Photoresist shrinkage amount prediction method and system, electronic device

Abstract

The application discloses a photoresist shrinkage prediction method, which comprises the following steps: obtaining input data, wherein the input data comprises CD-SEM measurement conditions, CD-SEM measured photoresist thickness and photoresist width original values; inputting the obtained input data into a pre-trained prediction model, and obtaining an output result of the prediction model, wherein the prediction model is trained based on a spider optimization algorithm and a support vector regression algorithm, and the output result is the shrinkage of the photoresist under CD-SEM measurement. The method of the application uses support vector regression (SVR) to construct a photoresist shrinkage prediction model, and performs parameter optimization on the support vector regression model through a spider optimization algorithm, so as to improve the regression performance of SVR, and thus the prediction model generated through training can achieve more accurate prediction results in predicting photoresist shrinkage values, which helps to improve the measurement accuracy of CD-SEM.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial intelligence, and particularly relates to a photoresist shrinkage amount prediction method and system, an electronic device and a storage medium. BACKGROUND

[0002] With the development of semiconductor manufacturing technology, the feature size of integrated circuits is continuously reduced, and the control accuracy of the photolithography process is increasingly improved. The critical dimension scanning electron microscope (CD-SEM) is an important measurement tool in the photolithography process. However, with the reduction of the feature size, the measurement accuracy of the CD-SEM is challenged by the shrinkage of the photoresist under electron beam irradiation. Existing studies show that the photoresist shrinkage is closely related to factors such as the electron beam dose, the acceleration voltage, the chemical formula of the photoresist, and the feature size structure, as shown in FIG. 1. The photoresist with a width of CD before electron beam irradiation is changed to CD' after the irradiation of the CD-SEM electron beam, resulting in a shrinkage of ΔCD (as shown in the gray area on the right side of FIG. 1). Figure 1 Figure 1

[0003] ​​In order to reduce the influence of the photoresist shrinkage effect caused by CD-SEM electron beam irradiation on the measurement accuracy of the CD-SEM, two mainstream solutions are proposed in the prior art. One is a mathematical model-based method. For example, Bunday et al. proposed a 1-D and 2-D photoresist shrinkage model, respectively, to fit the actual shrinkage data. Rana et al. proposed to apply an artificial neural network (ANN) to the prediction of the photoresist shrinkage phenomenon, to predict the unshrinkage value CD through supervised learning. Ohashi et al. proposed a photoresist shrinkage correction model based on electron beam energy, to recover the original unshrinkage profile from the two-dimensional pattern profile extracted from the CD-SEM image. Li et al. proposed an across sampling method, combined with a shrinkage model to deduce the CD difference. Sugie et al. proposed an improvement measure by improving the scanning strategy and frame integration algorithm, and verified the application effect of the measure on EUV photoresist through experiments. Wei et al. verified the effectiveness of the profile averaging method in relieving the pattern shrinkage in the SEM imaging process. The other method is to obtain SEM images with a lower acceleration voltage or a smaller number of scanning frames to reduce the shrinkage amount caused by electron beam irradiation. However, the correction efficiency and accuracy of the former method still need to be improved, and the operation mode of the latter method inevitably introduces image noise, reducing the measurement accuracy. SUMMARY

[0004] Embodiments of the present application provide a new solution to solve the problem of CD-SEM metrology accuracy caused by the photoresist shrinkage effect in the prior art.

[0005] According to a first aspect of the present application, a photoresist shrinkage amount prediction method is provided, comprising:

[0006] obtaining input data, wherein the input data includes CD-SEM measurement conditions, CD-SEM measured photoresist thickness and photoresist width original value;

[0007] inputting the obtained input data into a pre-trained prediction model, and obtaining an output result of the prediction model, wherein the prediction model is trained based on an improved spider optimization algorithm and a support vector regression algorithm, and the output result is a shrinkage amount of the photoresist caused by CD-SEM measurement.

[0008] According to a second aspect of the present application, a photoresist shrinkage amount prediction system is provided, comprising:

[0009] The shrinkage prediction module is configured to input data obtained from a critical dimension scanning electron microscope into a pre-trained prediction model, and obtain an output result of the prediction model, wherein the prediction model is trained based on an improved spider optimization algorithm and a support vector regression algorithm, the output result is a shrinkage of photoresist caused by CD-SEM measurement, and the input data includes CD-SEM measurement conditions, photoresist thickness and photoresist width original values measured by CD-SEM.

[0010] According to a third aspect of the present application, an electronic device is provided, comprising at least one processor, and a memory connected to the at least one processor in communication, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the method of the first aspect.

[0011] According to a fourth aspect of the present application, a storage medium is provided, which stores a computer program executable by a processor to implement the steps of the method of the first aspect.

[0012] The method of the embodiments of the present application uses support vector regression (SVR) to construct a photoresist shrinkage prediction model, and optimizes parameters of the support vector regression (SVR) model through an improved spider optimization algorithm, thereby improving the regression performance of SVR, so that the trained prediction model can achieve more accurate prediction results in predicting photoresist shrinkage values, which helps to improve the measurement accuracy of CD-SEM. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0014] Figure 1 A schematic diagram of the photoresist shrinkage effect caused by CD-SEM measurement in the prior art is shown;

[0015] Figure 2 A flowchart of a photoresist shrinkage prediction method according to an embodiment of the present application is shown schematically;

[0016] Figure 3 A schematic diagram of the principle of SVR loss calculation in the prior art is shown;

[0017] Figure 4 A flowchart of a method for training a prediction model according to an embodiment of the present application is shown schematically.

[0018] Figure 5 A flow chart of a method for training an initial model based on SVR using an improved spider optimization algorithm is shown schematically in an embodiment of the present application;

[0019] Figure 6 A comparative effect diagram of the MSE convergence curves of ISWO and other six optimization algorithms for training support vector regression models is shown schematically;

[0020] Figure 7 The optimal hyperparameter combinations obtained by ISWO and other six optimization algorithms and their prediction performance indicators on the validation set are shown schematically;

[0021] Figure 8 A radar comparison diagram based on four indicators of ISWO and other six optimization algorithms is shown schematically;

[0022] Figure 9 A scatter plot showing the predicted values and actual values of the photoresist shrinkage amount by the ISWO-SVR model is shown schematically;

[0023] Figure 10 A schematic diagram of the hardware structure of an electronic device in an embodiment of the present application is shown schematically;

[0024] Figure 11 A schematic diagram of the hardware structure of an electronic device in an embodiment of the present application is shown schematically;

[0025] Figure 12 A schematic diagram of the hardware structure of an electronic device in another embodiment of the present application is shown schematically. DETAILED DESCRIPTION

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0027] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0028] In the description of the application, it needs to be understood that if the terms "center", "middle", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "axial", "radial", "circumferential" and the like indicate the orientation or positional relationship shown in the drawings, which is only for the convenience of describing the application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the application. The features defined as "first", "second" are used to distinguish the feature names, not to have special meanings, and in addition, the features defined as "first", "second" can explicitly or implicitly include one or more of the features. In the description of the application, unless otherwise stated, the meaning of "a plurality of" is two or more.

[0029] In the description of the application, it needs to be explained that unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connecting" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the application can be understood according to the specific circumstances.

[0030] It also needs to be explained that in this paper, the terms "including", "containing", not only include those elements, but also include other elements not explicitly listed, or include elements inherent to such processes, methods, articles or devices. Without more limitations, the elements defined by the sentence "including" do not exclude the presence of other identical elements in the process, method, article or device including the elements. The terms used in this paper are generally the terms commonly used by those skilled in the art, and if they are inconsistent with commonly used terms, the terms in this paper shall prevail.

[0031] In order to make the purpose, technical scheme and advantages of the embodiments of the application more clear, the technical scheme in the embodiments of the application will be described clearly and completely in the following with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are part of the embodiments of the application, not all embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0032] The application will be described in further detail below with reference to the accompanying drawings.

[0033] The photoresist shrinkage prediction method in the embodiment of the present application can be applied in any mobile terminal device, and can also be applied on a server and a mobile electronic device, so that the device or server or terminal can use the scheme of the embodiment of the present application to predict the shrinkage of the photoresist caused by CD-SEM measurement, and the prediction accuracy is high, which helps to improve the measurement accuracy of CD-SEM.

[0034] Figure 2 The flow of the photoresist shrinkage prediction method of an embodiment of the present application is schematically shown, and the execution subject of the method can be a processor of a computer, a cloud server, a server in a cluster, a mobile terminal device, etc., which is not limited in the embodiment of the present application. As shown in Figure 2 , it includes:

[0035] Operation S1, obtaining input data, wherein the input data includes CD-SEM measurement conditions, CD-SEM measured photoresist thickness and photoresist width original value;

[0036] Operation S2, inputting the obtained input data into a pre-trained prediction model, and obtaining the output result of the prediction model, wherein the prediction model is trained based on the bumblebee optimization algorithm and the support vector regression algorithm, and the output result is the shrinkage of the photoresist caused by CD-SEM measurement.

[0037] Preferably, the CD-SEM measurement condition can be the acceleration voltage and exposure time set by the corresponding CD-SEM device when performing measurement, and the photoresist thickness and photoresist width original value are the measurement results output by the corresponding CD-SEM device. In operation S1, the input data can be obtained from the CD-SEM device which is currently to be measured and corrected. By inputting the measurement results and measurement conditions of a certain CD-SEM device into the trained prediction model, the shrinkage of the photoresist caused by the measurement of the CD-SEM device can be predicted, and based on the predicted shrinkage, the measurement results output by the CD-SEM device can be corrected, so as to avoid the measurement error caused by the shrinkage of the photoresist and improve the measurement accuracy of the CD-SEM.

[0038] In operation S2, the prediction model is trained by using the bumblebee optimization algorithm and the support vector regression algorithm. The support vector regression algorithm (SVR) is proposed by Vladimir Vapnik et al. based on the support vector machine (SVM) algorithm, which is used to solve the regression problem. Unlike traditional regression algorithms, SVR does not use the method of directly calculating the difference between the true value and the predicted value to calculate the loss, but sets the hyperparameter tolerance deviation ε to calculate the loss, such as Figure 3The loss is only calculated for the out-of-bag sample points in the ε-interval. The algorithmic process of SVR is as follows:

[0039] Suppose the training sample set is , The purpose of SVR is to obtain a regression model as shown in equation (1) so that the real value and the predicted value can be as close as possible.

[0040] (1)

[0041] where w is the weight vector, x is the input feature vector, and b is the bias term.

[0042] Therefore, the problem solved by the SVR algorithm can be converted into a minimum value solving problem as shown in equation (2):

[0043] (2)

[0044] where C in equation (2) is a penalty factor that can be used to control the balance between the regularization term and the error term, and is an important hyperparameter in the SVR algorithm; is an ε-insensitive loss function, the expression of which is shown in equation (3):

[0045] (3)

[0046] That is, when the sample point is within the ε-interval, the error between the real value and the predicted value is not calculated; when the sample point falls outside the ε-interval, the error between the real value and the predicted value that exceeds the interval is calculated.

[0047] In order to more conveniently solve the above minimum value solving problem, slack variables and are introduced, and the above problem is further converted into:

[0048] (4)

[0049]

[0050] SVR solves the above optimization problem through the Lagrange function expressed in equation (5)

[0051] (5)

[0052] where , , , is a Lagrange multiplier , , , ).

[0053] The partial derivatives of equation (5) with respect to , , , are solved respectively, and the partial derivatives are set to 0 to obtain:

[0054] (6)

[0055] (7)

[0056] (8)

[0057] (9)

[0058] Equation (6), (7), (8), and (9) are substituted into equation (5) to obtain the dual problem of SVR as:

[0059] (10)

[0060] Equation (6) is substituted into equation (1) to obtain the solution of the SVR problem as:

[0061] (11)

[0062] In order to solve the regression problem of SVR for nonlinear data, in the embodiment of the present application, x is preferably mapped to a high-dimensional feature vector , and equation (6) can be written as:

[0063] (12)

[0064] At this time, the solution of SVR can be written as:

[0065] (13)

[0066] where is a kernel function, and preferably, the kernel function selected in the present application is a Gaussian radial basis function, which is defined as follows:

[0067] (14)

[0068] where σ is the bandwidth parameter of the Gaussian radial basis function, and is the hyperparameter of the kernel function.

[0069] Spider Wasp Optimizer (SWO) is a meta-heuristic intelligent optimization algorithm based on the behavior of spider wasps in nature. By simulating the hunting, nesting and mating behavior of spider wasps, it can find the optimal solution in optimization problems. It has the characteristics of fast search speed and high solution accuracy. The algorithm process includes population initialization, hunting and nesting behavior, tracking and escape stage, nesting stage, mating behavior, and population reduction and memory storage. The algorithm process of Spider Wasp Optimization Algorithm will be briefly described below.

[0070] In the population initialization stage, the Spider Wasp Optimization Algorithm uses random initialization to initialize the female spider wasp population, as shown in equation (15).

[0071] (15)

[0072] Where represents the solution corresponding to the ith female spider wasp of the tth generation, and is the upper limit of the search space, the lower limit of the search space, a random vector.

[0073] The hunting and nesting behavior of the algorithm includes the search stage and the tracking and escape stage. In the search stage, the female spider wasp has two ways to search: the first way is to update the position by a fixed step to search for prey; the second way is to search around the position where the spider falls. After searching for prey by these two ways, the position of the female spider wasp is updated using equations (16) and (18), respectively.

[0074] (16)

[0075] Where and are two random female spider wasps in the tth generation population used to determine the search direction, is used to determine the step size along the direction, and its calculation formula is:

[0076] (17)

[0077] Where is a random number between 0 and 1, is a random number generated by normal distribution.

[0078] (18)

[0079] (19)

[0080] (20)

[0081] where is a random female wasp in the tth generation, and l is a random number between 1 and 2.

[0082] The SWO algorithm determines which search method to use to update the position of the female wasp by equation (21).

[0083] (21)

[0084] where and are random numbers between 0 and 1.

[0085] When the female wasp finds the prey, it will attack the prey, but the spider will fall off the web and escape. After that, there are two trends: the wasp chases the prey and the prey successfully escapes.

[0086] When the wasp chases the prey, equation (22) is used to simulate the situation of the female wasp chasing the spider to catch them.

[0087] (22)

[0088] (23)

[0089] where C is a distance control factor for determining the speed of the wasp, is a random vector between [0, 1], is a random number between [0, 1], t is the current iteration number, and t max is the maximum iteration number, is the tth generation wasp randomly selected.

[0090] When the prey successfully escapes, equation (24) is used to simulate the trend that the distance between the wasp and the spider increases as the iteration number increases.

[0091] (24)

[0092] (25)

[0093] where is a vector generated according to the normal distribution between -k and k, and the value of k is calculated by equation (25).

[0094] The above two trends are weighed by equation (26).

[0095] (26)

[0096] The search mechanism is mainly applied by the female wasps in the initial stage of the optimization process, and the following and escape mechanisms are mainly applied by the female wasps with the increase of the iteration number of the optimization algorithm, and the transition of the two mechanisms is completed through formula (27).

[0097] (27)

[0098] where p is a random number between [0, 1].

[0099] In the nest building stage, the SWO selects and simulates two nest building methods of the wasps, the first method is to drag the prey into the area containing the best spider to build a nest, and the second method is to randomly select the position of a spider to build a nest. The two nest building methods can be described by formula (28) and (29).

[0100] (28)

[0101] (29)

[0102] (30)

[0103] where xoptis the current optimal solution, is a random number in the interval [0, 1], is a number generated according to Levy flight, is a binary vector used to determine whether to apply the step size to avoid building two nests in the same position, and are two vectors of random values in the interval [0, 1], , and are three solutions randomly selected from the population.

[0104] The SWO determines which nest building method to use through formula (31).

[0105] (31)

[0106] In the optimization process of the algorithm, the hunting behavior and the nest building behavior are balanced through formula (32):

[0107] (32)

[0108] The SWO generates potential solutions by simulating the mating behavior of female spiders and male spiders, as shown in formula (33):

[0109] (33)

[0110] ​where Crossover is a uniform crossover operator and CR is the crossover rate. is a female spider wasp, is a male spider wasp, the male spider wasp is generated by

[0111] (34)

[0112] where and are two numbers randomly generated according to a normal distribution, e is the exponential constant, and are calculated according to the following equation:

[0113] (35)

[0114] (36)

[0115] where is a fitness function, , , is a random solution in the population.

[0116] During the iteration process, the number of female wasps is reduced by equation (37) to accelerate the convergence speed of the algorithm.

[0117] (37)

[0118] where, is the minimum population number set in the algorithm.

[0119] The solution obtained by each wasp will be compared with the previous optimal solution, if the new solution is better, the current solution will be replaced by the new solution.

[0120] In order to apply the SVR algorithm and the SWO algorithm to the embodiments of the present application to solve the problem of CD-SEM metrology accuracy caused by the photoresist shrinkage effect, the embodiments of the present application propose to apply the SVR algorithm to the photoresin shrinkage prediction task. As a possible implementation, the SVR algorithm can be used to construct a regression model for predicting the shrinkage amount of the photoresist caused by the CD-SEM measurement, and an optimization problem with the SVR model prediction accuracy as the target is constructed, and the optimization problem is solved by using the spider wasp optimization algorithm to obtain the optimal solution, so as to improve the regression performance and accuracy of the trained prediction model. As a preferred implementation, the spider wasp optimization algorithm of the embodiments of the present application is an improved spider wasp optimization algorithm, which is based on a good point set for population initialization. The good point set (GPS) is a method for optimizing the initial distribution, which can generate a point set with good coverage and high uniformity under the constraint of limited resources. Assuming is a unit cube in s-dimensional Euclidean space, and , then the good point set can be obtained by formula (41):

[0121] (41)

[0122] where the good point ; P is the smallest prime number satisfying ; and is the decimal part of .

[0123] , and the deviation of the good point set needs to satisfy formula (42)

[0124] (42)

[0125] where , is a constant related to r and .

[0126] Therefore, the initialization population strategy based on the good point set, i.e. formula (15) in the above-mentioned SWO algorithm, can be represented by formula (43).

[0127] (43)

[0128] Therefore, during the preset number of iteration solving process by using the spider wasp optimization algorithm, the population initialization can be performed based on the good point set.

[0129] More preferably, the improved spider optimization algorithm is population reduction using a cyclic population reduction technique in the process of iteration. The cyclic population reduction technique is a population reduction strategy proposed in the pikaia optimization algorithm. This strategy can not only speed up the optimization process, but also ensure that the population has enough change and exploration ability in the optimization process, so as to better avoid falling into local minimum and find the global optimal solution. CPR determines the number of times this process is performed in the optimization process based on the cycle variable T, as shown in equation (38). The mathematical model of cyclic population reduction is as follows, which can be used to replace the population reduction algorithm of equation (37) in the above SWO algorithm process:

[0130] (38)

[0131] Where T is a variable for determining the number of cycles, t is the current function evaluation number, is the maximum function evaluation number, is the minimum number of individuals in the newly generated population, so that the population size cannot be less than .

[0132] More preferably, the trade-off rate is a super parameter that significantly affects the optimization ability of the spider optimization algorithm. In SWO, manually adjusting the trade-off rate limits the effective use of the algorithm. To solve this problem, the improved spider optimization algorithm of the present embodiment proposes an adaptive trade-off rate (TR) strategy for dynamically adjusting the hunting and nesting behavior and mating behavior in the spider optimization algorithm. Specifically, the adaptive trade-off rate strategy can be implemented as follows: if the best fitness of the current iteration is improved by more than a preset threshold compared to the best fitness of the last iteration , the local development ability of the algorithm is strengthened by reducing the TR value; otherwise, if the improvement does not reach the threshold, the global search ability is enhanced by increasing the TR value.

[0133] The hunting behavior and nesting behavior of the improved spider optimization algorithm in the optimization process of the algorithm are balanced by the following formula:

[0134] (39)

[0135] Wherein, is the trade-off rate in the tth iteration, the adjustment range of the value is limited between 0.1 and 0.9, t is the current iteration number, is the preset threshold, is the adjustment step, is the best fitness improvement, and its calculation formula is as follows:

[0136] (40)

[0137] wherein denotes the best fitness in the tth iteration, denotes the best fitness in the (t-1)th iteration.

[0138] wherein, Figure 4 a method for training a prediction model is shown schematically, as Figure 4 shown, the method comprises:

[0139] In operation S21, a regression model for the shrinkage of the photoresist under CD-SEM measurement is constructed based on the CD-SEM measurement conditions, the photoresist thickness and the photoresist width raw values measured by the CD-SEM, to generate an initial model based on support vector regression.

[0140] In operation S22, the initial model constructed is trained based on the improved wasp optimization algorithm, the training sample set and the verification set, to generate the prediction model.

[0141] As a possible implementation, in operation S21, when the regression model for the shrinkage of the photoresist under CD-SEM measurement is constructed, the present application is based on the nonlinear mapping relationship between the CD-SEM measurement conditions, the photoresist thickness and the photoresist width raw values measured by the CD-SEM, and the shrinkage of the photoresist, to construct the SVR initial model. Specifically, the initial model constructed takes the shrinkage of the photoresist as the predicted value f(x), and takes the CD-SEM measurement conditions, the photoresist thickness and the photoresist width values as the input. The CD-SEM measurement conditions can be specifically the acceleration voltage and the exposure time set by the CD-SEM when performing measurement. The photoresist thickness and the photoresist width raw values are the measurement results obtained based on the corresponding acceleration voltage and exposure time. Based on this, the regression model constructed by the present application for predicting the shrinkage of the photoresist under SEM measurement can be abstracted as:

[0142]

[0143] wherein, f(x) is the predicted shrinkage value of the photoresist, and x is a vector including the acceleration voltage set by the CD-SEM, the exposure time of the CD-SEM, the photoresist thickness and the photoresist width raw values measured by the CD-SEM.

[0144] According to the foregoing SVR algorithm process, in order to obtain a final model that can be used to predict the shrinkage of photoresist caused by CD-SEM measurement, the training of the foregoing regression model actually converts into a problem of solving the hyperparameters in the model, and if the optimal solution of the hyperparameters is found, the accuracy of the SVR model in solving the photoresist shrinkage prediction value problem can be effectively improved. In combination with the foregoing SVR algorithm process, the hyperparameters that need to be solved optimally determined by the embodiment of the present application include a penalty factor C, a hyperparameter σ of a kernel function, and a tolerance deviation width ε of an insensitive loss function. As a preferred implementation manner, the embodiment of the present application uses an improved spider wasp optimization algorithm to optimize and solve these hyperparameters (referred to as to-be-optimized parameters in the embodiment of the present application) of the initial model by using a training sample set and a validation set, so as to improve the accuracy of the SVR model in solving the photoresist shrinkage prediction value problem. The training sample set includes at least one group of training data, each group of training data includes an acceleration voltage of a CD-SEM, an exposure time of the CD-SEM, a measured photoresist thickness and a photoresist width original value of the CD-SEM, and a photoresist shrinkage value, wherein the photoresist shrinkage value in each group of training data is the actual shrinkage value corresponding to the measured photoresist thickness and photoresist width original value under the corresponding CD-SEM acceleration voltage and exposure time. The validation set of the embodiment of the present application includes at least one group of validation data, each group of validation data includes an acceleration voltage of a CD-SEM, an exposure time of the CD-SEM, a measured photoresist thickness and a photoresist width original value of the CD-SEM, and a photoresist shrinkage value. Wherein, after obtaining the experimental data, the experimental data can be divided into the training sample set and the validation set by dividing the experimental data according to a ratio such as 8:2, and the experimental data can be obtained by measuring KrF photoresist with a thickness of 3300 Å, 4000 Å and 5200 Å and a critical dimension (CD) of 150 nm and 220 nm under different acceleration voltages (400 V, 500 V, 600 V, 700 V, 800 V) and different exposure times t by using a CD-SEM. When training, the embodiment of the present application sets the mean square error (MSE value) of the initial model on the validation set as the fitness function value of the improved spider wasp optimization algorithm, and sets the optimization target of the improved spider wasp optimization algorithm as minimizing the fitness function value, so that the problem of finding the optimal solution of the to-be-optimized parameters by the improved spider wasp optimization algorithm is converted into the problem of optimizing the following formula to continuously reduce the difference between the photoresist shrinkage value and the predicted photoresist shrinkage value:

[0145]

[0146] In the formula, n represents the number of groups of validation data in the validation set, is the photoresist shrinkage value corresponding to the i th group of validation data in the validation set, Predicted shrinkage value obtained from initial model and raw values of CD-SEM accelerating voltage, CD-SEM exposure time, photoresist thickness and photoresist width measured by CD-SEM in the i-th group of verification data in the verification set.

[0147] Thus, the training of the constructed initial model based on the improved wasp optimization algorithm, the training sample set and the verification set is actually implemented as follows: the improved wasp optimization algorithm is used for an iterative solving process of a preset number of times, in each iteration process, the improved wasp optimization algorithm finds the solution of the to-be-optimized parameter corresponding to the current population by using the training data in the training sample set (for example, the solution corresponding to the t-th generation population is determined in the t-th iteration process); after the solution of the to-be-optimized parameter corresponding to the current population is determined, the initial model is further updated by using the solution of the to-be-optimized parameter, and the mean square error value of the updated initial model is calculated by using the verification set; then, the initial model corresponding to the to-be-optimized parameter with the minimum mean square error value in the updated initial model is selected as the optimal solution of the current population, and the optimal solution of the current population is selected as the fitness; then, the position of the solution of the to-be-optimized parameter is updated according to the calculated fitness, and after the position of the solution is updated, the next improved wasp optimization algorithm iteration process is continued on the basis of the position of the solution by using the training sample set, so as to obtain the solution of the to-be-optimized parameter corresponding to the next generation population; in this way, when the preset number of iterations is reached, the optimal solution of the to-be-optimized parameter in the entire iteration process can be found. Then, the initial model is updated by using the optimal solution of the to-be-optimized parameter iterated by the improved wasp optimization algorithm, and the trained prediction model is determined by using the training sample set to train the updated initial model. It should be noted that the preset number of iterations can be set according to requirements and experience, for example, the preset number of iterations can be set to 50 times. In addition, it should be noted that the initial model of the embodiment of the present application refers to the regression model before the trained prediction model is obtained, including the model constructed at the initialization and the model in the intermediate state formed in the iteration process, and after being trained, it is referred to as a prediction model, and the initial model corresponding to each group of solutions is an SVR model obtained by updating the parameters of the initial model with each group of to-be-optimized parameters. The detailed implementation process of the improved wasp optimization algorithm for finding the solution of the to-be-optimized parameter corresponding to the current population by using the training data in the training sample set, and the detailed implementation process of the training of the regression model of the updated initial model by using the training sample set, can be implemented based on the algorithm process description of the corresponding algorithm in the foregoing description and the prior art, and the process will not be described in detail.

[0148] wherein, Figure 5 The process of optimization and solving by using the improved wasp optimization algorithm is schematically shown as follows:Figure 5 As shown, the algorithm process first initializes the population based on the set of good points, and then determines the optimized parameter solution corresponding to the current population at the preset iteration number t max within the range, and then updates the initial model using the optimized parameter solution corresponding to the current population, and calculates the mean square error value of the updated initial model using the validation set to find the optimal solution in the current population as the fitness of the solution, and updates the position of the optimized parameter solution according to the calculated fitness, and uses the loop population reduction technique to reduce the population, and then performs the next iteration process until the preset iteration number t max is reached, and outputs the found optimal solution. In the algorithm, the improved scheme described above is used in population initialization, hunting and nesting behavior, adjustment of the mating behavior rate, and population reduction.

[0149] The embodiment of the present application proposes a prediction method for optimizing the support vector regression (SVR) model based on the improved spider bee optimization algorithm (referred to as ISWO in the embodiment of the present application) to solve the problem of metrology error caused by the shrinkage of photoresist under electron beam irradiation in the CD-SEM measurement process. In the hyperparameter optimization stage, the ISWO is used to optimize the penalty factor C, the kernel function parameter σ, and the tolerance deviation width ε of the insensitive loss function of the SVR model. The ISWO improves the global search ability and convergence efficiency of the original SWO algorithm by introducing the loop population reduction, dynamic weighting probability, and good point set initialization strategies, and improves the stability and precision of parameter optimization. Therefore, the prediction model trained by the ISWO exhibits good performance in fitting accuracy, error control, and convergence stability. The method not only effectively improves the prediction ability of the SVR model, but also provides a practical modeling method for the shrinkage error correction of the CD-SEM.

[0150] In order to systematically evaluate the hyperparameter optimization capability of the ISWO algorithm in the support vector regression (SVR) model and verify its effectiveness in the photoresist shrinkage prediction task, the embodiment of the present application designs comparative experiments based on other six optimization algorithms, including SWO (i.e. the improved spider bee optimization algorithm), RIME (a high-efficiency optimization algorithm based on the physical phenomenon of fog ice, referred to as RIME, also known as frost ice optimization algorithm), PSO (Particle Swarm Optimization, particle swarm optimization algorithm), DBO (Dung beetle optimizer, dung beetle optimization algorithm), GWO (Grey Wolf Optimizer, grey wolf optimization algorithm), and BKA (black-winged kite algorithm), wherein Figure 6The ISWO and other six optimization algorithms are shown in the support vector regression (SVR) model in the process of super parameter optimization, the mean square error (MSE) convergence curve obtained in each iteration, the abscissa in the figure is the iteration number, and the ordinate is the MSE value under the corresponding iteration, from the figure, it can be seen that ISWO rapidly reduces the MSE to a lower level in the early iteration, and continues to decrease slightly in the subsequent iteration, and finally stabilizes at about 0.0034, the convergence speed and final accuracy are better than other algorithms.

[0151] To further evaluate the effect of different optimization algorithms on the optimization of SVR model super parameters, the SWO, RIME, PSO, DBO, GWO, BKA and ISWO proposed in the application are used to optimize the super parameters of the SVR model, and the performance is evaluated on the unified data set. Figure 7 The optimal super parameter combination (ε, C, σ) obtained by each algorithm and its prediction performance index on the validation set are listed, including mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and determination coefficient (R²).

[0152] As Figure 7 shown in the table, from the results in the table, it can be seen that the ISWO-SVR model performs well in all evaluation indicators. Its MSE is 0.0034, which is better than the remaining six algorithms, indicating that its prediction error is the smallest; at the same time, its MAE and MAPE are 0.0408 and 15.4993% respectively, which are at a low level, indicating that the model performs stably in the absolute value and relative proportion of prediction error; R² reaches 0.9964, close to 1, indicating that the model has a high goodness of fit and can accurately reflect the change trend of photoresist shrinkage. Compared with the PSO-SVR, although it also performs well in MSE (0.0038) and R² (0.996), its MAPE is 24.2867%, which is significantly higher than that of ISWO-SVR, indicating that it has a deficiency in relative error control. In summary, ISWO not only has stronger global search ability, but also can quickly jump out of local optimum and find better solution in a limited number of iterations. The results further verify the effectiveness and robustness of ISWO in the SVR super parameter optimization task proposed in this study, and the effectiveness of solving the photoresist shrinkage problem.

[0153] To more intuitively compare the performance of different optimization algorithms in the SVR model, Figure 8A radar chart based on four indicators (MSE, MAE, MAPE and R²) is drawn. For MSE, MAE and MAPE, the smaller the value, the better the performance, and the closer the vertex to the center of the graph. The R² value is closer to 1, so in this dimension, the farther the vertex is from the center, the stronger the fitting ability. As can be seen from the distribution in the figure, the ISWO comprehensive performance is better than other optimization algorithms.

[0154] Figure 9 The ISWO-SVR model is a scatter plot of the predicted value of the photoresist shrinkage (ΔCD) and the actual value. The horizontal axis of the figure is the actual ΔCD value, and the vertical axis is the predicted value of the model. The dotted line represents the ideal prediction result (i.e. the reference line where the predicted value is completely consistent with the actual value). As can be seen from the figure, most data points are closely distributed near the reference line, indicating that the prediction results of the model are highly consistent with the actual values. This shows that the ISWO-SVR model has good fitting ability and high prediction accuracy when modeling the photoresist shrinkage behavior. In addition, the data points are linearly aligned in the entire numerical range, without obvious systematic deviation or uneven distribution, further verifying the robustness and generalization ability of the model at different ΔCD levels.

[0155] In summary, the ISWO algorithm in the present application shows excellent super parameter optimization ability and generalization performance, and has significant advantages in photoresist shrinkage modeling tasks, verifying its effectiveness and robustness in SVR model super parameter optimization.

[0156] Figure 10 The architecture of a photoresist shrinkage prediction system according to an embodiment of the present application is schematically shown as follows: Figure 10 As shown in the figure, the system includes a critical dimension scanning electron microscope 100 and a computing device 200, wherein the computing device includes

[0157] A shrinkage prediction module 201 is configured to obtain input data from the critical dimension scanning electron microscope and input the input data into a pre-trained prediction model, and obtain an output result of the prediction model, wherein the prediction model is generated based on a spider optimization algorithm and support vector regression training, and the output result is a shrinkage of the photoresist under SEM measurement, and the input data includes CD-SEM measurement conditions, CD-SEM measured photoresist thickness and photoresist width original value.

[0158] The specific implementation process of each module in the system and the specific training process of the prediction model can refer to the description in the foregoing method part, and will not be described here. It should be noted that in other embodiments, the photoresist shrinkage amount prediction system can also be directly implemented as a computing device including the shrinkage amount prediction module 201, and the critical dimension scanning electron microscope can not be part of the system, but only as a third-party provider of input data, and the embodiments of the present application are not limited in this regard.

[0159] Figure 11 An electronic device according to an embodiment of the present application is schematically shown in the figure, which is implemented as including:

[0160] a memory 81 for storing executable instructions; and

[0161] a processor 82 communicatively connected with the memory 81, configured to execute the executable instructions stored in the memory, and the executable instructions, when executed by the processor, implement the steps of the critical dimension scanning electron microscope photoresist image processing method according to any one of the foregoing embodiments.

[0162] In some embodiments, the embodiments of the present application provide a non-volatile computer readable storage medium, in which one or more programs including execution instructions are stored, and the execution instructions can be read and executed by an electronic device (including but not limited to a computer, a server, or a network device, etc.) to execute the critical dimension scanning electron microscope photoresist image processing method according to any one of the foregoing embodiments of the present application.

[0163] In some embodiments, the embodiments of the present application also provide a computer program product, which includes a computer program stored on a non-volatile computer readable storage medium, and the computer program includes program instructions, which, when executed by a computer, cause the computer to execute the critical dimension scanning electron microscope photoresist image processing method according to any one of the foregoing embodiments.

[0164] In some embodiments, the embodiments of the present application also provide an electronic device, which includes at least one processor, and a memory communicatively connected with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the critical dimension scanning electron microscope photoresist image processing method according to any one of the foregoing embodiments.

[0165] In some embodiments, the embodiments of the present application also provide a storage medium having a computer program stored thereon, and the program, when executed by a processor, implements the critical dimension scanning electron microscope photoresist image processing method according to any one of the foregoing embodiments.

[0166] Figure 12 Figure 1 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application. The computing device can be implemented in the structure shown in Figure 1. Figure 12

[0167] One or more processors 610 and a memory 620, Figure 12 In the embodiment, the processor 610 is taken as an example.

[0168] The electronic device can further include an input device 630 and an output device 640.

[0169] The processor 610, the memory 620, the input device 630 and the output device 640 can be connected through a bus or other means, Figure 12 In the embodiment, the connection through the bus is taken as an example.

[0170] The memory 620 is a non-volatile computer readable storage medium, which can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules corresponding to the critical dimension scanning electron microscope photoresist image processing method in the embodiment of the present application. The processor 610 executes various function applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 620, that is, implements the critical dimension scanning electron microscope photoresist image processing method of the above method embodiments.

[0171] The memory 620 can include a program storage area and a data storage area, wherein the program storage area can store an operating system and application programs required by at least one function; the data storage area can store data created according to the use of the critical dimension scanning electron microscope photoresist image processing method, etc. In addition, the memory 620 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 620 can optionally include a memory remotely arranged with respect to the processor 610, which can be connected to the electronic device through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0172] The input device 630 can receive input digital or character information, and generate signals related to user settings and function control of the image processing device. The output device 640 can include a display device such as a display screen.

[0173] The one or more modules are stored in the memory 620, and when executed by the one or more processors 610, perform the critical dimension scanning electron microscope photoresist image processing method in any of the above method embodiments.​

[0174] The product can perform the method provided by the embodiment of the application, has the corresponding function modules and beneficial effects of performing the method. Technical details not described in detail in the embodiment can be referred to the method provided by the embodiment of the application.

[0175] The electronic device of the embodiment of the application exists in various forms, including but not limited to:

[0176] (1) Mobile communication device: The feature of this kind of device is to have mobile communication function, and to provide voice and data communication as the main target. This kind of terminal includes: smart phone (such as iPhone), multimedia phone, functional phone, and low-end phone, etc.

[0177] (2) Ultra-mobile personal computer device: This kind of device belongs to the category of personal computer, has computing and processing function, and generally has the feature of mobile Internet. This kind of terminal includes: PDA, MID and UMPC device, such as iPad.

[0178] (3) Portable entertainment device: This kind of device can display and play multimedia content. This kind of device includes: audio and video player (such as iPod), palm game machine, electronic book, and smart toy and portable vehicle navigation device.

[0179] (4) Server: The device providing computing service, the constitution of the server includes processor, hard disk, memory, system bus, etc. The server is similar to the general computer architecture, but since it needs to provide high reliable service, it has higher requirements in processing capacity, stability, reliability, security, scalability, manageability, etc.

[0180] (5) Other electronic devices with data interaction function.

[0181] The device embodiment described above is only schematic, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. According to actual needs, part or all of the modules can be selected to achieve the purpose of the embodiment of the application.

[0182] Those skilled in the art can clearly understand the implementation of the various embodiments by means of software plus a general hardware platform from the above description of the embodiments, and of course, the various embodiments can also be implemented by hardware. Based on such an understanding, the above technical solutions, essentially or in other words, the part that contributes to the related art, can be embodied in the form of a software product. The computer software product can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a plurality of instructions to cause a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0183] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A method of predicting a photoresist shrinkage, characterized by, The method comprises the following steps: acquiring input data, wherein the input data comprises CD-SEM measurement conditions, CD-SEM measured photoresist thickness and photoresist width original values; inputting the acquired input data into a pre-trained prediction model, and acquiring an output result of the prediction model, wherein the prediction model is trained based on an improved spider optimization algorithm and a support vector regression algorithm, and the output result is a shrinkage amount of photoresist under CD-SEM measurement, and the hunting behavior and nest building behavior of the improved spider optimization algorithm are balanced by the following formula: where TR t is the trade-off rate in the tth iteration, TR t The adjustment range of the value is limited between 0.1 and 0.9, t is the current iteration number, δ is a preset threshold, η is an adjustment step, and ΔFit is the optimal fitness improvement amplitude, and the calculation formula is as follows: where Best_score t represents the best fitness in the tth iteration, Best_score t-1 represents the best fitness in the t-1th iteration.

2. The method of claim 1, wherein, The prediction model is trained by the following method: regression modeling of the shrinkage amount of photoresist under CD-SEM measurement based on the CD-SEM measurement conditions, the CD-SEM measured photoresist thickness and the photoresist width original values, to generate an initial model based on support vector regression; training the constructed initial model based on the improved spider optimization algorithm, a training sample set and a verification set, to generate the prediction model.

3. The method of claim 2, wherein, Training the constructed initial model based on the improved spider optimization algorithm, the training sample set and the verification set to generate the prediction model comprises: setting the mean square error of the initial model on the verification set as a fitness function value, setting an optimization target as minimizing the fitness function value, training the initial model based on the optimization target, the training sample set and the verification set by using the improved spider optimization algorithm to generate the prediction model.

4. The method of claim 3, wherein, Training the initial model based on the optimization target, the training sample set and the verification set by using the improved spider optimization algorithm comprises: optimizing the to-be-optimized parameters of the initial model based on the improved spider optimization algorithm, the training sample set, the verification set and the optimization target, to determine an optimal solution of the to-be-optimized parameters; regression training the initial model based on the determined optimal solution and the training sample set to generate the prediction model.

5. The method of claim 4, wherein, Optimizing the to-be-optimized parameters of the initial model based on the improved spider optimization algorithm, the training sample set, the verification set and the optimization target to determine an optimal solution of the to-be-optimized parameters comprises: in each iteration process, the following processing is performed until a preset number of iterations is reached, and the optimal solution of the to-be-optimized parameters is output when the preset number of iterations is reached: determining the solution of the to-be-optimized parameters corresponding to the current population by using the training data in the training sample set, and updating the initial model by using the solution of the to-be-optimized parameters; calculating the mean square error value of the updated initial model by using the verification set, and selecting a set of solutions of the to-be-optimized parameters with the minimum mean square error value as the fitness; updating the position of the solution of the to-be-optimized parameters according to the calculated fitness.

6. The method of claim 5, wherein, Before starting the iteration process, the improved spider optimization algorithm performs population initialization based on a set of optimal points.

7. The method of claim 6, wherein, The improved spider optimization algorithm adopts a cyclic population reduction technique to reduce the population in the iteration process.

8. A photoresist shrinkage prediction system, characterized by, The method comprises the following steps: The shrinkage prediction module is configured to acquire input data from a critical dimension scanning electron microscope and input the input data into a pre-trained prediction model, and acquire an output result of the prediction model, wherein the prediction model is generated based on an improved spider optimization algorithm and a support vector regression algorithm, the output result is a shrinkage of photoresist caused by SEM measurement, and the input data includes CD-SEM measurement conditions, a photoresist thickness and a photoresist width original value measured by CD-SEM, and a hunting behavior and a nest building behavior of the improved spider optimization algorithm are balanced by the following formula: where TR t is the trade-off rate in the tth iteration, TR t The adjustment range of the value is limited between 0.1 and 0.9, t is the current iteration number, δ is a preset threshold, η is an adjustment step, and ΔFit is the optimal fitness improvement amplitude, and the calculation formula is as follows: where Best_score t represents the best fitness in the tth iteration, Best_score t-1 represents the best fitness in the t-1th iteration.

9. An electronic device, comprising: The shrinkage prediction module is configured to acquire input data from a critical dimension scanning electron microscope and input the input data into a pre-trained prediction model, and acquire an output result of the prediction model, wherein the prediction model is generated based on an improved spider optimization algorithm and a support vector regression algorithm, the output result is a shrinkage of photoresist caused by SEM measurement, and the input data includes CD-SEM measurement conditions, a photoresist thickness and a photoresist width original value measured by CD-SEM, and a hunting behavior and a nest building behavior of the improved spider optimization algorithm are balanced by the following formula: The shrinkage prediction module is configured to acquire input data from a critical dimension scanning electron microscope and input the input data into a pre-trained prediction model, and acquire an output result of the prediction model, wherein the prediction model is generated based on an improved spider optimization algorithm and a support vector regression algorithm, the output result is a shrinkage of photoresist caused by SEM measurement, and the input data includes CD-SEM measurement conditions, a photoresist thickness and a photoresist width original value measured by CD-SEM, and a hunting behavior and a nest building behavior of the improved spider optimization algorithm are balanced by the following formula: The shrinkage prediction module is configured to acquire input data from a critical dimension scanning electron microscope and input the input data into a pre-trained prediction model, and acquire an output result of the prediction model, wherein the prediction model is generated based on an improved spider optimization algorithm and a support vector regression algorithm, the output result is a shrinkage of photoresist caused by SEM measurement, and the input data includes CD-SEM measurement conditions, a photoresist thickness and a photores

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