CN115906543B - Parameter acquisition method based on lithography modeling simulation - Google Patents
Parameter acquisition method based on lithography modeling simulation Download PDFInfo
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Abstract
The application relates to the technical field of integrated circuit manufacturing, in particular to a parameter acquisition method based on photoetching modeling simulation, which can solve the problems that a great deal of time is required for acquiring parameters through simulation, a great deal of calculation force is consumed, and engineering requirements are difficult to meet to a certain extent. The method comprises the following steps: defining an objective function according to the light intensity function, performing initial global search on a definition domain space of the objective function, iteratively searching a potential optimal rectangle, and calculating a central point and a corresponding function value of the potential optimal rectangle; inputting a first termination condition, terminating the global search and outputting a first optimal point of the current population; acquiring a new definition domain based on the first optimal point; performing discrete point division on the new definition domain, stepping an objective function, and locally searching for new population points; inputting a second termination condition, and terminating the local search and outputting a plurality of second optimal points when the local search result reaches the second termination condition; outputting and checking parameters corresponding to a plurality of second optimal points.
Description
Technical Field
The application relates to the technical field of integrated circuit manufacturing, in particular to a parameter acquisition method based on lithography modeling simulation.
Background
Photolithography is one of the main processes in the production of planar transistors and integrated circuits. Is a processing technique for opening a mask (e.g., silicon dioxide) on the surface of a semiconductor wafer to effect localized diffusion of impurities. The lithography process employs the creation of a mathematical physical model to simulate the lithography process. When light irradiates on the mask to generate diffraction, diffraction orders are collected by the projection lens and converged on the surface of the photoresist, and the imaging process is an optical process; the image projected on the photoresist excites photochemical reaction, after baking, the photoresist is locally soluble in developer, which is a chemical process, and the model describing the physical and chemical process is the photoresist model.
In some processes for obtaining a model with a high degree of fit to a real lithographic process by calibrating calculated lithographic model parameters, the lithographic process includes a photochemical reaction model and a development model, and components in the photoresist, such as polymers, photosensitive muscles or photoacid generators, solvents, and some special function additives, are simulated by the photochemical reaction model. Taking a chemical amplification gel as an example, a photochemical reaction is initiated upon exposure to light, causing the photoacid generator to decompose to produce an acid. The concentration of the acid produced is related to the intensity of the localized light and the duration of the exposure. This process converts the optical aerial image into a three-dimensional distribution of acid. During post-exposure bake, the acid molecules diffuse in the glue, reacting at some major component of the objective function. Reaching the position of the protecting group on the polymer, causing it to decompose triggering the deprotection reaction and releasing another acid molecule. The polymer after the deprotection reaction can be dissolved in a developer. And the more acid molecules, the more protecting groups of the polymer are broken down. This process converts the three-dimensional distribution of acid into a three-dimensional distribution of solubility; the development rate R of a certain place in the photoresist (defined as the dissolution rate of the photoresist in the developing solution) is simulated by the development model, and the development rate R of the certain place in the photoresist is related to the concentration of the protecting group at the place, and the type of the photoresist also affects the accuracy of the development model.
However, since a large number of parameters are generated in the photochemical reaction model and the development model in the simulation process, the selection of parameters such as different photoresists can influence the final imaging result, and thus has a great influence on the actual production result, a large amount of time is required for obtaining the parameters through simulation, a large amount of calculation force is consumed, and engineering requirements are difficult to meet.
Disclosure of Invention
In order to solve the problems that a large amount of time is required for obtaining parameters through simulation, a large amount of calculation force is consumed, and engineering requirements are difficult to meet, the application provides a parameter obtaining method based on lithography modeling simulation.
Embodiments of the present application are implemented as follows:
the embodiment of the application provides a parameter acquisition method based on lithography modeling simulation, which comprises the following steps:
receiving a light intensity function, defining an objective function according to the light intensity function, performing initial global search on a definition domain space of the objective function, iteratively searching for a potential optimal rectangle, and calculating a central point and a corresponding function value of the potential optimal rectangle, wherein the potential optimal rectangle is a pareto set of the definition domain space set, and the population is a set of the optimal rectangles;
inputting a first termination condition, and terminating the global search and outputting a first optimal point of the current population when the iteration result of the global search reaches the first termination condition;
acquiring a new definition domain based on the first optimal point; performing discrete point division on the new definition domain, stepping an objective function, and locally searching for new population points;
inputting a second termination condition, and terminating the local search and outputting a plurality of second optimal points when the local search result reaches the second termination condition;
outputting and checking parameters corresponding to a plurality of second optimal points.
In some embodiments, in the process of receiving the light intensity function, defining the objective function according to the light intensity function, and performing initial global search on the domain space of the objective function, and iteratively searching for the potentially optimal rectangle, the method further includes:
converting the domain space into a unit hypercube; calculating an objective function obtained according to the light intensity function at the center point of the unit hypercube to obtain a corresponding function value;
dividing the hypercube iteration into smaller hypercubes, and sampling the center points of the smaller hypercubes;
and selecting a potential optimal rectangle in each iteration process, and calculating a value corresponding to the central point of the potential optimal rectangle.
In some embodiments, in the process of receiving the light intensity function, defining the objective function according to the light intensity function, and performing initial global search on the domain space of the objective function, and iteratively searching for the optimal rectangle, the method includes:
defining a rectangular area, calculating corresponding function values, and recording the rectangular area and the corresponding function values as a set S;
searching a pareto set P of the set S, and marking the pareto set P as a potential optimal rectangle;
judging the relation between the element number of the pareto set P and the set population number, and recording the population number as pop; if the number of the elements in the pareto set is larger than that of the pops, selecting the pops in the pareto set for calculation;
selecting pop elements, segmenting a rectangular area, calculating a function value corresponding to the segmented rectangular area, and storing the segmented rectangular area and the corresponding function value into a set S;
when the first termination condition is reached, the center point of the current potential optimal rectangle and the function value are output.
In some embodiments, in determining a relationship between the number of elements of the pareto set P and the number of set populations, the method includes:
when the number of the P elements of the pareto set is smaller than or equal to the set population number, the corresponding element number is increased by adopting a non-dominant sorting mode until the element number is increased to pop.
In some embodiments, the first termination condition is implemented as a first error threshold or a first number of iterations; the second termination condition is implemented as a second error threshold or a second number of iterations;
wherein the first error threshold is greater than the second error threshold.
In some embodiments, in acquiring a new domain based on the first optimal point, the method includes:
based on the first optimal point, giving the up-shift amplitude and the down-shift amplitude of the new definition domain space, and obtaining a new definition domain;
judging whether the new definition domain boundary is coincident with the original definition domain boundary; if the new definition domain contains the original definition domain boundary, selecting the coincident region boundary as the new definition domain boundary; otherwise, the new domain is output.
In some embodiments, in acquiring the new domain based on the first optimal point, the method further comprises:
if the point in the new definition domain calculated is outside the original definition domain, the point outside the original definition domain is removed.
In some embodiments, in locally searching for new population points, the method includes:
calculating a discrete point function value corresponding to the initial population based on the stepped original objective function;
selecting new population points according to covariance and mean value, and moving the population points to the nearest discrete points;
judging whether components corresponding to the discrete points are calculated or not; if the components corresponding to the discrete points are calculated, extracting discrete point function values; if the components corresponding to the discrete points are not calculated, calculating the function values of the discrete points, and storing the discrete point function values into a discrete point data memory;
judging whether a second termination condition of the local search is reached; if the second termination condition is met, outputting function values corresponding to a plurality of optimal points in the current population; if the second termination condition is not met, selecting new population points again according to the covariance and the mean value.
The method has the beneficial effects that after full space searching is carried out in the domain-defined space of the objective function, the whole space searching is carried out again in the local space, and the objective function is subjected to stepped division, so that the whole parameter acquisition process can greatly improve the data utilization rate under the condition of sacrificing the acceptable limited precision; the method can improve the efficiency of acquiring the optimal parameters by using the mode of searching in the whole space and using the local search, and can obviously improve the convergence rate under the same calculation power by adopting the method of recycling and mixing optimization of discrete point data, so that the obtained point is not the global optimal parameter, but the parameter value meeting the engineering requirement can be obtained with smaller calculation cost. Compared with a photoetching model, the method has the advantages that better parameters can be obtained, the simulation is more accurate, and meanwhile, the purpose of meeting the efficiency of actual engineering requirements can be achieved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief description will be given below of the drawings that are needed in the embodiments or the prior art descriptions, it being obvious that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of a method for obtaining parameters based on lithography modeling simulation according to an embodiment of the present application;
FIG. 2 is a flowchart of a global search process of a parameter acquisition method based on lithography modeling simulation according to an embodiment of the present application;
FIG. 3 is a flowchart of a method for obtaining parameters based on lithography modeling simulation according to another embodiment of the present application, in which the number of corresponding elements is increased by adopting a non-dominant ordering manner;
FIG. 4 is a diagram showing a variable CD in a finding objective function of a parameter acquisition method based on lithography modeling simulation according to another embodiment of the present application s Schematic of the process;
FIG. 5 is a diagram showing a variable CD in a finding objective function of a parameter acquisition method based on lithography modeling simulation according to another embodiment of the present application m Schematic of the process;
FIG. 6 is a trend chart showing actual engineering measurements and parameter simulation values of a parameter acquisition method based on lithography modeling simulation according to another embodiment of the present application;
FIG. 7 is a flowchart of an acquisition definition domain of a parameter acquisition method based on lithography modeling simulation according to another embodiment of the present application;
FIG. 8 is a schematic diagram of an iterative search of optimal parameter values based on a parameter acquisition method of lithography modeling simulation according to another embodiment of the present application;
FIG. 9 is a schematic diagram of a local search process of a parameter acquisition method based on lithography modeling simulation according to another embodiment of the present application.
Detailed Description
For purposes of clarity, embodiments and advantages of the present application, the following description will make clear and complete the exemplary embodiments of the present application, with reference to the accompanying drawings in the exemplary embodiments of the present application, it being apparent that the exemplary embodiments described are only some, but not all, of the examples of the present application.
It should be noted that the brief description of the terms in the present application is only for convenience in understanding the embodiments described below, and is not intended to limit the embodiments of the present application. Unless otherwise indicated, these terms should be construed in their ordinary and customary meaning.
The terms "first," second, "" third and the like in the description and in the claims and in the above-described figures are used for distinguishing between similar or similar objects or entities and not necessarily for limiting a particular order or sequence, unless otherwise indicated. It is to be understood that the terms so used are interchangeable under appropriate circumstances.
The terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a product or apparatus that comprises a list of elements is not necessarily limited to all elements explicitly listed, but may include other elements not expressly listed or inherent to such product or apparatus.
As shown in fig. 1, 2, 3, 4, 5 and 6. FIG. 1 is a flow chart of a method for obtaining parameters based on lithography modeling simulation according to an embodiment of the present application; FIG. 2 is a flowchart of a global search process of a parameter acquisition method based on lithography modeling simulation according to an embodiment of the present application; FIG. 3 is a flowchart illustrating a method for obtaining parameters based on lithography modeling simulation according to another embodiment of the present application by increasing the number of corresponding elements by adopting a non-dominant order.
FIG. 4 is a diagram showing a variable CD in a finding objective function of a parameter acquisition method based on lithography modeling simulation according to another embodiment of the present application s Schematic of the process; FIG. 5 is a diagram showing a variable CD in a finding objective function of a parameter acquisition method based on lithography modeling simulation according to another embodiment of the present application m Schematic of the process; FIG. 6 is a schematic illustration of actual engineering measurements and parameters of a method for parameter acquisition based on lithographic modeling simulation according to another embodiment of the present applicationTrend graph of digital simulation values.
In some embodiments, the parameter obtaining method based on lithography modeling simulation provided by the present application includes:
receiving a light intensity function, acquiring an objective function based on the light intensity function, performing initial global search on a definition domain space of the objective function, iteratively searching for a potential optimal rectangle, calculating a central point and a corresponding function value of the potential optimal rectangle, wherein the potential optimal rectangle is a pareto set of the definition domain space set, and the population is a set of the optimal rectangle;
inputting a first termination condition, and terminating the global search and outputting a first optimal point of the current population when the iteration result of the global search reaches the first termination condition;
acquiring a new definition domain based on the first optimal point; performing discrete point division on the new definition domain, stepping an objective function, and locally searching for new population points;
inputting a second termination condition, and terminating the local search and outputting a plurality of second optimal points when the local search times reach the second termination condition;
outputting and checking parameters corresponding to a plurality of second optimal points.
Taking the light intensity function I (x, y) obtained by scalar Hopkins diffraction equation as an example:
I(x,y)＝∫∫∫∫w(x-x' 0 ,y-y' 0 ；x-x” 0 ,y-y” 0 )O(x' 0 ,y' 0 )O * (x” 0 ,y” 0 )dx' 0 dy' 0 dx” 0 dy” 0
w(x' 0 ,y' 0 ；x” 0 ,y” 0 )＝J(x' 0 -x” 0 ,y' 0 -y” 0 )H(x' 0 ,y' 0 )H * (x” 0 ,y” 0 )
is a cross transfer function; j (x' 0 -x” 0 ,y' 0 -y” 0 ) For mutual intensity, H (x, y) is the pupil function and O (x, y) is the mask throw ratio.
The light intensity function I (x, y) is obtained by solving the Hopkins diffraction equation.
And the semi-GA algorithm is adopted in the global search of the definition domain space.
The objective function is expressed as f (x) to calculate a function reduced by the lithography process, and the objective problem can be written as:
wherein I (x, y) is an optical imaging distribution obtained by strict diffraction optical imaging theory.As Gaussian function, the diffusion effect conv (I, G) of the photoresist image is reflected by convolution operation s )。
And according to the fixed threshold S and the photoresist imaging R (x, y), the OPC model engineering obtains a contour corresponding to the photoresist imaging.
R(x,y)＝S
Wherein R (x, y) is a photoresist image reflected on the photoresist, and is physical optics I (x, y), gaussian function, light intensity derivative and coefficient c i And the like, S is a constant.
For profile R (x, y), sim_CD, denoted CD, can be obtained by setting a first termination condition s ；
The straight line in fig. 4 is the threshold condition. Two points of intersection r with curve 1 ,r 2 The distance between them is CD s ；
CD s ＝|r 1 -r 2 |
Measuring exposure data of test patterns in calculating lithographic modeling, i.e. actually measuring the true effective length, denoted as measure_cd, abbreviated as CD m ，CD m Related to the layout of the test pattern, and obtaining CD according to the corresponding position measurement of the layout m 。
The objective function WRMS is defined as:
wherein w is i For a given weight coefficient.
The problem of finding the best parameters translates into finding the smallest WRMS value on each feature region.
The whole space is searched in the domain space of the objective function, and then the whole space is searched again in the local space, and the objective function is subjected to stepped division, so that the whole parameter acquisition process can greatly improve the data utilization rate under the condition of sacrificing the acceptable limited precision; the method can improve the efficiency of acquiring the optimal parameters by using the mode of searching in the whole space and using the local search, and can obviously improve the convergence rate under the same calculation power by adopting the method of recycling and mixing optimization of discrete point data, so that the obtained point is not the global optimal parameter, but the parameter value meeting the engineering requirement can be obtained with smaller calculation cost. Compared with a photoetching model, the method has the advantages that better parameters can be obtained, the simulation is more accurate, and meanwhile, the purpose of meeting the efficiency of actual engineering requirements can be achieved.
In some embodiments, in the process of receiving a light intensity function, defining an objective function according to the light intensity function, and performing initial global search on a domain space of the objective function, and iteratively searching for an optimal rectangle, the parameter obtaining method based on lithography modeling simulation provided by the application further includes:
converting the domain space into a unit hypercube; calculating a light intensity function at the center point of the unit hypercube to obtain a corresponding function value;
dividing the hypercube iteration into smaller hypercubes, and sampling the center points of the smaller hypercubes;
and selecting a potential optimal rectangle in each iteration process, and calculating a value corresponding to the central point of the potential optimal rectangle.
In some embodiments, a light intensity function is received, an objective function is defined according to the light intensity function, and an initial global search is performed on a domain space of the objective function by using a semi-GA algorithm, and in the process of iteratively searching an optimal rectangle, the parameter acquisition method based on lithography modeling simulation provided by the application includes:
defining a rectangular area, calculating corresponding function values, and recording the rectangular area and the corresponding function values as a set S;
searching a pareto set P of the set S, and marking the pareto set P as a potential optimal rectangle;
judging the relation between the number of the elements of the pareto set P and the set population number, wherein the population number is marked as pop, and if the number of the elements of the pareto set P is judged to be larger than the set population number, selecting the pop elements in the pareto set P for calculation;
selecting pop elements, segmenting a rectangular area, calculating a function value corresponding to the segmented rectangular area, and storing the segmented rectangular area and the corresponding function value into a set S;
when the first termination condition is reached, the center point of the current potential optimal rectangle and the function value are output.
In the global searching process of the defined domain interval by using the semi-GA algorithm, each time the newly-increased population always finds a prototype of which the variable value is changed by only 1 dimension in the previous generation population, and the corresponding values of the light intensity I (x, y) and the conv (I, gauss) are not changed by less values of the transformed dimension, so that the speed of acquiring the optimal parameters can be greatly improved by repeatedly using the newly-increased population in the process of not influencing the precision of parameter acquisition, and meanwhile, the global searching stage can be rapidly ended by setting the first termination condition, and the efficiency of searching the first optimal point is improved.
In some embodiments, in determining a relationship between the number of elements of the pareto set P and the number of set populations, the method includes:
when the number of the P elements of the pareto set is smaller than or equal to the set population number, the corresponding element number is increased by adopting a non-dominant sorting mode until the element number is increased to pop.
In some embodiments, the first termination condition is implemented as a first error threshold or a first number of iterations; the second termination condition is implemented as a second error threshold or a second number of iterations;
wherein the first error threshold is greater than the second error threshold.
The first termination condition with larger error is set, so that the efficiency of global search and the data utilization rate can be greatly improved under the condition of sacrificing acceptable limited precision, and meanwhile, the convergence speed of an algorithm is improved, so that the consumed calculation force can be reduced, and the engineering requirement can be conveniently met.
Referring to fig. 7 and 8, fig. 7 is a flowchart of an acquisition definition domain of a parameter acquisition method based on lithography modeling simulation according to another embodiment of the present application; FIG. 8 is a schematic diagram of an iterative search of optimal parameter values based on a parameter acquisition method of lithography modeling simulation according to another embodiment of the present application.
In some embodiments, in acquiring a new domain based on the first optimal point, the parameter acquiring method provided in the present application includes:
based on the first optimal point, giving the up-shift amplitude and the down-shift amplitude of the new definition domain space, and obtaining a new definition domain;
judging whether the new definition domain boundary is coincident with the original definition domain boundary; if the new definition domain contains the original definition domain boundary, selecting the coincident region boundary as the new definition domain boundary; otherwise, the new domain is output.
In some embodiments, in the process of acquiring the new domain based on the first optimal point, the parameter acquiring method provided in the present application further includes:
if the point in the new definition domain calculated is outside the original definition domain, the point outside the original definition domain is removed.
From the first obtained optimum point p, the shift up in each dimension is given by the magnitude [ x ] 1 ,x 2 ...x n ]Downshifting amplitude y 1 ,y 2 ...y n ]If new definition domain [ lb, ub ] is calculated]In which some are outside the original definition p i +x i >ub i Or p j +y j <ub j These points are removed, thereby reducing the probability of large errors in the overall parameter acquisition process due to the use of the new definition fieldThe rate is convenient for the subsequent discrete points to realize convergence in the local search process.
Local searching is performed through a CMAES algorithm. When the one-dimensional normal distribution is adopted, the corresponding normal distribution can be determined only by determining the mean value and the variance; similarly, in a high-dimensional case, the mean and covariance matrices need to be determined; the assumption solution is made to satisfy the normal distribution by using the CMAES algorithm. Therefore, the distribution of the samples in the current generation can be given only by obtaining the mean value and the covariance in the current generation, and the population is selected according to the distribution.
The node distribution position of each dimension is defined according to the actual engineering requirement, taking the kth node of the ith dimension of the variable x as an example, and obtaining discrete point nodes, wherein all the nodes satisfy x for any dimension kiL ≤x≤x kiU All at the point ofThe values of the points are replaced, thereby achieving the objective of the objective function stepping.
Referring to fig. 9, fig. 9 is a schematic diagram illustrating a local search process of a parameter acquisition method based on lithography modeling simulation according to another embodiment of the present application.
In some embodiments, in a process of searching for new population points locally, the parameter obtaining method based on lithography modeling simulation provided by the application includes:
calculating a discrete point function value corresponding to the initial population based on the stepped original objective function;
selecting new population points according to covariance and mean value, and moving the population points to the nearest discrete points;
judging whether components corresponding to the discrete points are calculated or not; if the components corresponding to the discrete points are calculated, extracting discrete point function values; if the components corresponding to the discrete points are not calculated, calculating the function values of the discrete points, and storing the discrete point function values into a discrete point data memory;
judging whether a second termination condition of the local search is reached; if the second termination condition is met, outputting function values corresponding to a plurality of optimal points in the current population; if the second termination condition is not met, selecting new population points again according to the covariance and the mean value.
As the single parameter has limited influence on the calculation result due to the error of the parameter, the effective components of the objective function are conveniently stored and utilized by converting the single parameter into a large number of reusable intermediate functions, and meanwhile, after the objective function is stepped in the local searching process, the data of the intermediate functions can be conveniently reused in the uniform searching process, thereby realizing the purpose of reducing the calculation force consumption.
The method has the advantages that after full space searching is carried out in the domain space of the objective function, searching is carried out again in the local space, and the objective function is subjected to stepped division, so that the whole parameter acquisition process can greatly improve the data utilization rate under the condition of sacrificing the acceptable limited precision; the method can improve the efficiency of acquiring the optimal parameters by using the mode of searching in the whole space and using the local search, and can obviously improve the convergence rate under the same calculation power by adopting the method of recycling and mixing optimization of discrete point data, so that the obtained point is not the global optimal parameter, but the parameter value meeting the engineering requirement can be obtained with smaller calculation cost. Compared with a photoetching model, the method has the advantages that better parameters can be obtained, the simulation is more accurate, and meanwhile, the purpose of meeting the efficiency of actual engineering requirements can be achieved.
The foregoing description, for purposes of explanation, has been presented in conjunction with specific embodiments. However, the above discussion in some examples is not intended to be exhaustive or to limit the embodiments to the precise forms disclosed above. Many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles and the practical application, to thereby enable others skilled in the art to best utilize the embodiments and various embodiments with various modifications as are suited to the particular use contemplated.
Claims (7)
1. A method for obtaining parameters based on lithography modeling simulation, the method comprising:
obtaining a light intensity function by solving a Hopkins diffraction equation;
defining an objective function according to the light intensity function, obtaining a contour corresponding to the photoresist imaging according to a fixed threshold value and the photoresist imaging, performing initial global search on a definition domain space of the objective function, iteratively searching a potential optimal rectangle, calculating a central point and a corresponding function value of the potential optimal rectangle, wherein the potential optimal rectangle is a pareto set of the definition domain space set, and the population is a set of the optimal rectangles, and the fixed threshold value is a constant;
inputting a first termination condition, and terminating the global search and outputting a first optimal point of the current population when the iteration result of the global search reaches the first termination condition;
acquiring a new definition domain based on the first optimal point; performing discrete point division on the new definition domain, stepping an objective function, and locally searching for new population points, wherein the locally searching for the new population points comprises:
calculating a discrete point function value corresponding to the initial population based on the stepped original objective function;
selecting new population points according to covariance and mean value, and moving the population points to the nearest discrete points;
judging whether components corresponding to the discrete points are calculated or not;
if the components corresponding to the discrete points are calculated, extracting discrete point function values;
if the components corresponding to the discrete points are not calculated, calculating the function values of the discrete points, and storing the discrete point function values into a discrete point data memory;
judging whether a second termination condition of the local search is reached; if the second termination condition is met, outputting function values corresponding to a plurality of optimal points in the current population; if the second termination condition is not met, selecting new population points again according to the covariance and the mean value;
inputting a second termination condition, and terminating the local search and outputting a plurality of second optimal points when the local search result reaches the second termination condition;
outputting and checking parameters corresponding to a plurality of second optimal points.
2. The method for obtaining parameters based on lithography modeling simulation according to claim 1, wherein in the process of receiving the light intensity function, defining the objective function according to the light intensity function, and performing an initial global search on a domain space of the objective function, and iteratively searching for a potentially optimal rectangle, the method further comprises:
converting the domain space into a unit hypercube; calculating an objective function obtained according to the light intensity function at the center point of the unit hypercube to obtain a corresponding function value;
dividing the hypercube iteration into smaller hypercubes, and sampling the center points of the smaller hypercubes;
and selecting a potential optimal rectangle in each iteration process, and calculating a value corresponding to the central point of the potential optimal rectangle.
3. The method for obtaining parameters based on lithography modeling simulation according to claim 1, wherein in the process of receiving the light intensity function, defining the objective function according to the light intensity function, and performing an initial global search on a domain space of the objective function, and iteratively searching for an optimal rectangle, the method comprises:
defining a rectangular area, calculating corresponding function values, and recording the rectangular area and the corresponding function values as a set S;
searching a pareto set P of the set S, and marking the pareto set P as a potential optimal rectangle;
judging the relation between the element number of the pareto set P and the set population number, and recording the population number as pop; if the number of the elements in the pareto set is larger than that of the pops, selecting the pops in the pareto set for calculation;
selecting pop elements, segmenting a rectangular area, calculating a function value corresponding to the segmented rectangular area, and storing the segmented rectangular area and the corresponding function value into a set S;
when the first termination condition is reached, the center point of the current potential optimal rectangle and the function value are output.
4. A parameter obtaining method based on lithography modeling simulation according to claim 3, wherein in the process of judging the relationship between the number of elements of the pareto set P and the set population number, the method comprises:
when the number of the P elements of the pareto set is smaller than or equal to the set population number, the corresponding element number is increased by adopting a non-dominant sorting mode until the element number is increased to pop.
5. The method for obtaining parameters based on lithography modeling simulation according to claim 1, wherein the first termination condition is implemented as a first error threshold or a first number of iterations; the second termination condition is implemented as a second error threshold or a second number of iterations;
wherein the first error threshold is greater than the second error threshold.
6. The method for obtaining parameters based on lithography modeling simulation according to claim 1, wherein in the process of obtaining the new definition domain based on the first optimal point, the method comprises:
based on the first optimal point, giving the up-shift amplitude and the down-shift amplitude of the new definition domain space, and obtaining a new definition domain;
judging whether the new definition domain boundary is coincident with the original definition domain boundary;
if the new definition domain contains the original definition domain boundary, selecting the coincident region boundary as the new definition domain boundary;
otherwise, the new domain is output.
7. The method for obtaining parameters based on lithography modeling simulation according to claim 1, wherein in the process of obtaining the new definition domain based on the first optimal point, the method further comprises:
if the point in the new definition domain calculated is outside the original definition domain, the point outside the original definition domain is removed.
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