WO2023193428A1 - Pixelation optical proximity correction method and system applied to super-resolution photolithography - Google Patents

Abstract

A pixelation optical proximity correction method applied to super-resolution photolithography. The method comprises: S1, obtaining pixelated initial mask data according to a target pattern; S2, calculating an imaging error between a photoresist output pattern and the target pattern according to the initial mask data and a condition for super-resolution photolithography; S3, encoding the initial mask data, and initializing structural parameters of super-resolution photolithography and parameters of a covariance matrix adaptation evolution strategy-based algorithm; and S4, performing an iterative operation by using the covariance matrix adaptation evolution strategy-based algorithm until mask data meeting a preset condition is obtained, such that optical proximity correction is completed.

Description

Pixelated optical proximity effect correction method and system applied to super-resolution lithography

This disclosure claims priority from the Chinese patent application with application number 202210372026.3, submitted on April 8, 2022, the entire content of which is incorporated into this disclosure by reference.

Technical field

The present disclosure relates to the field of integrated circuit technology, and specifically to a pixelated optical proximity effect correction method and system applied to super-resolution lithography, a super-resolution lithography method, electronic equipment, storage media and program products.

Background technique

Lithography technology is one of the core technologies of large-scale integrated circuits. The continuous reduction of critical dimensions has led to the revolutionary development and progress of lithography technology and lithography systems. Currently, projection lithography is widely used in the mass production of integrated circuits. However, when the system does not break through the diffraction limit, it requires the cooperation of multiple resolution enhancement technologies, which makes the entire process system complex and costly; Super-resolution lithography can make full use of evanescent waves that carry high-frequency information about objects during imaging, and can achieve optical nanoimaging beyond the diffraction limit.

At the same time, during the integrated circuit manufacturing process, distortion-free transfer of mask patterns must be achieved as much as possible to ensure the reliability and yield of semiconductor devices. However, when photolithographic exposure is performed on high-density arranged mask patterns, due to the interference and diffraction effects of incident light, the pattern transferred to the silicon wafer will appear rounded at right angles, the ends of straight lines will be retracted, and the width of straight lines will increase. Or distortion phenomena such as reduction. In order to compensate for these graphics distortions, many resolution enhancement techniques (Resolution Enhancement Technique, RET) have been widely studied, and optical proximity effect correction technology (Optical Proximity Correction, OPC) is one of the important branches. Due to complex imaging models and nonlinear photoresist effects, gradient-based OPC methods are difficult to apply to super-resolution lithography of sub-wavelength electromagnetic structures.

Pixel-based OPC can effectively increase the degree of freedom in optimization, but the increase in variables will consume more computing resources and increase running time. Therefore, there is an urgent need for a simple, efficient, and low-cost optical proximity effect compensation method to optimize the mask to meet the requirements of minimizing the deviation between the output pattern in the photoresist and the target mask pattern, reduce pattern distortion, and improve Product reliability.

Contents of the invention

(1) Technical problems to be solved

In response to the above problems, the present disclosure provides a pixelated optical proximity effect correction method and system for super-resolution lithography, a super-resolution lithography method, electronic equipment, storage media and program products to solve the problem of traditional gradient-based problems. The OPC method is difficult to apply to super-resolution lithography and there are technical problems such as the excessive calculation amount of pixel-based OPC.

(2) Technical solutions

On the one hand, the present disclosure provides a pixelated optical proximity effect correction method applied to super-resolution lithography, including: S1, obtaining pixelated initial mask data according to the target pattern; S2, according to the initial mask data and super-resolution light Calculate the imaging error between the photoresist output pattern and the target pattern according to the etching conditions; S3, encode the initial mask data, initialize the structural parameters of super-resolution lithography, and the parameters of the covariance matrix adaptive update strategy algorithm; S4, use Based on the covariance matrix adaptive update strategy algorithm, iterative operations are performed until mask data that meets the preset conditions is obtained, and the correction of the optical proximity effect is completed.

Further, S1 includes: S11, obtaining an initial mask pattern according to the target pattern; S12, performing pixelation processing on the initial mask pattern to obtain pixelated initial mask data.

Further, S2 includes: S21, calculate the spatial light field intensity distribution in the photoresist based on the initial mask data and super-resolution lithography conditions; S22, obtain the photoresist based on the spatial light field intensity distribution in the photoresist. Output the pattern, and calculate the total number of pixel errors between the photoresist output pattern and the target pattern as the imaging error.

Further, S3 includes: S31, encoding the initial mask data into a row matrix in a column-by-point scanning manner to obtain the encoded iterative mask data; S32, initializing the structural parameters of super-resolution lithography, and the structural parameters are at least Including the thickness and dielectric constant of each film layer; S33, initialize the parameters of the covariance matrix adaptive update strategy algorithm. The parameters at least include the number of optimization variables, distribution mean, search step size, covariance matrix and population number, where The encoded iterative mask data is used as the initial distribution mean.

Further, S31 includes: if the mask pattern corresponding to the initial mask data is a mask pattern that is symmetrical about the coordinate axis, then encoding the mask data of the first quadrant into a row matrix in a column-by-point scanning manner, to obtain The encoded iterative mask data; otherwise, encode all the initial mask data into a row matrix by scanning column by point, to obtain the encoded iterative mask data.

Further, S4 includes: S41, using an adaptive update strategy algorithm based on the covariance matrix to sample and binarize the encoded iterative mask data to obtain a first number of iterative mask data; S42, convert the first number of iterative mask data to The iterative mask data is decoded, and a first number of imaging errors are calculated according to the conditions of super-resolution lithography; S43, based on the first number of imaging errors, select a second number of iterative mask data from the first number Iterate the mask data; S44, update and obtain the next generation iteration mask data according to the second number of iteration mask data; S45, use the next generation iteration mask data as the updated distribution mean and update the search step size and coherence Variance matrix, repeat S41 to S45 for iterative calculation until mask data that meets the preset conditions is obtained, and the correction of the optical proximity effect is completed.

Further, S42 includes: if the mask pattern corresponding to the initial mask data is a mask pattern that is symmetrical about the coordinate axis, decoding the first number of iterative mask data and performing a mirroring operation to obtain a mask of the entire mask pattern. mask data; otherwise, decode the first number of iteration mask data to obtain the mask data of the entire mask pattern.

Further, S43 to S44 include: arranging the first number of imaging errors in ascending order; selecting the iterative mask data corresponding to the first second number of imaging errors, and performing a weighted sum on them to obtain the next generation iterative mask data.

Further, S45 includes: calculating and updating the search step size based on the accumulation of evolutionary paths; using rank-1 and rank-μ update mechanisms to update the covariance matrix based on the evolutionary paths.

Further, S45 also includes: if the current imaging error meets a preset threshold condition or the number of iterations is greater than the maximum number of iterations condition, the current iteration mask data is the mask data that meets the preset conditions to complete the correction of the optical proximity effect.

Furthermore, the conditions for super-resolution lithography in S2 include the structure of super-resolution lithography. The structure of super-resolution lithography includes mask substrate, mask, air spacer layer, metal transmission layer, photoresist, metal reflection layer and substrate. The structure of super-resolution lithography includes a mask substrate, a mask, an air spacer layer, a metal transmission layer, a photoresist and a substrate; or the structure of super-resolution lithography includes a mask substrate, Structure of mask, air spacer, photoresist, metal reflective layer and substrate.

On the other hand, the present disclosure provides a method for applying pixelated optical proximity effect correction to super-resolution lithography, including: S01, obtaining pixelated initial mask data according to the target pattern; S02, according to the initial mask data and super-resolution lithography The conditions for resolution lithography are to calculate the imaging error between the photoresist output pattern and the target pattern; S03, encode the initial mask data, initialize the structural parameters of super-resolution lithography, and the parameters of the adaptive update strategy algorithm based on the covariance matrix; S04 , use the adaptive update strategy algorithm based on the covariance matrix to perform iterative operations until the mask data that meets the preset conditions is obtained to complete the correction of the optical proximity effect; and output the final mask pattern; S05, perform super-resolution based on the final mask pattern Lithography.

In another aspect, the present disclosure provides a pixelated optical proximity effect correction system applied to super-resolution lithography, including: a preprocessing module for obtaining pixelated initial mask data according to the target pattern; and a calculation module for Calculate the imaging error between the photoresist output pattern and the target pattern according to the initial mask data and the conditions of super-resolution lithography; the encoding module is used to encode the initial mask data, initialize the structural parameters of super-resolution lithography, and based on covariance The parameters of the matrix adaptive update strategy algorithm; the iterative operation module is used to perform iterative operations using the covariance matrix adaptive update strategy algorithm until mask data that meets the preset conditions is obtained to complete the correction of the optical proximity effect.

Another aspect of the present disclosure provides an electronic device, including: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors When, one or more processors are caused to execute the aforementioned pixelated optical proximity effect correction method applied to super-resolution lithography.

In another aspect, the present disclosure provides a computer-readable storage medium on which executable instructions are stored. When executed by a processor, the instructions cause the processor to perform the aforementioned pixelated optical proximity effect correction applied to super-resolution lithography. method.

In another aspect, the present disclosure provides a computer program product, including a computer program. When the computer program is executed by a processor, the computer program implements the aforementioned pixelated optical proximity effect correction method applied to super-resolution lithography.

(3) Beneficial effects

The present disclosure provides a pixelated optical proximity effect correction method and system for super-resolution lithography, super-resolution lithography methods, electronic equipment, storage media and program products, using a covariance matrix-based adaptive update strategy (Covariance The Matrix Adaptation Evolution Strategy (CMA-ES) algorithm optimizes the initial mask data to achieve optical proximity effect correction suitable for super-resolution lithography. This can be achieved without establishing a complete mathematical model of the super-resolution lithography system and without solving the gradient. OPC of pixelated masks; global encoding of mask data can realize OPC of arbitrarily complex masks. In particular, symmetry encoding of symmetric mask data can double the optimization variables and greatly improve Optimization speed; compared with OPC based on other heuristic algorithms, this method controls the search step size and solution search space during the optimization process, allowing the optimization variables to find the adjustment direction faster.

Description of the drawings

For a more complete understanding of the present disclosure and its advantages, reference will now be made to the following description taken in conjunction with the accompanying drawings, in which:

Figure 1 schematically shows an application scenario diagram of a pixelated optical proximity effect correction method applied to super-resolution lithography according to an embodiment of the present disclosure;

Figure 2 schematically shows a flow chart of a pixelated optical proximity effect correction method applied to super-resolution lithography according to an embodiment of the present disclosure;

Figure 3 schematically shows a flow chart of a method for iterative operations using an adaptive update strategy algorithm based on a covariance matrix according to an embodiment of the present disclosure;

Figure 4 schematically shows a flow chart of a method for applying pixelated optical proximity effect correction to super-resolution lithography according to an embodiment of the present disclosure;

Figure 5 schematically shows a flow chart of a super-resolution photolithography mask OPC method based on the CMA-ES algorithm according to an embodiment of the present disclosure;

Figure 6 schematically shows a comparison diagram of the initial mask pattern, the corresponding imaged pattern in the photoresist, and the contour of the imaged pattern in the photoresist before optimization and the contour of the target pattern according to Embodiment 1 of the present disclosure;

Figure 7 schematically shows the OPC-optimized mask according to Embodiment 1 of the present disclosure, the corresponding imaging pattern in the photoresist, and the comparison diagram of the contour of the imaging pattern in the optimized photoresist and the contour of the target pattern;

Figure 8 schematically shows the mask pattern before and after OPC optimization according to Embodiment 2 of the present disclosure and the contour comparison diagram of the imaging pattern and the target pattern in the corresponding photoresist;

Figure 9 schematically shows the mask pattern before and after OPC optimization according to Embodiment 3 of the present disclosure and the contour comparison diagram of the imaging pattern and the target pattern in the corresponding photoresist;

10 schematically illustrates a block diagram of a pixelated optical proximity effect correction system applied to super-resolution lithography according to an embodiment of the present disclosure;

Figure 11 schematically shows a block diagram of an electronic device suitable for implementing the above-described method according to an embodiment of the present disclosure.

Detailed ways

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood, however, that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for convenience of explanation, numerous specific details are set forth to provide a comprehensive understanding of the disclosed embodiments. It will be apparent, however, that one or more embodiments may be practiced without these specific details. Furthermore, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessarily confusing the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the disclosure. The terms "comprising," "comprising," and the like, as used herein, indicate the presence of stated features, steps, operations, and/or components but do not exclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art, unless otherwise defined. It should be noted that the terms used here should be interpreted to have meanings consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

Several block diagrams and/or flow diagrams are shown in the accompanying drawings. It will be understood that some blocks in the block diagrams and/or flowchart illustrations, or combinations thereof, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the instructions, when executed by the processor, create functions for implementing the functions illustrated in these block diagrams and/or flowcharts. /operating device. The technology of the present disclosure may be implemented in the form of hardware and/or software (including firmware, microcode, etc.). Additionally, the technology of the present disclosure may take the form of a computer program product on a computer-readable storage medium having instructions stored thereon, and the computer program product may be used by or in conjunction with an instruction execution system.

In this disclosure, for convenience of explanation, only the target pattern, the initial mask pattern, the mask pattern and the final mask pattern are called patterns, while the calculation process and the results obtained by the imaging process in the pixelated optical proximity effect correction are both called As data, it can be understood that the data in the process can output corresponding graphics.

FIG. 1 schematically shows an application scenario diagram of a pixelated optical proximity effect correction method that can be applied to super-resolution lithography according to an embodiment of the present disclosure. It should be noted that Figure 1 is only an example of application scenarios in which embodiments of the present disclosure can be applied, to help those skilled in the art understand the technical content of the present disclosure, but does not mean that the embodiments of the present disclosure cannot be used in other applications. Device, system, environment or scenario.

The super-resolution lithography structure of the embodiment of the present disclosure can be shown in Figure 1. Two structures are shown in Figure 1. The super-resolution lithography structure of 1001 in Figure 1 includes a mask (SiO ₂ +Cr) and an air spacer layer. (Air), metal layer (Ag), photoresist (Pr), metal reflective layer (Ag) and substrate (SiO ₂ ) structure; the super-resolution photolithography structure of 1002 in Figure 1 includes: mask (SiO ₂ + The structure of Cr), air spacer layer (Air), metal layer (Ag), photoresist (Pr) and substrate (SiO ₂ ). In addition, the structure of super-resolution lithography may also be a structure including a mask substrate, a mask, an air spacer layer, a photoresist, a metal reflective layer and a substrate (not shown in the figure). Of course, the method of the present disclosure is not limited to being only applicable to the above three structures. Other super-resolution lithography structures can also use the pixelated optical proximity effect correction method of the present disclosure.

Figure 2 schematically shows a flow chart of a pixelated optical proximity effect correction method applied to super-resolution lithography according to an embodiment of the present disclosure.

As shown in Figure 2, the pixelated optical proximity effect correction method applied to super-resolution lithography includes:

In operation S1, pixelated initial mask data is obtained according to the target pattern.

The initial mask pattern is obtained according to the target pattern. The initial mask pattern processing part in this disclosure is to divide the mask pattern into pixelated grids in the Cartesian coordinate system. The transmittance of each pixel can be expressed as 0 or 1 represents the two states of opacity and light transmission respectively, and the pixelated initial mask data is obtained.

In operation S2, the imaging error between the photoresist output pattern and the target pattern is calculated according to the initial mask data and the conditions of super-resolution lithography.

The conditions for super-resolution lithography include the structure of super-resolution lithography. The structure may be the structure shown in FIG. 1 . Any structure that can achieve super-resolution lithography can be applied to the method of the present disclosure. Based on the super-resolution lithography imaging model, the spatial light field intensity distribution corresponding to the current mask data is obtained, and the imaging error is calculated. The imaging error is characterized by the graphic error function value. The size of the graphic error function value indicates the quality of the optimization result. The pattern error function value is defined as the total number of pixel deviations between the output pattern of the current mask data in the photoresist and the target pattern.

In operation S3, the initial mask data is encoded, and structural parameters of super-resolution lithography and parameters of the covariance matrix-based adaptive update strategy algorithm are initialized.

Before optimization, the transmittance value of the initial mask data is encoded into a row matrix; the structural parameters of super-resolution lithography are initialized to obtain the spatial light field intensity distribution corresponding to the current mask data, and then in the photoresist model Calculate the output pattern to calculate the imaging error between the photoresist output pattern and the target pattern; initialize the inherent parameters, distribution mean, system parameters, etc. of the CMA-ES algorithm for later iterative calculations of the CMA-ES algorithm to update and optimize Mask data.

In operation S4, an algorithm based on the adaptive update strategy of the covariance matrix is used to perform iterative operations until mask data that meets the preset conditions is obtained to complete the correction of the optical proximity effect.

OPC based on the covariance matrix adaptive update strategy algorithm is an effective lithography resolution enhancement technology. During the optimization process, gradient information is not used, and is updated by sampling in a multivariate normal distribution and using individuals with good adaptability. The mask data modulates the spatial light field intensity distribution, thereby improving the resolution of the lithography system and the image fidelity of the output pattern in the photoresist.

This disclosure uses an algorithm based on the covariance matrix adaptive update strategy to optimize the initial mask data to achieve optical proximity effect correction suitable for super-resolution lithography. There is no need to establish a complete mathematical model of the super-resolution lithography system, and there is no need to solve the gradient. OPC that can implement pixelated masks improves optimization speed.

Based on the above embodiment, S1 includes: S11, obtaining an initial mask pattern according to the target pattern; S12, performing pixelation processing on the initial mask pattern to obtain pixelized initial mask data.

Input the target graphic as the initial mask graphic, and then perform pixelation processing on the initial mask graphic. The value of each pixel is 0 or 1, where 0 means opaque and 1 means transparent, that is, the initial mask graphic is converted into binary matrix, the initial mask data is the binarized mask matrix data.

Based on the above embodiment, S2 includes: S21, calculating the spatial light field intensity distribution in the photoresist according to the initial mask data and the conditions of super-resolution lithography; S22, according to the spatial light field intensity distribution in the photoresist The photoresist output pattern is obtained from the distribution, and the total number of pixel errors between the photoresist output pattern and the target pattern is calculated as the imaging error.

A constant threshold model is used to characterize the output pattern in the photoresist PI=I>tr, where I is the spatial light field intensity distribution and tr is the photoresist threshold. The method of obtaining the spatial light field intensity in this operation can be calculated using the Rigorous Coupled-Wave Analysis (RCWA) method, or the Finite Differential Time Domain (FDTD) method. It can also be calculated using Calculated by Finite Element Method (FEM). The imaging error is the total number of pixel errors between the output pattern and the target pattern in the photoresist corresponding to the current mask data.

Based on the above embodiment, S3 includes: S31, encoding the initial mask data into a row matrix in a column-by-point scanning manner, and obtaining the encoded iterative mask data; S32, initializing the structural parameters of super-resolution lithography , the structural parameters at least include the thickness and dielectric constant of each film layer; S33, initialize the parameters of the covariance matrix adaptive update strategy algorithm, the parameters at least include the number of optimization variables, distribution mean, search step size, covariance matrix and population number, where the encoded iteration mask data is used as the initial distribution mean.

The encoded iteration mask data is a row matrix, and the iteration encoding variables satisfy the multivariate normal distribution. Initialize various parameters, mainly including super-resolution lithography structural parameters - thickness of each film layer and dielectric constant size, etc., CMA-ES algorithm parameters - number of optimization variables D _m , distribution mean m, search step size σ, covariance Matrix C and population number λ, etc. Since the initial mask data is directly sampled from the target graphics, the initial value m ⁽⁰⁾ of the distribution mean is equal to the initial iteration mask data; the initialization of the covariance matrix is C=I _N*N , and I is the identity matrix; The population number λ=a+floor(b×log(N)), a∈N ⁺ , b∈N ⁺ . Different target graphics need to set different population numbers during optimization. The larger the population, the greater the possibility of finding the most suitable individual in each generation, which also means that optimization requires more computing resources and running time. Generally speaking, a takes 4 and b takes 3. For target graphics with a large number of pixels, the values of a and b can be increased but it is not recommended to decrease them.

This disclosure uses the transmittance distribution of the mask pattern, that is, the pixelated mask data, as an optimization parameter, encodes and decodes the mask data, and determines the final mask pattern through multiple iterations of the CMA-ES algorithm.

Based on the above embodiment, S31 includes: if the mask pattern corresponding to the initial mask data is a mask pattern that is symmetrical about the coordinate axis, encoding the mask data of the first quadrant in a column-by-point scanning manner as A row matrix to obtain the above-encoded iterative mask data; otherwise, encode all the initial mask data into a row matrix in a column-by-point scanning manner to obtain the above-encoded iterative mask data.

如果N为偶数，此行矩阵大小为

本公开在掩模优化过程中，掩模单位像素的大小满足实际加工的最小尺寸限制，对掩模图形进行全局编码，可以实现任意复杂掩模的OPC；特别地，对对称性掩模图形进行对称性编码，可以使优化变量成倍减少，大大提升优化速度。 This disclosure uses global coding to realize OPC of any complex graphics, and uses symmetry coding to realize OPC of symmetry masks faster. Encoding is to encode the transmittance value of the pixelated mask into a row matrix in a column-by-point scanning manner, thereby obtaining the encoded iterative mask data. For an asymmetric N×N pixelated mask, use global encoding to encode it, that is, start from the first row and first column, scan column by column, point by point, until the entire mask is transmitted through The rate values are encoded as a row matrix, and the size of this row matrix is 1×N ² ; for an N×N pixelated mask that is symmetrical about the coordinate axis (taking N as an odd number as an example), symmetry encoding is used to The encoding is to start from the first row and first column of the first quadrant and scan column by point until the transmittance values of the mask in the first quadrant are encoded into a row matrix. The size of this row matrix is

If N is an even number, the size of this row matrix is

In the mask optimization process of this disclosure, the size of the unit pixel of the mask meets the minimum size limit of actual processing, and the mask pattern is globally encoded, and OPC of any complex mask can be realized; in particular, the symmetrical mask pattern is Symmetry coding can double the optimization variables and greatly improve the optimization speed.

FIG. 3 schematically shows a flowchart of a method for performing iterative operations using an adaptive update strategy algorithm based on a covariance matrix in Embodiment S4 of the present disclosure.

As shown in Figure 3, the method of using iterative operations based on the covariance matrix adaptive update strategy algorithm includes:

In operation S41, the encoded iterative mask data is sampled and binarized using an adaptive update strategy algorithm based on a covariance matrix to obtain a first number of iterative mask data.

Optimization of the mask is achieved using multiple iterations of the CMA-ES algorithm. First, a new candidate solution is obtained by sampling from the multivariate normal distribution, and then the candidate solution is binarized, that is, the first number of iteration mask data is obtained.

In operation S42, a first number of iterative mask data are decoded, and a first number of imaging errors are calculated according to conditions of super-resolution lithography.

Binarize and decode (and mirror) the sampled next-generation candidate solutions to obtain multiple sets of next-generation mask data. The imaging errors are calculated for each set of mask data according to the calculation method in S2, that is, according to Currently, multiple sets of mask data and super-resolution lithography conditions are used to calculate the spatial light field intensity distribution in the photoresist. Then, the photoresist output pattern is obtained based on the spatial light field intensity distribution in the photoresist, and the photolithography results are calculated separately. The total number of pixel errors between the glue output graphics and the target graphics is used as the imaging error.

In operation S43, a second number of iterative mask data is selected from the first number of iterative mask data according to the first number of imaging errors.

According to the first number of imaging errors, that is, the size of the graphics error function value, some solutions with smaller graphics error function values, that is, better imaging performance, are selected as the second quantity of iterative mask data.

In operation S44, the next generation iteration mask data is updated according to the second number of iteration mask data.

Using the second number of iterative mask data with better imaging performance, weighted summation is used to obtain the next generation of iterative mask data.

In operation S45, the next generation iterative mask data is used as the updated distribution mean and the search step size and covariance matrix are updated. Repeat S41 to S45 for iterative calculations until mask data that meets the preset conditions is obtained, and the optical proximity effect is completed. correction.

Update the evolutionary path and search step size. The update of the search step size is calculated by comparing the evolutionary path value and the expected length. The covariance matrix is updated through the rank-1 and rank-μ methods. The rank-1 update mechanism is obtained using the evolutionary path. The accumulated information between generations, the rank-μ update mechanism can effectively use the information of the entire population to better estimate the optimal value of this generation. The mask in the disclosed method is a pixelated and binary graphic, which has a high degree of optimization freedom. At the same time, due to the algorithm's continuous updating of the global solution search space and search step size, it can be much more efficient than other heuristic algorithms. Quickly find the direction of variable optimization, which greatly improves optimization efficiency.

Based on the above embodiment, S42 includes: if the mask pattern corresponding to the initial mask data is a mask pattern symmetrical about the coordinate axis, decode the first number of iterative mask data, and perform a mirroring operation to obtain the entire The mask data of the mask pattern; otherwise, decode the first number of iteration mask data to obtain the mask data of the entire mask pattern.

When the mask pattern corresponding to the initial mask data is a mask pattern that is symmetrical about the coordinate axis, the mask data in the iterative calculation process only uses the mask data of the first quadrant, so a mirror operation is required after decoding to obtain the updated changes. The mask data of the entire mask pattern. It should be noted that during the entire iteration process, except for the row matrix data obtained after encoding, the rest of the iterative mask data is two-dimensional matrix data.

On the basis of the above embodiment, S43 to S44 include: arranging the first number of imaging errors in ascending order; selecting the iterative mask data corresponding to the first second number of imaging errors, and performing a weighted sum on them to obtain the next generation iterative mask. module data.

μ个权重依次递减并使得所有权重之和为1，计算公式为

In each generation, the operations of sampling, decoding, and calculating the graphics error function value are repeated λ times, where λ is the aforementioned population number, and the obtained λ imaging errors, that is, the graphics error function values, are arranged in ascending order. Take the distribution of the better solutions corresponding to the first μ graphics error function values, and perform a weighted sum on them to obtain the next generation of iterative mask data. This iterative mask data is also the updated distribution mean. normally,

The μ weights decrease in sequence so that the sum of all weights is 1. The calculation formula is:

Based on the above embodiment, S45 includes: calculating and updating the search step size according to the accumulation of evolutionary paths; using rank-1 and rank-μ update mechanisms to update the covariance matrix according to the evolutionary paths.

其中有效变化量

g表示迭代代数，σ表示搜索步长。搜索步长的更新是通过比较该路径值和期望长度E||N(0，I)||来计算的，

更新后的搜索步长为

其中搜索步长的时间常数

搜索步长的阻尼因子

In order to avoid losing symbolic information during calculation, the concept of evolutionary path is introduced. The calculation formula of the evolutionary path of each generation search step is:

Among them, the effective change amount

g represents the iteration algebra, and σ represents the search step size. The update of the search step is calculated by comparing this path value with the expected length E||N(0,I)||,

The updated search step size is

where the time constant of the search step is

Damping factor for search steps

其中

协方差矩阵的累计时间常数为

用rank-1和rank-μ更新机理来更新协方差矩阵，

rank-1更新机理是使用进化路径来获得代与代之间的积累信息，其学习因子的计算公式为

rank-μ更新机理能够有效的利用整个种群的信息去更好的估计本代的最优值，其学习因子的计算公式为

The formula for calculating the evolutionary path of the covariance matrix is

in

The cumulative time constant of the covariance matrix is

Use rank-1 and rank-μ update mechanisms to update the covariance matrix,

The rank-1 update mechanism uses evolutionary paths to obtain accumulated information between generations. The calculation formula of its learning factor is

The rank-μ update mechanism can effectively use the information of the entire population to better estimate the optimal value of this generation. The calculation formula of its learning factor is:

Based on the above embodiment, S45 also includes: if the current imaging error meets the preset threshold condition or the number of iterations is greater than the maximum number of iterations condition, the current iteration mask data is the mask data that satisfies the preset conditions, and optical proximity is completed. Effect correction.

Determine whether the graphics error function value of the current mask data is less than the preset threshold condition, or whether the number of iterations exceeds the maximum number of iterations. When it is less than the threshold or the number of iterations is greater than the maximum number of iterations, the current mask data is considered to be the optimized mask data, the pixelated optical proximity effect correction operation is completed, and the final mask pattern is output based on the current mask data; otherwise, repeat Operations S41 to S45 are performed until the iteration stop condition is met. The value of the imaging performance threshold set by the method of the present disclosure needs to be selected based on the size of the target mask and the complexity of the target graphics. This disclosure sacrifices a certain amount of mask complexity and optimization time, and achieves better convergence effects through multiple iterations.

The present disclosure provides a method for super-resolution photolithography mask optical proximity effect correction based on a covariance matrix adaptive update strategy. Without using gradient information, by sampling in a multivariate normal distribution and using good adaptability Individuals update the mask pattern, and continue to converge until the optimal mask structure is found through the update of the search step size and covariance matrix, thereby correcting the optical proximity effect and obtaining a photoresist output pattern that is closer to the target pattern; This method can not only realize the optical proximity effect correction of arbitrary graphics, but also use the symmetry encoding mask to exponentially reduce the optimization variables, greatly improving the optimization speed.

4 schematically illustrates a flow chart of applying pixelated optical proximity effect correction to a super-resolution lithography method according to an embodiment of the present disclosure. The super-resolution lithography method includes:

S101, obtain pixelated initial mask data according to the target graphic;

S102, calculate the imaging error between the photoresist output pattern and the target pattern based on the initial mask data and super-resolution lithography conditions;

S103, encode the initial mask data, initialize the structural parameters of super-resolution lithography, and the parameters of the covariance matrix adaptive update strategy algorithm;

S104, use an adaptive update strategy algorithm based on the covariance matrix to perform iterative operations until mask data that meets the preset conditions is obtained to complete the correction of the optical proximity effect; and output the final mask pattern;

S105, perform super-resolution lithography according to the final mask pattern.

That is, the final mask pattern is output based on the aforementioned pixelated optical proximity effect correction method applied to super-resolution lithography, and photolithography is performed based on the final mask pattern. The CMA-ES algorithm is used to optimize the initial mask data to achieve optical proximity effect correction suitable for super-resolution lithography. There is no need to establish a complete mathematical model of the super-resolution lithography system, and the pixelated mask can be achieved without solving the gradient. OPC, thereby improving optimization efficiency. Operations S101 to S104 correspond to the aforementioned operations S1 to S4, and will not be described again here.

The present disclosure will be further described below through specific embodiments. The above-mentioned pixelated optical proximity effect correction method applied to super-resolution lithography will be specifically described in the following embodiments. However, the following examples are only for illustrating the present disclosure, and the scope of the present disclosure is not limited thereto.

Specifically, the method of the present disclosure includes the following steps, as shown in Figure 5:

Step S01:

Determine the target graphic, obtain the pixelated initial mask data M(x, y) based on the target graphic, and rasterize it into equally spaced M×N pixelated grids (M and N can be the same or different , in the following steps, M=N is used as an example, and N is an odd number). Each pixel unit represents the transmittance of the mask at the current position. The value is 0 or 1, which represents the mask's opacity and light transmission respectively. Two states. The mask data of the first quadrant in the initial mask data is defined as the initial iteration mask data M′(x, y), whose size is

A constant threshold model is used to characterize the photoresist output pattern PI=I>tr, where I is the spatial light field intensity distribution and tr is the photoresist threshold. This disclosure characterizes the imaging error by a graphics error function value. The graphics error function F is defined as: the total number of pixel errors of the photoresist output pattern corresponding to the target pattern and the current mask data, that is, F=∑|PI{M}-TP |. Among them, M is the current binary mask data, PI is the photoresist output pattern corresponding to the mask data, and TP is the target pattern, both of which are N×N binary matrices. Therefore, the pattern error of each generation Function values are positive integers.

Step S02:

RI是处于区间[0，1]的连续值。 Based on the given super-resolution lithography structure, use RCWA, FDTD or FEM method to calculate the spatial light field intensity distribution, draw the spatial light field intensity distribution map corresponding to the initial mask data and the imaging pattern in the photoresist, calculate and save the initial pattern error function value. The Sig function is used to characterize the photoresist effect, I _aerial represents the spatial light field intensity distribution, and the imaging pattern in the photoresist is

RI is a continuous value in the interval [0, 1].

For conventional unpolarized light illumination, it can be approximated as the superposition of two incoherent transverse magnetic (TM) and transverse electric (TE) polarized plane waves. Therefore, the spatial light field intensity in the photoresist is the space after irradiation of the TE polarized and TM polarized light sources. The average value of the light field intensity superposition.

Step S03:

编码后的迭代掩模数据是一个行矩阵，使用对称性编码方式而不是全局编码方式使得优化变量成倍减小，大大提高了优化速度。 This step uses symmetry encoding mask data to conduct research on symmetry mask graphics. For a 4-fold symmetry mask pattern of size N×N (taking N as an odd number as an example), that is, only the target mask pattern in the first quadrant of the iteration is encoded,

The encoded iterative mask data is a row matrix. The use of symmetry encoding instead of global encoding reduces the optimization variables exponentially, greatly improving the optimization speed.

对称性编码方式的使用使得优化变量个数急剧减少。 Initialize various parameters, mainly including super-resolution lithography structural parameters - thickness of each film layer and dielectric constant size, etc., CMA-ES algorithm parameters - number of optimization variables D _m , distribution mean m, covariance matrix C and population number λ etc. _For ^a 4-fold symmetry mask pattern with size N for

The use of symmetry coding greatly reduces the number of optimization variables.

Step S04:

其中，g为迭代代数。CMA-ES算法通过在其中采样，产生不同的迭代掩模数据。 The iterative coding variables satisfy the multivariate normal distribution, expressed as

Among them, g is the iteration algebra. The CMA-ES algorithm generates different iterative mask data by sampling among them.

CMA-ES samples from a set of solutions that satisfy a certain multivariate normal distribution to obtain the solutions required for optimization, and then binarizes this set of solutions to obtain different iterative mask data in each generation. Specifically, the sampling process includes: first, generate the standard normal distribution vector z _k =randn(N, 1), then generate the solution y _k =BDz _k that satisfies the normal distribution with mean 0 and variance C, and finally obtain The solution of the normal distribution with mean m and variance C is x _k =m+σy _k . Among them, B and D are used to generate the covariance matrix C = BD ² B ^T , B is the orthogonal set of the covariance matrix eigenvectors, D is the arithmetic square root of the corresponding positive eigenvalues, and the initial values of both are Identity matrix.

Since the transmittance value obtained by sampling is continuous, a threshold of 0.5 is used for binarization processing. If the transmittance at this position is greater than 0.5, it is set to 1, otherwise it is 0. At this time, the obtained It is the encoded binary iterative mask data. Among them, each generation will generate λ iteration mask data.

再通过镜像操作得到整个掩模数据M。计算图形误差函数值需要整个掩模板的折射率分布情况，但实际上更新变化的只有第一象限的掩模数据，因此需要用解码和镜像的操作得到更新变化后的整个掩模数据。其中解码指的是将编码后的行矩阵再恢复为编码前迭代掩模大小的矩阵，即将

的行矩阵解码为

的矩阵；镜像操作就是利用矩阵的翻转和拼接，利用第一象限的信息得到整个掩模板的信息。利用RCWA计算获得该掩模数据对应的空间光场强度分布以及图形误差函数值，每一代会调用λ次图形误差函数，因此每一代会获得λ个图形误差函数值，对这些值进行升序排列，最小的图形误差函数值对应的掩模数据就是在当代适应性最好的掩模数据。 When calculating the graphics error function value, the iterative mask data needs to be decoded first.

Then the entire mask data M is obtained through the mirror operation. Calculating the graphics error function value requires the refractive index distribution of the entire mask plate, but in fact only the mask data in the first quadrant is updated and changed, so decoding and mirroring operations are required to obtain the updated and changed entire mask data. Decoding refers to restoring the encoded row matrix to the matrix of the iteration mask size before encoding, that is,

The row matrix of is decoded as

The matrix; the mirror operation is to use the flipping and splicing of the matrix to obtain the information of the entire mask using the information in the first quadrant. Use RCWA calculation to obtain the spatial light field intensity distribution and graphics error function value corresponding to the mask data. Each generation will call the graphics error function λ times, so each generation will obtain λ graphics error function values, and arrange these values in ascending order. The mask data corresponding to the smallest graphics error function value is the mask data with the best adaptability in the contemporary era.

Step S05:

Get the iterative mask data corresponding to the first μ graphics error function values after sorting, and perform weighted summation to update the distribution mean m of the next generation.

Step S06:

Update the evolution path and search step size, and update the covariance matrix through the rank-1 and rank-μ methods.

Step S07:

Determine whether the conditions for stopping the iteration are met. If the currently calculated imaging error function value meets the set threshold, or the number of iterations is greater than the maximum number of iterations, jump to step S08, otherwise go to step S04 to continue iterative optimization. The size of the threshold and the maximum number of iterations need to be adjusted according to different target graphics, and the optimization effect and the impact of running time need to be taken into consideration. Generally, the larger the number of populations in each generation, the greater the possibility of finding the optimal individual, but the total number of iterations required will increase and the running time will also become longer.

Step S08:

After the optimization is completed, the optimized final mask pattern and the final pattern error function value are output.

The following is a description of specific embodiments.

Example 1:

The super-resolution photolithography structure in this embodiment is shown as 1002 in Figure 1, in which the thickness of the mask (SiO ₂ +Cr) is set to 40 nm, the air spacer layer (Air) is 30 nm, the metal layer (Ag) is 20 nm, and the photoresist is (Pr)30nm.

Figure 6 schematically shows the comparison of the initial mask pattern M(x, y), the corresponding imaged pattern RI in the photoresist, and the imaged pattern outline in the photoresist before optimization and the target pattern outline in this embodiment. In this example, the spatial light field intensity distribution in the photoresist is the average value of the superposition of the spatial light field intensity after irradiation by TE polarized and TM polarized light sources, calculated using RCWA, in which the Fourier expansion series is 10.

Next, the super-resolution lithography imaging performance of the mask pattern was evaluated. 601 is the initial mask pattern, which is the target pattern. The white area represents the transparent part, and the black represents the non-transparent part. The key feature size is 90nm, the unit pixel is 10nm, and the entire mask size is 99×99; 602 represents 601 As a mask pattern, the pattern is imaged in the photoresist after the super-resolution lithography system. The photoresist factor is set to 80 and the photosensitivity threshold is set to 0.3; 603 is the difference between the contour of the imaged pattern in the photoresist and the contour of the target pattern before optimization. For comparison, the black dotted line is the outline of the target pattern, and the black solid line is the outline of the imaged pattern in the photoresist.

Figure 7 is a comparison diagram of the optimized mask pattern, the corresponding image pattern in the photoresist, and the contour of the image pattern in the optimized photoresist and the target pattern outline obtained through optimization using the disclosed method. 701 is the optimized mask pattern obtained by using the disclosed method, 702 is the image pattern in the photoresist after using 701 as the mask pattern through the super-resolution lithography system, 703 is the contour of the image pattern in the photoresist after optimization and Comparison of the outline of the target pattern, where the black dotted line is the outline of the target pattern, and the black solid line is the outline of the imaged pattern in the photoresist. It can be seen that the black dotted line and the black solid line almost overlap.

The calculated initial graphics error function value is 892, the threshold of imaging error is set to 30, the number of populations in the CMA-ES algorithm is 50, and the maximum number of iterations is 2000. According to steps S04 to S07, the mask data is updated, and finally the optimized mask pattern is obtained, and the optimized pattern error function value is 84.

Comparing Figures 6 and 7, it can be seen that the method of the present disclosure effectively compensates for the optical proximity effect in the super-resolution lithography system, and provides a mask pattern with excellent effects for the actual needs of super-resolution lithography.

Example 2:

The super-resolution photolithography structure in this embodiment is shown as 1001 in Figure 1, in which the thickness of the mask (SiO ₂ +Cr) is set to 40 nm, the air spacer layer (Air) is 50 nm, the metal layer (Ag) is 20 nm, and the photoresist is (Pr) 30nm, metal reflective layer (Ag) 50nm.

Figure 8 shows the mask pattern before and after optimization using the mask OPC method based on the CMA-ES algorithm of the present disclosure in the super-resolution lithography structure shown as 1001 in Figure 1, as well as the contours of the corresponding imaging pattern and target pattern in the photoresist. The comparison chart lists the before and after optimization of the two mask patterns. 801 and 805 are the initial mask patterns, which are the target patterns. 802 and 806 respectively represent the comparison between the imaged pattern outline in the photoresist and the target pattern outline using 801 and 805 as the mask pattern after the super-resolution lithography system; 803 and 807 For the optimized mask pattern obtained by using the disclosed method, 804 and 808 respectively represent the comparison between the contour of the imaged pattern in the optimized photoresist and the contour of the target pattern. Among them, the spatial light field intensity distribution is calculated using the FDTD method.

The unit pixel in this embodiment is 10 nm, the population numbers are 46 and 50 respectively, the photoresist factors are all 80, the photosensitivity thresholds are all 0.5, and the mask sizes are 50×100 and 99×99 respectively. In this embodiment, the feature sizes of the two mask patterns are 120nm and 130nm respectively. After the optimization of the present disclosure, the corresponding pattern error values of the two mask patterns were reduced from 100 and 802 to 5 and 389 respectively.

Embodiment three:

Figure 9 shows the mask pattern before and after optimization using the mask OPC method based on the CMA-ES algorithm of the present disclosure in the super-resolution lithography structure shown as 1002 in Figure 1, as well as the contours of the corresponding imaging pattern and target pattern in the photoresist. The comparison chart lists the conditions before and after optimization of the two mask patterns. 901 and 905 are the initial mask patterns, which are the target patterns. 902 and 906 respectively represent the comparison between the imaged pattern outline in the photoresist and the target pattern outline after the super-resolution lithography system using 901 and 905 as the mask pattern; 903 and 907 For the optimized mask pattern obtained by using the disclosed method, 904 and 908 respectively represent the comparison between the contour of the imaged pattern in the optimized photoresist and the contour of the target pattern. The spatial light field intensity distribution in the photoresist is the average value of the superposition of the spatial light field intensity after irradiation with TE polarization and TM polarization light sources. It is calculated using RCWA, in which the Fourier expansion series is 10.

The unit pixel in this embodiment is 10 nm, the population number is 250, the photoresist factor is 80, the photosensitivity thresholds are 0.2 and 0.25 respectively, and the mask size is 119×119. In this embodiment, the feature sizes of the two mask patterns are both 90nm. After the optimization of the present disclosure, the corresponding pattern error values of the two mask patterns were reduced from 1190 and 1006 to 288 and 244 respectively.

FIG. 10 schematically shows a block diagram of a pixelated optical proximity effect correction system applied to super-resolution lithography according to an embodiment of the present disclosure.

As shown in FIG. 10 , FIG. 10 schematically shows a block diagram of a pixelated optical proximity effect correction system applied to super-resolution lithography according to an embodiment of the present disclosure. The optical proximity effect correction system 1000 includes: a preprocessing module 1010, a calculation module 1020, an encoding module 1030, and an iterative operation module 1040.

The preprocessing module 1010 is used to obtain pixelated initial mask data according to the target graphics. According to an embodiment of the present disclosure, the preprocessing module 1010 may, for example, be used to perform step S1 described above with reference to FIG. 2, which will not be described again here.

The calculation module 1020 is used to calculate the imaging error between the photoresist output pattern and the target pattern according to the initial mask data and the conditions of super-resolution lithography. According to an embodiment of the present disclosure, the computing module 1020 may, for example, be used to perform step S2 described above with reference to FIG. 2, which will not be described again here.

The encoding module 1030 is used to encode the initial mask data, initialize the structural parameters of super-resolution lithography, and the parameters of the adaptive update strategy algorithm based on the covariance matrix. According to an embodiment of the present disclosure, the encoding module 1030 may, for example, be used to perform step S3 described above with reference to FIG. 2, which will not be described again here.

The iterative operation module 1040 is used to perform iterative operations using an adaptive update strategy algorithm based on the covariance matrix until mask data that meets preset conditions is obtained to complete the correction of the optical proximity effect. According to an embodiment of the present disclosure, the iterative operation module 1040 may be used, for example, to perform step S4 described above with reference to FIG. 2 , which will not be described again here.

It should be noted that any multiple of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure, or at least part of the functions of any multiple of them, can be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be split into multiple modules for implementation. Any one or more of the modules, sub-modules, units, and sub-units according to embodiments of the present disclosure may be at least partially implemented as hardware circuits, such as field programmable gate arrays (FPGAs), programmable logic arrays (PLA), System-on-a-chip, system-on-substrate, system-on-package, application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging circuits that can be implemented in hardware or firmware, or in a combination of software, hardware, and firmware Any one of these implementation methods or an appropriate combination of any of them. Alternatively, one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be at least partially implemented as a computer program module, and when the computer program module is executed, corresponding functions may be performed.

For example, any one of the preprocessing module 1010, the calculation module 1020, the encoding module 1030, and the iterative operation module 1040 can be combined and implemented in one module, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of the present disclosure, at least one of the preprocessing module 1010, the calculation module 1020, the encoding module 1030, and the iterative operation module 1040 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable A logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging circuits that can be implemented in hardware or firmware, or in It can be implemented in any one of the three implementation methods of software, hardware and firmware or in an appropriate combination of any of them. Alternatively, at least one of the preprocessing module 1010, the calculation module 1020, the encoding module 1030, and the iterative operation module 1040 can be at least partially implemented as a computer program module, and when the computer program module is run, corresponding functions can be performed.

Figure 11 schematically shows a block diagram of an electronic device suitable for implementing the above-described method according to an embodiment of the present disclosure. The electronic device shown in FIG. 11 is only an example and should not impose any limitations on the functions and scope of use of the embodiments of the present disclosure.

As shown in Figure 11, the electronic device 1100 described in this embodiment includes: a processor 1101, which can be loaded into a random access memory (RAM) according to a program stored in a read-only memory (ROM) 1102 or from a storage part 1108. )1103 to perform various appropriate actions and processes. Processor 1101 may include, for example, a general purpose microprocessor (eg, a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (eg, an application specific integrated circuit (ASIC)), among others. Processor 1101 may also include onboard memory for caching purposes. The processor 1101 may include a single processing unit or multiple processing units for performing different actions of the method flow according to embodiments of the present disclosure.

In the RAM 1103, various programs and data required for the operation of the system 1100 are stored. The processor 1101, ROM 1102 and RAM 1103 are connected to each other through a bus 1104. The processor 1101 performs various operations according to the method flow of the embodiment of the present disclosure by executing programs in the ROM 1102 and/or RAM 1103. Note that the program may also be stored in one or more memories other than ROM 1102 and RAM 1103. The processor 1101 may also perform various operations according to the method flow of embodiments of the present disclosure by executing programs stored in one or more memories.

According to embodiments of the present disclosure, the electronic device 1100 may further include an input/output (I/O) interface 1105 that is also connected to the bus 1104 . System 1100 may also include one or more of the following components connected to I/O interface 1105: an input portion 1106 including a keyboard, mouse, etc.; including a device such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc.; and a speaker. an output section 1107, etc.; a storage section 1108 including a hard disk, etc.; and a communication section 1109 including a network interface card such as a LAN card, a modem, etc. The communication section 1109 performs communication processing via a network such as the Internet. Driver 1110 is also connected to I/O interface 1105 as needed. Removable media 1111, such as magnetic disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on the drive 1110 as needed, so that a computer program read therefrom is installed into the storage portion 1108 as needed.

According to embodiments of the present disclosure, the method flow according to the embodiments of the present disclosure may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product including a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the method illustrated in the flowchart. In such embodiments, the computer program may be downloaded and installed from the network via communication portion 1109 and/or installed from removable media 1111 . When the computer program is executed by the processor 1101, the above-described functions defined in the system of the embodiment of the present disclosure are performed. According to embodiments of the present disclosure, the systems, devices, devices, modules, units, etc. described above may be implemented by computer program modules.

Embodiments of the present disclosure also provide a computer-readable storage medium. The computer-readable storage medium may be included in the device/device/system described in the above embodiments; it may also exist separately without being assembled into the device/system. in equipment/devices/systems. The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed, the pixelation optical proximity effect correction method applied to super-resolution lithography according to the embodiment of the present disclosure is implemented.

According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, but is not limited to, portable computer disks, hard disks, random access memory (RAM), and read-only memory (ROM). , erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include one or more memories other than ROM 1102 and/or RAM 1103 and/or ROM 1102 and RAM 1103 described above.

Embodiments of the present disclosure also include a computer program product including a computer program containing program code for performing the method illustrated in the flowchart. When the computer program product is run in the computer system, the program code is used to cause the computer system to implement the pixelation optical proximity effect correction method applied to super-resolution lithography provided by the embodiments of the present disclosure.

When the computer program is executed by the processor 1101, the above-described functions defined in the system/device of the embodiment of the present disclosure are performed. According to embodiments of the present disclosure, the systems, devices, modules, units, etc. described above may be implemented by computer program modules.

In one embodiment, the computer program can rely on tangible storage media such as optical storage devices and magnetic storage devices. In another embodiment, the computer program can also be transmitted and distributed in the form of a signal on a network medium, and downloaded and installed through the communication part 1109, and/or installed from the removable medium 1111. The program code contained in the computer program can be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

In such embodiments, the computer program may be downloaded and installed from the network via communication portion 1109 and/or installed from removable media 1111 . When the computer program is executed by the processor 1101, the above-described functions defined in the system of the embodiment of the present disclosure are performed. According to embodiments of the present disclosure, the systems, devices, devices, modules, units, etc. described above may be implemented by computer program modules.

According to the embodiments of the present disclosure, the program code for executing the computer program provided by the embodiments of the present disclosure may be written in any combination of one or more programming languages. Specifically, high-level procedural and/or object-oriented programming may be utilized. programming language, and/or assembly/machine language to implement these computational procedures. Programming languages include, but are not limited to, programming languages such as Java, C++, python, "C" language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In situations involving remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device, such as provided by an Internet service. (business comes via Internet connection).

It should be noted that each functional module in various embodiments of the present disclosure can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or software function modules. Integrated modules can be stored in a computer-readable storage medium if they are implemented in the form of software function modules and sold or used as independent products. Based on this understanding, the technical solution of the present disclosure is essentially or contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more logic functions that implement the specified executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown one after another may actually execute substantially in parallel, or they may sometimes execute in the reverse order, depending on the functionality involved. It will also be noted that each block in the block diagram or flowchart illustration, and combinations of blocks in the block diagram or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or may be implemented by special purpose hardware-based systems that perform the specified functions or operations. Achieved by a combination of specialized hardware and computer instructions.

Those skilled in the art will understand that features recited in various embodiments and/or claims of the present disclosure may be combined and/or combined in various ways, even if such combinations or combinations are not explicitly recited in the present disclosure. In particular, various combinations and/or combinations of features recited in the various embodiments and/or claims of the disclosure may be made without departing from the spirit and teachings of the disclosure. All such combinations and/or combinations fall within the scope of this disclosure.

While the present disclosure has been shown and described with reference to specific exemplary embodiments thereof, it will be understood by those skilled in the art that other modifications may be made without departing from the spirit and scope of the disclosure as defined by the appended claims and their equivalents. The present disclosure is subject to various changes in form and detail. Therefore, the scope of the present disclosure should not be limited to the above-described embodiments, but should be determined not only by the appended claims but also by the equivalents of the appended claims.

Claims (16)

A pixelated optical proximity effect correction method applied to super-resolution lithography, which is characterized by including: S1, obtain the pixelated initial mask data according to the target graphics; S2, calculate the imaging error between the photoresist output pattern and the target pattern according to the initial mask data and the conditions of super-resolution lithography; S3, encode the initial mask data, and initialize the structural parameters of the super-resolution lithography and the parameters of the covariance matrix adaptive update strategy algorithm; S4: Use the covariance matrix-based adaptive update strategy algorithm to perform iterative operations until mask data that meets the preset conditions is obtained to complete the correction of the optical proximity effect. The pixelated optical proximity effect correction method applied to super-resolution lithography according to claim 1, wherein the S1 includes: S11, obtain the initial mask pattern according to the target pattern; S12: Perform pixelization processing on the initial mask pattern to obtain the pixelated initial mask data. The pixelated optical proximity effect correction method applied to super-resolution lithography according to claim 1, wherein the S2 includes: S21, calculate the spatial light field intensity distribution in the photoresist according to the initial mask data and the conditions of super-resolution lithography; S22: Obtain a photoresist output pattern according to the spatial light field intensity distribution in the photoresist, and calculate the total number of pixel errors between the photoresist output pattern and the target pattern as the imaging error. The pixelated optical proximity effect correction method applied to super-resolution lithography according to claim 1, wherein the S3 includes: S31, encode the initial mask data into a row matrix in a column-by-point scanning manner, and obtain the encoded iterative mask data; S32. Initialize the structural parameters of the super-resolution lithography. The structural parameters at least include the thickness and dielectric constant of each film layer; S33. Initialize the parameters of the adaptive update strategy algorithm based on covariance matrix. The parameters at least include the number of optimization variables, distribution mean, search step size, covariance matrix and population number, where the encoded iterative mask The data is taken as the initial distribution mean. The pixelated optical proximity effect correction method applied to super-resolution lithography according to claim 4, wherein the S31 includes: If the mask pattern corresponding to the initial mask data is a mask pattern that is symmetrical about the coordinate axis, then the mask data in the first quadrant is encoded into a row matrix by scanning column by point, and the encoded iterative mask data; Otherwise, all the initial mask data is encoded into a row matrix in a column-by-point scanning manner to obtain the encoded iterative mask data. The pixelated optical proximity effect correction method applied to super-resolution lithography according to claim 5, wherein the S4 includes: S41, use the covariance matrix-based adaptive update strategy algorithm to sample and binarize the encoded iterative mask data to obtain the first number of iterative mask data; S42, decode the first number of iterative mask data, and calculate the first number of imaging errors according to the conditions of the super-resolution lithography; S43. Select a second number of iterative mask data from the first number of iterative mask data according to the first number of imaging errors; S44, update and obtain the next generation iterative mask data according to the second number of iterative mask data; S45: Use the next-generation iteration mask data as the updated distribution mean and update the search step size and the covariance matrix. Repeat S41 to S45 to perform iterative calculations until a distribution that satisfies the preset conditions is obtained. Mask data to complete the correction of optical proximity effect. The pixelated optical proximity effect correction method applied to super-resolution lithography according to claim 6, wherein the S42 includes: If the mask pattern corresponding to the initial mask data is a mask pattern that is symmetrical about the coordinate axis, decode the first number of iterative mask data and perform a mirroring operation to obtain a mask of the entire mask pattern. module data; Otherwise, the first number of iteration mask data is decoded, that is, the mask data of the entire mask pattern is obtained. The pixelated optical proximity effect correction method applied to super-resolution lithography according to claim 6, wherein the S43-S44 include: Arrange the first number of imaging errors in ascending order; Select the iterative mask data corresponding to the first second number of imaging errors, and perform a weighted sum on them to obtain the next generation iterative mask data. The pixelated optical proximity effect correction method applied to super-resolution lithography according to claim 6, wherein the S45 includes: Calculate and update the search step size based on the accumulation of evolutionary paths; According to the evolutionary path, the rank-1 and rank-μ update mechanisms are used to update the covariance matrix. The pixelated optical proximity effect correction method applied to super-resolution lithography according to claim 6, wherein the S45 further includes: If the current imaging error meets the preset threshold condition or the number of iterations is greater than the maximum number of iterations, the current iteration mask data is the mask data that meets the preset conditions, and the correction of the optical proximity effect is completed. The pixelated optical proximity effect correction method applied to super-resolution lithography according to claim 6, characterized in that the conditions of super-resolution lithography in S2 include the structure of super-resolution lithography, and the conditions of super-resolution lithography are A structure including a mask substrate, a mask, an air spacer layer, a metal transmissive layer, a photoresist, a metal reflective layer, and a substrate; or, The structure of the super-resolution lithography includes a mask substrate, a mask, an air spacer layer, a metal transmission layer, a photoresist and a base structure; or, The structure of the super-resolution lithography includes a mask substrate, a mask, an air spacer layer, a photoresist, a metal reflective layer and a base structure. A method for applying pixelated optical proximity effect correction to super-resolution lithography, which is characterized by including: S01, obtain the pixelated initial mask data based on the target graphics; S02, calculate the imaging error between the photoresist output pattern and the target pattern according to the initial mask data and the conditions of super-resolution lithography; S03, encode the initial mask data, and initialize the structural parameters of the super-resolution lithography and the parameters of the covariance matrix adaptive update strategy algorithm; S04, use the covariance matrix-based adaptive update strategy algorithm to perform iterative operations until mask data that meets the preset conditions is obtained to complete the correction of the optical proximity effect; and output the final mask pattern; S05, perform super-resolution lithography according to the final mask pattern. A pixelated optical proximity effect correction system applied to super-resolution lithography, which is characterized by including: The preprocessing module is used to obtain pixelated initial mask data based on the target graphics; A calculation module configured to calculate the imaging error between the photoresist output pattern and the target pattern according to the initial mask data and the conditions of super-resolution lithography; An encoding module, used to encode the initial mask data and initialize the structural parameters of the super-resolution lithography and the parameters of the covariance matrix adaptive update strategy algorithm; The iterative operation module is used to use the covariance matrix-based adaptive update strategy algorithm to perform iterative operations until mask data that meets the preset conditions is obtained to complete the correction of the optical proximity effect. An electronic device including: one or more processors; a storage device for storing one or more programs, Wherein, when the one or more programs are executed by the one or more processors, the one or more processors are caused to execute the method applied to super-resolution light according to any one of claims 1 to 11. An engraved pixelated optical proximity effect correction method. A computer-readable storage medium having executable instructions stored thereon. When executed by a processor, the instructions cause the processor to execute the pixelation optics applied to super-resolution lithography according to any one of claims 1 to 11. Proximity effect correction method. A computer program product, comprising a computer program, which when executed by a processor implements the pixelated optical proximity effect correction method applied to super-resolution lithography according to any one of claims 1 to 11.

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