimensional structure of the exposure dose distribution based on the Sobel operator edge extraction height optimization method, and solves the problem of missing height optimization in the existing proper matrix optimization method. The invention performs edge extraction under two different gradient thresholds, automatically updates the gradient threshold according to the updating judgment conditions, and further obtains an optimal gradient threshold, namely the invention provides a method for automatically searching the optimal threshold, and solves the problem that the gray gradient threshold cannot be set to an optimal value artificially.
Drawings
FIG. 1 is a flowchart of an OPC method based on three-dimensional feature enhancement according to an embodiment of the present invention.
Detailed Description
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
The embodiment provides an OPC method based on three-dimensional feature enhancement, referring to fig. 1, comprising the following steps:
step S1, an optimized photoresist function model is obtained according to photoresist exposure data and a chemical reaction function of photoresist; the target pattern is converted into a target pixelated pattern according to the lithography machine parameters.
Wherein, the step S1 comprises the following substeps:
step S11, establishing a second cost function according to photoresist exposure data obtained through actual measurement and combining a chemical reaction function of the photoresist;
the second cost function is expressed as:
wherein H represents a second cost function, PR represents photoresist exposure data obtained by actual measurement, and the photoresist exposure data comprises exposure energy and film retention rate; sig (·) represents the chemical reaction function of the photoresist.
And step S12, optimizing the photoresist parameters by using an optimization algorithm according to the second cost function until the second cost function is minimum, and obtaining the optimal photoresist parameters, wherein the photoresist parameters comprise etching speed and etching threshold.
Step S13, an optimized photoresist function model is obtained based on the optimal etching speed and the optimal etching threshold;
the optimized photoresist function model is expressed as:
wherein a represents an optimal etching rate, t r Indicating an optimal etch threshold.
And S14, inputting parameters of the photoetching machine, and converting the target pattern into a target pixelated pattern.
Among other parameters, the lithography machine parameters include resolution, scan speed, step speed, light source size, etc.
And S2, carrying out edge extraction on the target pixelized pattern under two different gradient thresholds by adopting a Sobel operator to obtain a corresponding edge feature matrix, and taking the numerical value of the edge feature matrix as the numerical value of the target pattern matrix.
Wherein, the step S2 comprises the following substeps:
s21, performing convolution operation on the Sobel operator and the target pixelized pattern to obtain a pattern gradient change diagram; the Sobel operator convolution kernel is as follows:
x direction:
y direction:
G＝|Gx|+|Gy|
wherein Gx represents the gradient size of each pixel in the x-axis direction obtained by convolving the target pixelized pattern with the Sobel operator convolution kernel in the x-direction, and Gy represents the gradient size of each pixel in the y-axis direction obtained by convolving the target pixelized pattern with the Sobel operator convolution kernel in the y-direction; g represents the height gradient magnitude of each pixel, and the value of G is the sum of absolute values of gradient values of each pixel in the x-axis and y-axis directions.
Step S22, based on the pattern gradient change diagram, according to two set gradient thresholds beta 1 、β 2 Respectively extracting two gradient distribution matrixes larger than gradient threshold as an edge feature matrix S z1 、S z2 ；
For each gradient threshold, if S ij More than or equal to beta, S Zij ＝S ij Otherwise, S Zij ＝0；
Wherein S is ij Coordinate values of the ith row and the jth column pixel points in the pattern gradient change diagram are represented by S zij Coordinate values of pixel points in the ith row and the jth column in the edge feature matrix are represented;
the value of the edge feature matrix is taken as the value of the target pattern matrix Z (x, y).
And step S3, obtaining an initial imaging pattern matrix according to the parameters of the photoetching machine and the optimized photoresist function model.
Wherein, the step S3 comprises the following substeps:
step S31, according to the parameters of the photoetching machine, carrying out pixelation processing on the target pattern to obtain an exposure dose distribution matrix:
wherein E (x, y) represents an exposure dose distribution matrix, an initial value of the exposure dose distribution matrix is derived from a numerical value of a position of a corresponding pixel point of the target pattern matrix Z (x, y), (x, y) represents a position coordinate of one exposure point, a position coordinate of a single exposure point is equal to a position coordinate of a corresponding pixel point, and θ represents an unconstrained optimization variable.
Step S32, gaussian beam matrix is obtained:
wherein B (x, y) represents Gaussian beam matrix, P represents overall exposure power, ω 0 Is the radius of the laser spot at the focal plane; the mathematical form of the gaussian beam matrix is related to the parameters of the lithography machine, and since the parameters of the lithography machine are fixed, the values of the gaussian beam matrix are fixed in the next step.
Step S33, obtaining an exposure energy distribution matrix according to the exposure dose distribution matrix and the Gaussian beam matrix:
wherein D (x, y) represents an exposure energy distribution matrix,representing a convolution symbol;
step S34, obtaining the initial imaging pattern matrix according to the exposure energy distribution matrix and the optimized photoresist function model:
wherein,representing an initial imaging pattern matrix.
And S4, constructing a first cost function and a constraint condition, and updating the values of the exposure dose distribution and the imaging pattern matrix according to the constraint condition.
Specifically, a first cost function and constraint conditions are constructed by combining an optimization algorithm, and iterative optimization is carried out on an exposure dose distribution matrix and an imaging pattern matrix:
wherein F is Z Representing pattern errors after imaging the photoresist, F E Representing the total output dose of the system, F representing the first cost function, Z (x, y) representing the target pattern matrix,representing an imaging pattern matrix, E (x, y) representing an exposure dose distribution matrix.
Iterative optimization is carried out on an exposure dose distribution matrix:
where s represents the updated step size in the optimization algorithm.
And S5, automatically updating the gradient threshold according to the updating judgment conditions.
Wherein the update judgment conditions include a first condition and a second condition; the first condition is F 2 -F 1 > 0, said second condition is F 2 -F 1 ≤0；F 1 Expressed at gradient threshold beta 1 Next, a first cost function is constructed by using the edge feature matrix, F 2 Expressed at gradient threshold beta 2 The first cost function is built by using the edge feature matrix.
If the first condition is met, automatically updating the gradient threshold according to the following principle:
β 2 ＝β 1 ，β 1 ＝β 1 -α(F 2 -F 1 )
if the second condition is met, automatically updating the gradient threshold according to the following principle:
β 1 ＝β 2 ，β 2 ＝β 2 +α(F 2 -F 1 )
where α represents the set step size.
S6, judging whether a cycle ending condition is met; if not, returning to the step S2; if so, the cycle is ended.
Wherein, if the cycle is ended, beta is taken 1 And beta 2 Taking the smaller numerical value of the optimal gradient threshold as an optimal edge characteristic matrix, taking the edge characteristic matrix corresponding to the optimal gradient threshold as an optimal edge characteristic matrix, and taking the exposure dose distribution matrix and the exposure energy distribution matrix corresponding to the optimal edge characteristic matrix as global optimal distribution.
The cycle end condition is that the number of optimizations is reached or the value of the first cost function is less than an optimization threshold.
In addition, in a preferred scheme, before the edge extraction by adopting the Sobel operator, the method further comprises the following steps: and carrying out Gaussian smoothing processing on the target pixelated pattern.
Specifically, the gaussian smoothing convolution kernel is as follows:
| 0.075 | 0.124 | 0.075 |
| 0.124 | 0.204 | 0.124 |
| 0.075 | 0.124 | 0.075 |
and performing convolution operation on the Gaussian smoothing convolution kernel and the target pixelized pattern to realize Gaussian smoothing processing on the target pixelized pattern.
According to the method, the Sobel operator is adopted to process the gray level image, after the image edge is extracted, the inverse photoetching calculation is carried out on the image edge, so that the exposure dose distribution of the image edge is obtained, and the calculation complexity of the solution is rapidly increased along with the increase of the number of pixel points. In addition, the magnitude of the gradient threshold influences the magnitude of the cost function, the gradient thresholds suitable for different patterns are not necessarily the same, the suitable gradient threshold cannot be determined in advance, and the method adopts an automatic optimizing algorithm to solve the problem that the threshold is not set by people. Therefore, the technical scheme provided by the invention has the advantages of rapidness, high efficiency and simplicity in operation, and can greatly improve the speed of reverse photoetching.
The OPC method based on three-dimensional feature reinforcement provided by the embodiment of the invention at least comprises the following technical effects:
the invention solves the problem of large single calculation amount caused by the integral optimization of the existing optimization method through local optimization; the local optimization method aiming at the three-dimensional height features is provided for optimizing the exposure dose distribution, so that the problem of missing of the height optimization in the conventional proper matrix optimization method is solved, meanwhile, the optimal threshold can be automatically found, and the problem that the gray gradient threshold cannot be set to the optimal value manually is solved.
Finally, it should be noted that the above-mentioned embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same, and although the present invention has been described in detail with reference to examples, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention, and all such modifications and equivalents are intended to be encompassed in the scope of the claims of the present invention.
Claims (6)
1. An OPC method based on three-dimensional feature reinforcement is characterized by comprising the following steps:
step S1, an optimized photoresist function model is obtained according to photoresist exposure data and a chemical reaction function of photoresist; converting the target pattern into a target pixelated pattern according to the parameters of the photoetching machine;
said step S1 comprises the sub-steps of:
step S11, establishing a second cost function according to photoresist exposure data obtained through actual measurement and combining a chemical reaction function of the photoresist;
the second cost function is expressed as:
wherein, H represents a second cost function, PR represents photoresist exposure data obtained by actual measurement, and Sig (DEG) represents a chemical reaction function of the photoresist;
step S12, optimizing the photoresist parameters by using an optimization algorithm according to the second cost function until the second cost function is minimum, and obtaining the optimal photoresist parameters, wherein the photoresist parameters comprise etching speed and etching threshold;
step S13, an optimized photoresist function model is obtained based on the optimal etching speed and the optimal etching threshold;
the optimized photoresist function model is expressed as:
wherein a represents an optimal etching rate, t r Representing an optimal etching threshold;
s14, inputting parameters of a photoetching machine, and converting a target pattern into a target pixelated pattern;
s2, carrying out edge extraction on the target pixelized pattern under two different gradient thresholds by adopting a Sobel operator to obtain a corresponding edge feature matrix, and taking the numerical value of the edge feature matrix as the numerical value of a target pattern matrix;
said step S2 comprises the sub-steps of:
s21, performing convolution operation on the Sobel operator and the target pixelized pattern to obtain a pattern gradient change diagram; the Sobel operator convolution kernel is as follows:
x direction:
y direction:
G＝|Gx|+|Gy|
wherein Gx represents the gradient size of each pixel in the x-axis direction obtained by convolving the target pixelized pattern with the Sobel operator convolution kernel in the x-direction, and Gy represents the gradient size of each pixel in the y-axis direction obtained by convolving the target pixelized pattern with the Sobel operator convolution kernel in the y-direction; g represents the height gradient of each pixel, and the value of G is the sum of absolute values of gradient values of each pixel in the x-axis direction and the y-axis direction;
step S22, based on the pattern gradient change diagram, according to two set gradient thresholds beta 1 、β 2 Respectively extracting two gradient distribution matrixes larger than gradient threshold as an edge feature matrix S z1 、S z2 ；
For each gradient threshold, if S ij More than or equal to beta, S Zij ＝S ij Otherwise, S Zij ＝0；
Wherein S is ij Coordinate values of the ith row and the jth column pixel points in the pattern gradient change diagram are represented by S zij Coordinate values of pixel points in the ith row and the jth column in the edge feature matrix are represented;
taking the numerical value of the edge characteristic matrix as the numerical value of a target pattern matrix Z (x, y);
step S3, obtaining an initial imaging pattern matrix according to the parameters of the photoetching machine and the optimized photoresist function model;
said step S3 comprises the sub-steps of:
step S31, according to the parameters of the photoetching machine, carrying out pixelation processing on the target pattern to obtain an exposure dose distribution matrix:
wherein E (x, y) represents an exposure dose distribution matrix, the initial value of the exposure dose distribution matrix is from the numerical value of the position of the corresponding pixel point of the target pattern matrix Z (x, y), and (x, y) represents the position coordinate of one exposure point, the position coordinate of a single exposure point is equal to the position coordinate of the corresponding pixel point, and θ represents an unconstrained optimization variable;
step S32, gaussian beam matrix is obtained:
wherein B (x, y) represents Gaussian beam matrix, P represents overall exposure power, ω 0 Is the radius of the laser spot at the focal plane;
step S33, obtaining an exposure energy distribution matrix according to the exposure dose distribution matrix and the Gaussian beam matrix:
wherein D (x, y) represents an exposure energy distribution matrix,representing a convolution symbol;
step S34, obtaining the initial imaging pattern matrix according to the exposure energy distribution matrix and the optimized photoresist function model:
wherein,representing an initial imaging pattern matrix;
s4, constructing a first price function and constraint conditions, and updating the values of the exposure dose distribution and the imaging pattern matrix according to the constraint conditions;
in the step S4, a first cost function and constraint conditions are constructed by combining an optimization algorithm, and iterative optimization is performed on an exposure dose distribution matrix and an imaging pattern matrix:
wherein F is Z Representing pattern errors after imaging the photoresist, F E Representing the total output dose of the system, F representing the first cost function, Z (x, y) representing the target pattern matrix,representing an imaging pattern matrix, E (x, y) representing an exposure dose distribution matrix;
iterative optimization is carried out on an exposure dose distribution matrix:
wherein s represents the updated step size in the optimization algorithm;
step S5, automatically updating the gradient threshold according to the updating judgment conditions;
s6, judging whether a cycle ending condition is met; if not, returning to the step S2; if so, the cycle is ended.
2. The three-dimensional feature-based reinforcement OPC method of claim 1 further comprising, prior to edge extraction using the Sobel operator: and carrying out Gaussian smoothing processing on the target pixelated pattern.
3. The OPC method based on three-dimensional feature enhancement of claim 2, wherein the gaussian smoothing convolution kernel is as follows:
and performing convolution operation on the Gaussian smoothing convolution kernel and the target pixelized pattern to realize Gaussian smoothing processing on the target pixelized pattern.
4. The OPC method based on three-dimensional feature enhancement of claim 1, wherein in step S5, the update judgment conditions include a first condition and a second condition; the first condition is F 2 -F 1 > 0, said second condition is F 2 -F 1 ≤0；F 1 Expressed at gradient threshold beta 1 First generation price built by edge feature matrixFunction F 2 Expressed at gradient threshold beta 2 A first cost function constructed by using the edge feature matrix;
if the first condition is met, automatically updating the gradient threshold according to the following principle:
β 2 ＝β 1 ，β 1 ＝β 1 -α(F 2 -F 1 )
if the second condition is met, automatically updating the gradient threshold according to the following principle:
β 1 ＝β 2 ，β 2 ＝β 2 +α(F 2 -F 1 )
where α represents the set step size.
5. The method of OPC based on three-dimensional feature enhancement as claimed in claim 4, wherein in step S6, if the cycle is ended, β is taken out 1 And beta 2 Taking the smaller numerical value of the optimal gradient threshold as an optimal edge characteristic matrix, taking the edge characteristic matrix corresponding to the optimal gradient threshold as an optimal edge characteristic matrix, and taking the exposure dose distribution matrix and the exposure energy distribution matrix corresponding to the optimal edge characteristic matrix as global optimal distribution.
6. The OPC method based on three-dimensional feature enhancement of claim 1, wherein in step S6, the cycle end condition is that the number of optimizations is reached or the value of the first cost function is smaller than an optimization threshold.
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