CN117518726A - Correction method of optical proximity effect - Google Patents

Correction method of optical proximity effect Download PDF

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Publication number
CN117518726A
CN117518726A CN202210911091.9A CN202210911091A CN117518726A CN 117518726 A CN117518726 A CN 117518726A CN 202210911091 A CN202210911091 A CN 202210911091A CN 117518726 A CN117518726 A CN 117518726A
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optical proximity
test
patterns
test pattern
pattern
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罗连波
刘文东
丁丽华
程仁强
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Semiconductor Manufacturing International Shanghai Corp
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Semiconductor Manufacturing International Shanghai Corp
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    • G—PHYSICS
    • G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
    • G03F—PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR
    • G03F7/00—Photomechanical, e.g. photolithographic, production of textured or patterned surfaces, e.g. printing surfaces; Materials therefor, e.g. comprising photoresists; Apparatus specially adapted therefor
    • G03F7/70—Microphotolithographic exposure; Apparatus therefor
    • G03F7/70425—Imaging strategies, e.g. for increasing throughput or resolution, printing product fields larger than the image field or compensating lithography- or non-lithography errors, e.g. proximity correction, mix-and-match, stitching or double patterning
    • G03F7/70433—Layout for increasing efficiency or for compensating imaging errors, e.g. layout of exposure fields for reducing focus errors; Use of mask features for increasing efficiency or for compensating imaging errors
    • G03F7/70441—Optical proximity correction [OPC]
    • G—PHYSICS
    • G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
    • G03F—PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR
    • G03F1/00—Originals for photomechanical production of textured or patterned surfaces, e.g., masks, photo-masks, reticles; Mask blanks or pellicles therefor; Containers specially adapted therefor; Preparation thereof
    • G03F1/36—Masks having proximity correction features; Preparation thereof, e.g. optical proximity correction [OPC] design processes

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Exposure And Positioning Against Photoresist Photosensitive Materials (AREA)

Abstract

A method for correcting optical proximity effects, comprising: providing an initial optical proximity correction model; providing a plurality of test patterns; grouping the test patterns to obtain a plurality of test pattern groups, wherein each test pattern group comprises at least one test pattern; acquiring any number of test patterns in each group as final test patterns; obtaining a photoetching pattern according to the final test pattern, wherein the photoetching pattern corresponds to the final test pattern one by one; obtaining fitting parameters according to the photoetching graph; and fitting the initial optical proximity correction model according to the fitting parameters to obtain an optical proximity correction model. The method improves efficiency.

Description

Correction method of optical proximity effect
Technical Field
The invention relates to the technical field of semiconductors, in particular to a correction method of an optical proximity effect.
Background
Model-based optical proximity correction (Model based OPC) is widely used in advanced node lithography processes, particularly on layers where there are a wide variety of patterns. The current model for optical proximity correction comprises two parts, namely an optical part and a photoresist part. The former is a purely physical model, which can be derived from a derivation formulated by global settings. The latter involves complex chemical reactions of the photoresist and is difficult to describe by an accurate model or formula. Currently, the photoresist model can only be obtained by using an empirical model and parameters, collecting a large number of electron microscope measurement data of test patterns from a wafer and fitting the electron microscope measurement data through a mathematical formula. This process requires a lot of time, manpower and material resources.
Therefore, the success of the optical proximity correction model for the optical proximity effect is largely determined by the choice of test patterns on the wafer, taking into account the time cost and the accuracy of the model.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a correction method of the optical proximity effect, so as to obtain an optical proximity effect optical proximity correction model with higher precision under the condition of considering efficiency.
In order to solve the above technical problems, the present invention provides a method for correcting optical proximity effect, including: providing an initial optical proximity correction model; providing a test layout, wherein the test layout is provided with a plurality of test patterns; grouping the test patterns to obtain a plurality of test pattern groups, wherein each test pattern group comprises at least one test pattern; obtaining photoetching patterns corresponding to test patterns in a test pattern group, wherein the photoetching patterns correspond to the test patterns one by one; obtaining fitting parameters according to the photoetching graph; fitting the initial optical proximity correction model according to the fitting parameters to obtain an optical proximity correction model; and performing optical proximity effect correction processing based on the optical proximity correction model.
Optionally, the method for grouping the plurality of test patterns to obtain the plurality of test pattern groups includes a machine learning big data analysis method.
Optionally, the method for grouping the plurality of test patterns to obtain the plurality of test pattern groups includes: providing a constant threshold model; acquiring a plurality of characteristic parameters of each test pattern according to a constant threshold model, and acquiring a characteristic parameter matrix, wherein the characteristic parameter matrix consists of characteristic vectors of each test pattern, and the characteristic vectors of each test pattern consist of a plurality of characteristic parameters; performing dimension reduction compression processing on the characteristic parameter matrix by adopting a principal component analysis method to obtain a dimension reduction matrix; and clustering the dimension reduction matrix by adopting a k-means clustering algorithm to obtain a plurality of test pattern groups, wherein any test pattern group is internally provided with a plurality of test patterns.
Optionally, the constant threshold model is:
wherein C0, C1 … C9 are parameters to be fitted, I max For maximum light intensity at any point of the graph, I min For the minimum light intensity at any point of the graph, curvature is the Curvature at any point of the graph, and Slope is the Slope at any point of the graph.
Optionally, the fitting parameters include: critical dimensions of the lithographic patterns and spacing between the lithographic patterns.
Optionally, the method for obtaining the optical proximity correction model includes: fitting parameters C0 and C1 … C9 of the constant threshold model according to the critical dimension of the photoetching patterns and the spacing between the photoetching patterns to obtain a constant threshold VT; performing simulated exposure on the final test pattern according to the constant threshold VT to obtain a simulated exposure pattern; and adjusting the initial optical proximity correction model according to the deviation between the simulated exposure pattern and the photoetching pattern to obtain an optical proximity correction model.
Optionally, the plurality of characteristic parameters of each test pattern includes at least one of: CD. pitch, imax, imin, contrast, slope and NILS, wherein CD is the critical dimension of the test patterns, pitch is the distance between the test patterns, imax is the maximum light intensity at any point of the test patterns, imin is the minimum light intensity at any point of the test patterns, contrast is the Contrast, slope is the Slope at any point of the test patterns, and NILS is the normalized log Slope of the test patterns.
Optionally, the characteristic parameter matrix
Optionally, the optical proximity effect correction process based on the optical proximity correction model includes: providing a layout to be corrected; and carrying out optical proximity correction on the layout to be corrected according to the optical proximity correction model to obtain a corrected layout.
Optionally, obtaining the lithography pattern corresponding to the test pattern in the test pattern group includes: providing a wafer, wherein the wafer is provided with a photoresist layer; and exposing and developing the test patterns in the test pattern group on the photoresist layer to obtain the photoetching patterns.
Optionally, the method for obtaining fitting parameters according to the lithography graph includes: and measuring the photoetching pattern to obtain fitting parameters.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
according to the technical scheme, the test patterns are grouped, the photoetching patterns corresponding to the test patterns in the test pattern group are obtained, then the fitting parameters are obtained and the fitting of the model is carried out, the selection mode of the test patterns is optimized, the test patterns for model fitting, which are simultaneously compatible with the model running time and the model coverage, are quantitatively selected, the number of the test patterns is reduced, the running time is shortened, and the efficiency is improved. Meanwhile, fitting processing of the model is carried out based on fitting parameters corresponding to the lithography patterns obtained by the test patterns, so that the fitting processing of the model is more targeted, the accuracy and the precision of an optical proximity correction model obtained after fitting can be improved, and the accuracy of correcting the optical proximity effect can be improved.
Drawings
Fig. 1 and fig. 2 are flow charts of a method for correcting an optical proximity effect according to an embodiment of the invention.
Detailed Description
As background art, the success or failure of the optical proximity correction model is largely determined by the choice of test patterns on the wafer.
Specifically, the current choice of test patterns is largely empirical: and designing a 1D or 2D graph and a graph close to a real chip according to the design rule. And selecting a pattern near the size specified by the design rule on the wafer for measurement. For example, the design rules specify that the pattern width is 40nm and the pitch is 120nm. The test pattern is selected to have a width of 35 nm to 45 nm and a pitch of 100 nm to 140 nm. In order to ensure the coverage of the model, a plurality of test patterns are selected as much as possible under the condition of ensuring proper time cost.
This method of empirically selecting a pattern has no criteria that can be quantified and optimized, and has three problems: 1. the less the pattern is selected, the coverage of the final model is insufficient; 2. the time cost of training the model is increased due to the fact that the number of patterns is large, and the model is easy to overload; 3. graphics can only be grouped by geometric size and cannot truly reflect the inherent features of graphics.
In order to solve the problems, the technical scheme of the invention provides a correction method of optical proximity effect, which optimizes the selection mode of test patterns, quantitatively selects the test patterns for model fitting, which simultaneously gives consideration to the model running time and the model coverage, reduces the number of the test patterns, shortens the running time and improves the efficiency.
In order to make the above objects, features and advantages of the present invention more comprehensible, embodiments accompanied with figures are described in detail below.
Fig. 1 and fig. 2 are flow charts of a method for correcting an optical proximity effect according to an embodiment of the invention.
Referring to fig. 1, the optical proximity correction method includes:
step S10: an initial optical proximity correction model is provided.
Step S20: several test patterns are provided.
Step S30: grouping the test patterns to obtain a plurality of test pattern groups, wherein each test pattern group comprises at least one test pattern.
Step S40: and obtaining the photoetching patterns corresponding to the test patterns in the test pattern group, wherein the photoetching patterns correspond to the test patterns one by one.
Step S50: and obtaining fitting parameters according to the photoetching graph.
Step S60: and fitting the initial optical proximity correction model according to the fitting parameters to obtain the optical proximity correction model.
Step S70: and performing optical proximity effect correction processing based on the optical proximity correction model.
The method in the embodiment optimizes the selection mode of the test patterns, quantitatively selects the test patterns for model fitting, which simultaneously gives consideration to the model running time and the model coverage, reduces the number of the test patterns, shortens the running time and improves the efficiency.
Next, each step is explained analytically.
With continued reference to fig. 1, step S10 is performed: an initial optical proximity correction model is provided.
The initial optical proximity correction model is an initial optical proximity correction model for correcting the optical proximity effect, and model parameters of the initial optical proximity correction model are initial parameters and cannot be directly used for correcting the optical proximity effect.
In this embodiment, after collecting data of a plurality of simulated exposure patterns and lithography patterns of the test patterns, the initial optical proximity correction model may be fitted according to the collected data, so that the model parameters obtain the best fitting parameters, and the corresponding model parameters are optimal, so as to obtain the optical proximity correction model, and further, the optical proximity effect correction may be performed based on the optical proximity correction model.
With continued reference to fig. 1, step S20 is performed: several test patterns are provided.
Wherein the test pattern is the aforementioned collected simulated exposure pattern. The test patterns participate in the fitting process of the optical proximity correction model, and each test pattern has a plurality of characteristic parameters.
With continued reference to fig. 1, step S30 is performed: grouping the test patterns to obtain a plurality of test pattern groups, wherein each test pattern group comprises at least one test pattern.
The method for grouping the test patterns to obtain the test pattern groups comprises a machine learning big data analysis method.
Specifically, referring to fig. 2, the method for grouping a plurality of test patterns to obtain a plurality of test pattern groups includes:
step S301: a constant threshold model is provided.
Step S302: and acquiring a plurality of characteristic parameters of each test pattern according to the constant threshold model, and acquiring a characteristic parameter matrix, wherein the characteristic parameter matrix consists of characteristic vectors of each test pattern, and the characteristic vectors of each test pattern consist of a plurality of characteristic parameters.
Step S303: and performing dimension reduction compression processing on the characteristic parameter matrix by adopting a principal component analysis method to obtain a dimension reduction matrix.
Step S304: and clustering the dimension reduction matrix by adopting a k-means clustering algorithm to obtain a plurality of test pattern groups, wherein any test pattern group is internally provided with a plurality of test patterns.
With continued reference to fig. 2, step S301 is performed: a constant threshold model is provided.
Wherein the constant threshold model may be expressed by the following formula:
VT＝C 0 +C 1 I max +C 2 I min +C 3 ×Slope+C 4 ×Curvature+C 5 (I max ) 2 +C 6 (I min ) 2 +C 7 (Slope) 2 +C 8 (Curvature) 2 +C 9 I max I min +…
wherein C0, C1 … C9 are parameters to be fitted, I max For maximum light intensity at any point of the graph, I min For the minimum light intensity of any point of the graph, curvature is the Curvature of any point of the graph, slope is the Slope of any point of the graph, I max 、I min Curvatures and Slope can be obtained by calculation.
With continued reference to fig. 2, step S302 is performed: and acquiring a plurality of characteristic parameters of each test pattern according to the constant threshold model, and acquiring a characteristic parameter matrix, wherein the characteristic parameter matrix consists of characteristic vectors of each test pattern, and the characteristic vectors of each test pattern consist of a plurality of characteristic parameters.
In this embodiment, a plurality of characteristic parameters of each test pattern are obtained according to the constant threshold model VT, and at this time, the parameters C0, C1 … C9 to be fitted of the constant threshold model VT are already fitted, and at this time, the constant threshold model VT is a fixed model.
In this embodiment, the plurality of feature parameters of each test pattern includes at least one of: CD. pitch, I max 、I min Contrast, slope and NILS.
Wherein CD is the critical dimension of the test patterns, pitch is the spacing between the test patterns, I max To test the maximum light intensity at any point of the graph, I min For the minimum light intensity at any point of the test pattern, contrast is Contrast, slope is the Slope at any point of the test pattern, and NILS is the normalized log Slope of the test pattern.
In the present embodiment, the contrast ratioNormalized log slope +.>
Feature vector of each test pattern
Characteristic parameter matrix
By defining feature vectors of test patternsThe characteristic parameter matrix H is composed to accurately represent a test pattern from the geometric space and the imaging space.
With continued reference to fig. 2, step S303 is performed: and performing dimension reduction compression processing on the characteristic parameter matrix by adopting a principal component analysis method to obtain a dimension reduction matrix.
The principal component analysis method (Principal Component Analysis, abbreviated as PCA) is a data analysis method, which is used for reducing the dimension of high-dimension data and extracting the principal characteristic components of the data.
In this embodiment, the principal component analysis method reduces the high-dimensional feature parameter matrix H into a low-dimensional dimension matrixDimension-reducing matrix->The data of the characteristic parameter matrix H is basically maintained, and the dimension-reducing matrix is +.>The dimension of the (c) becomes smaller, and the (c) is convenient for subsequent data processing, so that the subsequent calculation amount is reduced.
With continued reference to fig. 2, step S304 is performed: and clustering the dimension reduction matrix by adopting a k-means clustering algorithm to obtain a plurality of test pattern groups, wherein any test pattern group is internally provided with a plurality of test patterns.
The K-means clustering algorithm (K-means clustering algorithm, K-means for short) is an iterative solution clustering analysis algorithm. The method comprises the steps of dividing data into K groups, randomly selecting K objects as initial cluster centers, calculating the distance between each object and each seed cluster center, and distributing each object to the cluster center closest to the object. The above process is repeated until no more change occurs in the cluster center.
In the embodiment, a k-means clustering algorithm is adopted for the dimension reduction matrixClustering is carried out, k test graph groups are obtained, the k test graph groups are respectively marked as 1,2,3 and … k, the value range of i is 1 to k, and k is a natural number larger than 1.
K-means clustering algorithm pair dimension-reducing matrixClustering is carried out to obtain k test pattern groups, namely, a plurality of test patterns are divided into k test pattern groups, the test patterns in each test pattern group are patterns of the same type, and any test pattern in each test pattern group can represent the same type of test patterns in the test pattern group.
With continued reference to fig. 1, step S40 is performed: and obtaining the photoetching patterns corresponding to the test patterns in the test pattern group, wherein the photoetching patterns correspond to the test patterns one by one.
Obtaining a lithography pattern corresponding to a test pattern in the test pattern group, comprising: acquiring any number of test patterns in each group as final test patterns; and obtaining the photoetching patterns according to the final test patterns, wherein the photoetching patterns correspond to the final test patterns one by one.
Any number of test patterns in each group can represent the same type of test patterns in the test pattern group, any number of test patterns in each group are obtained to be final test patterns, so that the test patterns are more uniformly selected, and more test patterns can be selected as much as possible under the condition of ensuring proper time cost.
Specifically, the method for obtaining the photoetching pattern according to the final test pattern comprises the following steps: providing a wafer, wherein the wafer is provided with a photoresist layer; and exposing and developing the photoresist layer according to the final test pattern to obtain the photoetching pattern.
With continued reference to fig. 1, step S50 is performed: and obtaining fitting parameters according to the photoetching graph.
Specifically, the fitting parameters include: critical dimensions of the lithographic patterns and spacing between the lithographic patterns.
Wherein, the critical dimension refers to the dimension of the photoetching pattern in a first direction or a second direction, and the first direction and the second direction are mutually perpendicular; the pitch between the photolithography patterns refers to the distance between adjacent photolithography patterns in the first direction or the second direction.
In this embodiment, the determination of the critical dimension of the lithography pattern and the pitch between the lithography patterns may be different according to the application scenario, the test patterns in the test pattern group, the lithography patterns, and the like, and may be determined based on the actual application scenario.
In this embodiment, the method for obtaining fitting parameters according to a photolithography pattern includes: and measuring the photoetching pattern to obtain fitting parameters.
Specifically, after the corresponding lithography pattern is obtained based on the test pattern, the lithography pattern may be measured by an electron microscope, so as to obtain fitting parameters of the corresponding lithography pattern, that is, C0, C1 … C9, and the like.
With continued reference to fig. 1, step S60 is performed: and fitting the initial optical proximity correction model according to the fitting parameters to obtain the optical proximity correction model.
Fitting the initial optical proximity correction model according to the fitting parameters, and obtaining the optical proximity correction model comprises the following steps: fitting parameters C0 and C1 … C9 of the constant threshold model according to the critical dimension of the photoetching patterns and the spacing between the photoetching patterns to obtain a constant threshold VT; performing simulated exposure on the final test pattern according to the constant threshold VT to obtain a simulated exposure pattern; and adjusting the initial optical proximity correction model according to the deviation between the simulated exposure pattern and the photoetching pattern to obtain an optical proximity correction model.
Fitting parameters C0, C1 … C9 of the constant threshold model according to the critical dimensions of the lithography patterns and the spacing between the lithography patterns, and obtaining the constant threshold VT comprises the following steps: providing initial values of C0, C1 … C9, wherein the initial values of C0, C1 … C9 have respective value intervals; substituting initial values of C0 and C1 … C9 and fitting parameters of the measured lithography patterns into a constant threshold model to obtain an initial constant threshold; performing simulated exposure on the test pattern, and intercepting the pattern with the light intensity larger than a constant threshold value as a simulated exposure pattern; obtaining a root mean square value between the sizes of the simulated exposure pattern and the photoetching pattern, wherein the smaller the root mean square value is, the closer the simulated exposure pattern is to the photoetching pattern is, and the values of C0 and C1 … C9 are determined; if the root mean square value is larger than the preset value, re-taking the values C0 and C1 … C9 in the taking interval, and repeating the processes until the root mean square value between the simulated exposure pattern and the photoetching pattern size is in the preset range, and completing the fitting process of the C0 and C1 … C9.
In the process of acquiring the optical proximity correction model, the selection mode of the test patterns is optimized, the test patterns for model fitting, which are simultaneously compatible with the model running time and the model coverage, are quantitatively selected, the number of the test patterns is reduced, the running time is shortened, and the efficiency is improved. By analyzing the feature space (geometry and imaging space) of the test pattern, the number of patterns for model test can be compressed to 20% and the running time can be reduced by 30%. Meanwhile, fitting processing of the model is carried out based on fitting parameters corresponding to the lithography patterns obtained by the test patterns, so that the fitting processing of the model is more targeted, the accuracy and the precision of an optical proximity correction model obtained after fitting can be improved, and the accuracy of correcting the optical proximity effect can be improved.
With continued reference to fig. 1, step S70 is performed: and carrying out optical proximity effect correction processing based on the optical proximity correction model.
And performing optical proximity effect correction processing based on the optical proximity correction model, wherein the optical proximity effect correction processing comprises the following steps: providing a layout to be corrected; and carrying out optical proximity correction on the layout to be corrected according to the optical proximity correction model to obtain a corrected layout.
Specifically, the optical proximity correction model may determine a pattern deviation between an exposure pattern and a lithography pattern of each pattern in the layout to be corrected based on the layout to be corrected, and then perform correction of the layout to be corrected, such as zooming in, zooming out, panning, and the like, based on the pattern deviation, so as to obtain a corrected layout.
In the above embodiment, the number of the test patterns is enough according to the optical proximity correction model obtained by fitting the test patterns, and the types of the test patterns are uniformly covered, so that the obtained optical proximity correction model has high accuracy and good optical proximity effect correction effect.
Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the invention, and the scope of the invention should be assessed accordingly to that of the appended claims.

Claims (11)

1. A method for correcting optical proximity effects, comprising:
providing an initial optical proximity correction model;
providing a plurality of test patterns;
grouping the test patterns to obtain a plurality of test pattern groups, wherein each test pattern group comprises at least one test pattern;
obtaining photoetching patterns corresponding to test patterns in a test pattern group, wherein the photoetching patterns correspond to the test patterns one by one;
obtaining fitting parameters according to the photoetching graph;
fitting the initial optical proximity correction model according to the fitting parameters to obtain an optical proximity correction model;
and carrying out optical proximity effect correction processing based on the optical proximity correction model.
2. The method for correcting optical proximity effects according to claim 1, wherein the method for grouping the plurality of test patterns to obtain the plurality of test pattern groups comprises a machine learning big data analysis method.
3. The method for correcting optical proximity effects according to claim 1, wherein the step of grouping the plurality of test patterns to obtain a plurality of test pattern groups comprises: providing a constant threshold model; acquiring a plurality of characteristic parameters of each test pattern according to the constant threshold model, and acquiring a characteristic parameter matrix, wherein the characteristic parameter matrix consists of characteristic vectors of each test pattern, and the characteristic vectors of each test pattern consist of a plurality of characteristic parameters; performing dimension reduction compression processing on the characteristic parameter matrix by adopting a principal component analysis method to obtain a dimension reduction matrix; and clustering the dimension reduction matrix by adopting a k-means clustering algorithm to obtain a plurality of test pattern groups, wherein any test pattern group is internally provided with a plurality of test patterns.
4. The method for correcting optical proximity effects according to claim 3, wherein the constant threshold model is:
wherein C0, C1 … C9 are parameters to be fitted, I max For maximum light intensity at any point of the graph, I min For the minimum light intensity at any point of the graph, curvature is the Curvature at any point of the graph, and Slope is the Slope at any point of the graph.
5. The method for correcting for optical proximity effects of claim 4 wherein the fitting parameters include: critical dimensions of the lithographic patterns and spacing between the lithographic patterns.
6. The method for correcting optical proximity effects according to claim 5, wherein the method for fitting the initial optical proximity correction model according to the fitting parameters to obtain an optical proximity correction model comprises: fitting parameters C0 and C1 … C9 of the constant threshold model according to the critical dimension of the photoetching patterns and the spacing between the photoetching patterns to obtain a constant threshold VT; performing simulated exposure on the final test pattern according to the constant threshold VT to obtain a simulated exposure pattern; and adjusting the initial optical proximity correction model according to the deviation between the simulated exposure pattern and the photoetching pattern to obtain an optical proximity correction model.
7. A method of modifying optical proximity effects as in claim 3 wherein the plurality of characteristic parameters of each of the test patterns comprises at least one of: CD. pitch, I max 、I min Contrast, slope and NILS, wherein CD is the key to the test patternSize, pitch is the spacing between test patterns, I max To test the maximum light intensity at any point of the graph, I min For the minimum light intensity at any point of the test pattern, contrast is Contrast, slope is the Slope at any point of the test pattern, and NILS is the normalized log Slope of the test pattern.
8. The method for correcting optical proximity effects as claimed in claim 7, wherein the characteristic parameter matrix
9. The method for correcting an optical proximity effect according to claim 1, wherein the correcting an optical proximity effect based on the optical proximity correction model includes: providing a layout to be corrected; and carrying out optical proximity correction on the layout to be corrected according to the optical proximity correction model to obtain a corrected layout.
10. The method for correcting optical proximity effects according to claim 1, wherein the step of obtaining the lithography pattern corresponding to the test pattern in the test pattern group comprises: providing a wafer, wherein the wafer is provided with a photoresist layer; and exposing and developing the test patterns in the test pattern group on the photoresist layer to obtain the photoetching patterns.
11. The method of correcting for optical proximity effects of claim 1 wherein the method of obtaining fitting parameters from the lithographic pattern comprises: and measuring the photoetching graph to obtain the fitting parameters.
CN202210911091.9A 2022-07-29 2022-07-29 Correction method of optical proximity effect Pending CN117518726A (en)

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