Model and its establishing method and system, compensation method, device and storage medium

Abstract

A model and its establishment method and system, a compensation method, equipment and a storage medium, the establishment method comprises the following steps: providing a test layout, including a test pattern, including a plurality of test points; obtaining the initial pattern density of each test point according to the test pattern; taking any test point as a feature point, obtaining the effective pattern density of the feature point according to the initial pattern density of each feature point and the influence of the initial pattern density of the plurality of test points located around the feature point on the feature point, wherein the influence degree of the initial pattern density of the plurality of test points around the feature point on the feature point is set anisotropically; obtaining the actual size deviation corresponding to the feature point; obtaining the estimated size deviation of the feature point; judging whether the estimated size deviation meets the preset condition according to the estimated size deviation and the actual size deviation; when the estimated size deviation meets the preset condition, the establishment of the etching deviation compensation model is completed. The etching deviation compensation model is more accurately established.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor manufacturing, and in particular to an etching model and its creation method and system, compensation method, equipment and storage medium. Background Technology

[0002] With the trend of semiconductor dimensions shrinking, due to limitations in device resolution or more stringent requirements for patterning, the same pattern layer may be split into multiple photomasks to define the pattern in different regions. This causes drastic variations in pattern density across different regions of the same photomask, resulting in significant size loading (CD) effects in areas with large differences in pattern density. Even with advanced equipment, this CD loading effect is a long-range effect ranging from tens to hundreds of micrometers.

[0003] However, there are currently very strict limitations on changes in critical structural dimensions in the back-end process (FEOL). For example, in the fin and gate loop, if a self-aligned multiple patterning method is used, such CD loading will result in a relatively large pitch walking, which may affect the process window of subsequent layers, device performance, and reliability. Therefore, this CD loading effect is an important problem that cannot be ignored and needs to be addressed. Summary of the Invention

[0004] The problem addressed by the embodiments of the present invention is to provide a model and its establishment method and system, a compensation method, a device and a storage medium, to improve the effect of etching deviation compensation using an etching deviation compensation model.

[0005] To address the aforementioned problems, this invention provides a method for establishing an etching deviation compensation model, comprising: providing a test layout, the test layout including a test pattern and multiple test points; obtaining the initial pattern density of each test point based on the test pattern; taking any test point as a feature point, obtaining the effective pattern density of the feature point based on the initial pattern density of each feature point and the influence of the initial pattern densities of multiple test points surrounding the feature point on the feature point, wherein the influence of the initial pattern densities of the multiple test points surrounding the feature point on the feature point is anisotropically set; obtaining the actual etched pattern corresponding to the test pattern; and measuring the actual etched pattern. Obtain the actual size deviation of the corresponding feature point; obtain the estimated size deviation of the feature point based on the effective pattern density; determine whether the estimated size deviation meets the preset conditions based on the estimated size deviation and the actual size deviation; when the estimated size deviation does not meet the preset conditions, adjust the influence of the initial pattern density of multiple test points around the feature point on the feature point, and return to execute the process of taking any test point as the feature point, obtaining the effective pattern density of the feature point based on the initial pattern density of each feature point and the influence of the initial pattern density of multiple test points around the feature point on the feature point; when the estimated size deviation meets the preset conditions, complete the establishment of the etching deviation compensation model.

[0006] Optionally, a test layout is provided, which includes test windows that correspond one-to-one with the test points; based on the test graphics, the initial graphic density of each test point is obtained, including: obtaining the ratio of the area of ​​the test graphics in the test window to the total area of ​​the test window as the initial graphic density of the test point.

[0007] Optionally, taking any test point as a feature point, the effective graphic density of the feature point is obtained based on the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points around the feature point on the feature point. This includes: performing a weighted average of the initial graphic densities of the feature point and its surrounding test points to obtain the effective graphic density of the feature point, wherein the weights of test points equidistant from the feature point are configured separately; when the estimated size deviation does not meet the preset conditions, the influence of the initial graphic density of the multiple test points around the feature point on the feature point is adjusted, including: adjusting the weights of the initial graphic density of the multiple test points around the feature point.

[0008] Optionally, a weighted average is performed on the initial graphic density of the feature point and multiple test points around it to obtain the effective graphic density of the feature point. This includes: convolving the feature point and the graphic density distribution of multiple test points around it with a filter function to obtain the effective graphic density of the feature point, wherein the filter function is different from the function at the same distance from the feature point; when the estimated size deviation does not meet the preset conditions, the influence of the initial graphic density of multiple test points around the feature point on the feature point is adjusted, including: adjusting the parameters of the filter function.

[0009] Optionally, a filter function is used to convolve with the feature point and the graphic density distribution of multiple test points around it. The filter function includes a Gaussian function, which has a first variance parameter along the x-axis and a second variance parameter along the y-axis, with the x-axis and y-axis being perpendicular to each other.

[0010] Optionally, the expression for the Gaussian function includes: Where, σ x Let σ be the first variance parameter. y Here, (x0, y0) represents the second variance parameter, (x0, y0) represents the coordinates of the feature point, x represents the x-coordinate of the feature point and the test point along the x-axis, and y represents the y-coordinate of the feature point and the test point along the y-axis.

[0011] Optional, in the Gaussian function, σ x With σ y They are not equal.

[0012] Optionally, based on the effective graphic density, the estimated size deviation of the feature points is obtained, including: performing polynomial fitting on the effective graphic density of multiple feature points to obtain a fitting function between the estimated size deviation and the effective graphic density; and obtaining the estimated size deviation corresponding to the feature points through the fitting function.

[0013] Optionally, based on the estimated size deviation and the actual size deviation, the method of least squares is used to determine whether the estimated size deviation meets the preset conditions.

[0014] Optionally, polynomial fitting is performed on the effective pattern density of multiple feature points to obtain a fitting function between the estimated size deviation and the effective pattern density. This includes: setting the initial polynomial coefficients of the fitting function; obtaining the initial estimated size deviation based on the initial polynomial coefficients; and using the least squares method to determine whether the estimated size deviation meets the preset conditions. This includes: obtaining the sum of squares of the differences between the initial estimated size deviation and the actual size deviation of multiple feature points; determining whether the sum of squares is less than or equal to a preset threshold; when the sum of squares is less than or equal to the preset threshold, the estimated size deviation meets the preset conditions, and the initial polynomial coefficients are obtained as polynomial coefficients to complete the establishment of the etching deviation compensation model; when the sum of squares is greater than the preset threshold, the estimated size deviation does not meet the preset conditions, and the influence of the initial pattern density of multiple test points around the feature point on the feature point is adjusted. This also involves adjusting the initial polynomial coefficients and returning to execute the process of using any test point as the feature point, and obtaining the effective pattern density of the feature point based on the initial pattern density of each feature point and the influence of the initial pattern density of multiple test points around the feature point on the feature point.

[0015] Optionally, the expression for the fitting function includes: in, K represents the effective graph density of the j-th feature point. n The coefficients are the initial polynomial coefficients.

[0016] Optionally, use an expression L is the sum of squares of the differences between the initial estimated size deviation and the actual size deviation of multiple feature points. j* This represents the actual dimensional deviation.

[0017] Optionally, after establishing the etching deviation compensation model, the method further includes: setting the etching deviation compensation value corresponding to the test pattern according to the etching deviation compensation model; obtaining the predicted size of the test pattern according to the etching deviation compensation value; obtaining the actual size corresponding to the test pattern; determining whether the etching deviation compensation model passes the verification based on the predicted size and the actual size; if the etching deviation compensation model passes the verification, no correction is made to the etching deviation compensation model; if the etching deviation compensation model fails the verification, correction is made to the etching deviation compensation model.

[0018] Optionally, based on the predicted size and the actual size, determine whether the etching deviation compensation model has passed the verification, including: obtaining the difference between the predicted size and the actual size; and determining whether the difference is less than or equal to a preset difference threshold.

[0019] Accordingly, this invention also provides a system for establishing an etching deviation compensation model, comprising: a layout providing module for providing a test layout, the test layout including a test pattern and multiple test points; an initial pattern density acquisition module for obtaining the initial pattern density of each test point based on the test pattern; an effective pattern density acquisition module for obtaining the effective pattern density of a feature point by taking any test point as a feature point and considering the influence of the initial pattern density of each feature point and the initial pattern density of multiple test points surrounding the feature point on the feature point, wherein the influence of the initial pattern density of multiple test points surrounding the feature point on the feature point is anisotropically set; an actual etching pattern acquisition module for obtaining the actual etching pattern corresponding to the test pattern; and an actual size deviation acquisition module. The module is used to measure the actual etched pattern and obtain the actual size deviation of the corresponding feature point; the estimated size deviation acquisition module is used to obtain the estimated size deviation of the feature point based on the effective pattern density; the judgment module is used to determine whether the estimated size deviation meets the preset conditions based on the estimated size deviation and the actual size deviation; when the estimated size deviation does not meet the preset conditions, the influence of the initial pattern density of multiple test points around the feature point on the feature point is adjusted, and the process returns to execute the process of taking any test point as the feature point and obtaining the effective pattern density of the feature point based on the initial pattern density of each feature point and the influence of the initial pattern density of multiple test points around the feature point on the feature point; the model building module is used to complete the establishment of the etching deviation compensation model when the estimated size deviation meets the preset conditions.

[0020] Accordingly, embodiments of the present invention also provide an etching deviation compensation model, including an etching deviation compensation model obtained by using the etching deviation compensation model establishment method provided in embodiments of the present invention.

[0021] Accordingly, embodiments of the present invention also provide an etching deviation compensation method, including an etching deviation compensation method based on an etching deviation compensation model established by the etching deviation compensation model establishment method provided in embodiments of the present invention.

[0022] Accordingly, embodiments of the present invention also provide an apparatus including at least one memory and at least one processor, wherein the memory stores one or more computer instructions, and the one or more computer instructions are executed by the processor to implement the etching deviation compensation method provided in the embodiments of the present invention.

[0023] Accordingly, embodiments of the present invention also provide a storage medium storing one or more computer instructions, which are used to implement the etching deviation compensation method provided in embodiments of the present invention.

[0024] Compared with the prior art, the technical solution of the embodiments of the present invention has the following advantages:

[0025] In the etching deviation compensation model establishment method provided in this embodiment of the invention, any test point is taken as a feature point. Based on the initial pattern density of each feature point and the influence of the initial pattern density of multiple test points surrounding the feature point on the feature point, the effective pattern density of the feature point is obtained. The influence of the initial pattern density of multiple test points surrounding the feature point on the feature point is set anisotropically. Compared to the scheme of isotropically setting the influence of the initial pattern density of multiple test points surrounding the feature point, in this embodiment of the invention, due to the anisotropy during the etching process, the influence of the test points surrounding the feature point on the size of the feature point is anisotropic. Therefore, anisotropically setting the influence of the initial pattern density of multiple test points surrounding the feature point on the feature point allows for flexible adjustment of the influence of each test point in obtaining the effective pattern density of the feature point according to the specific distribution of the test pattern. This makes the etching deviation compensation model adaptable to test patterns of various shapes, and at the same time, it enables a more accurate establishment of the etching deviation compensation model, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[0026] In the optional scheme, the Gaussian function has a first variance parameter along the x-axis and a second variance parameter along the y-axis. Compared to the scheme with only one variance parameter, the Gaussian function has two variance parameters, increasing the adjustable parameters of the Gaussian function. This allows for flexible selection of two variance parameters, adapting to various test patterns. Moreover, in the process of establishing the etching deviation compensation model, based on the initial pattern density distribution of multiple test points around the feature point, the two parameters of the Gaussian function can be adjusted to obtain a more accurate Gaussian function. This allows for a more precise allocation of the weights of the initial pattern density of the multiple test points around the feature point in the weighted average processing of the initial pattern density, thus establishing a more accurate etching deviation compensation model and improving the effect of etching deviation compensation using the etching deviation compensation model. Attached Figure Description

[0027] Figures 1 to 4 This is a step-by-step diagram illustrating the establishment and application of an etching deviation compensation model;

[0028] Figure 5 This is a flowchart of an embodiment of the method for establishing the etching deviation compensation model of the present invention;

[0029] Figures 6 to 7 This is a schematic diagram of each step in one embodiment of the method for establishing the etching deviation compensation model of the present invention;

[0030] Figure 8 This is a functional block diagram of an embodiment of the etching deviation compensation model establishment system of the present invention;

[0031] Figure 9 This is a hardware structure diagram of an embodiment of the device provided by the present invention. Detailed Implementation

[0032] The effectiveness of current etching deviation compensation models needs improvement. This paper analyzes the reasons why the effectiveness of etching deviation compensation needs to be improved by establishing and applying an etching deviation compensation model.

[0033] Figures 1 to 4 This is a step-by-step diagram illustrating the establishment and application of an etching deviation compensation model.

[0034] As the background technology explains, due to limitations in device resolution or stricter requirements for patterns, the same pattern layer may be split into multiple photomasks to define patterns in different regions. This causes drastic variations in pattern density across different regions of the same photomask, resulting in significant dimensional loading (CDloading) effects in areas with large differences in pattern density. Even with advanced equipment, this CDloading effect is a long-range effect ranging from tens to hundreds of micrometers.

[0035] like Figure 1 and Figure 2 As shown, Figure 1 For the plate layer, along Figure 1 The density difference of the graphic in the X-direction layer is relatively large. Therefore, the size of the actual graphic obtained by etching along the X-direction will gradually decrease unstablely from near the blank area to far away from the blank area, until the graphic density is relatively stable in the middle of the graphic area, at which point the size of the actual graphic will be relatively stable. Figure 2 As shown, the X-axis is along Figure 1 The distance between the position of the graphic in the X direction and the boundary of the graphic region, and the actual size of the graphic on the Y axis. The difference between the actual size of the graphic at the outermost edge and the actual size of the graphic that tends to be stable is CD loading. It can be seen that due to the difference in graphic density, the CD loading effect causes the actual graphic size to be unstable and vary greatly.

[0036] Therefore, etching deviation compensation is needed for the pattern. In traditional CD loading etching compensation models, an isotropic Gaussian function or a response function that is inversely related to distance (i.e., function values ​​are equal at positions equidistant from the center point) is typically used as the filtering function. The final effective pattern density is obtained by convolving the discrete local pattern density with the filtering function. Then, by fitting the relationship between the effective pattern density and the actual CD loading value, a compensation model (such as...) is obtained. Figure 3 (As shown).

[0037] However, for Figure 3 The points marked with red circles in the middle have almost the same effective graphic density, but their CDLoading values ​​are arranged in a regular pattern from small to large. Figure 4 Corresponding to the actual graphic distribution of these points, as the CDLoading value increases, the graphic distribution length decreases but the number of roots increases (i.e., from...). Figure 4 ①to Figure 4 (as shown in ⑤).

[0038] Traditional compensation model building methods use isotropic filtering functions, meaning that environmental graphics at the same distance from the center point in different directions have the same weighting coefficient for the effective graphic density. However, from... Figure 4 and Figure 5 In actual CD loading results, this method ignores the anisotropy in the etching process and cannot accurately represent the weighting coefficients of patterns in different directions, thus the prediction accuracy of CD loading is insufficient.

[0039] To address the technical problem, embodiments of the present invention provide a method for establishing an etching deviation compensation model. (Reference) Figure 5The flowchart illustrates an embodiment of the method for establishing the etching deviation compensation model of the present invention.

[0040] In this embodiment, the method for establishing the etching deviation compensation model includes the following basic steps:

[0041] Step S1: Provide a test layout, which includes test patterns and multiple test points;

[0042] Step S2: Obtain the initial pattern density of each test point based on the test pattern;

[0043] Step S3: Taking any test point as a feature point, obtain the effective graphic density of the feature point based on the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points around the feature point on the feature point. The influence of the initial graphic density of multiple test points around the feature point on the feature point is set anisotropically.

[0044] Step S4: Obtain the actual etched pattern corresponding to the test pattern;

[0045] Step S5: Measure the actual etched pattern to obtain the actual size deviation of the corresponding feature points;

[0046] Step S6: Obtain the estimated size deviation of feature points based on the effective graphic density;

[0047] Step S7: Based on the estimated size deviation and the actual size deviation, determine whether the estimated size deviation meets the preset conditions;

[0048] Step S8: When the estimated size deviation does not meet the preset conditions, adjust the influence of the initial graphic density of multiple test points around the feature point on the feature point, and return to execute the process of taking any test point as the feature point and obtaining the effective graphic density of the feature point based on the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points around the feature point on the feature point.

[0049] Step S9: When the estimated size deviation meets the preset conditions, the etching deviation compensation model is established.

[0050] In the etching deviation compensation model establishment method provided in this embodiment of the invention, compared with the scheme of isotropically setting the influence of the initial pattern density of multiple test points around the feature point on the feature point, in this embodiment of the invention, due to the anisotropy in the etching process, the influence of the test points around the feature point on the size of the feature point is anisotropic. Therefore, the influence of the initial pattern density of multiple test points around the feature point on the feature point can be flexibly adjusted according to the specific distribution of the test pattern to obtain the effective pattern density of the feature point. This makes the etching deviation compensation model adaptable to test patterns of various shapes. At the same time, it can establish the etching deviation compensation model more accurately, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[0051] To make the above-mentioned objects, features and advantages of the embodiments of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0052] Figures 6 to 7 This is a schematic diagram of each step in one embodiment of the method for establishing the etching deviation compensation model of the present invention.

[0053] refer to Figure 6 Step S1: Provide a test layout 100, which includes a test pattern 110 and multiple test points 100s.

[0054] The test pattern 110 is the target pattern transferred onto the wafer. The test pattern 110 is used to create a test photomask, thereby using the test photomask to perform photolithography to form the corresponding actual exposure pattern on the wafer.

[0055] In this embodiment, the test layout 100 includes multiple test points 100s.

[0056] Test point 100s is the point used for measuring graphic density.

[0057] Specifically, in this embodiment, the coordinates (x, y) are used to represent the position of the test point 100s.

[0058] In this embodiment, the test layout 100 includes a test window 100a that corresponds one-to-one with the test point 100s.

[0059] The test density of test point 100s is obtained by calculating the density of test pattern 110 in test window 100a.

[0060] In this embodiment, test point 100s is represented by the coordinates of the center point of test window 100a. In other embodiments, test points may also be represented by the coordinates of other points within the test window.

[0061] Step S2: Based on the test pattern 110, obtain the initial pattern density of each test point for 100 seconds.

[0062] The initial graphic density of each test point is obtained after 100 seconds, which is used to obtain the effective graphic density of the feature points later.

[0063] In this embodiment, the initial graphic density of each test point 100s is obtained based on the test graphic 110, including: obtaining the ratio of the area of ​​the test graphic 110 in the test window 100a to the total area of ​​the test window 100a as the initial graphic density of the test point 100s.

[0064] Each test window 100a has a one-to-one corresponding test point 100s, that is, the graphic density of the test graphic 110 in the test window is the initial graphic density of the coordinates (x, y) corresponding to the test point 100s.

[0065] Continue to refer to Figure 5 Step S3: Take any test point 100s as feature point j, and obtain the effective graphic density of feature point j based on the initial graphic density of each feature point j and the influence of the initial graphic density of multiple test points 100s around feature point j on feature point j. The influence of the initial graphic density of multiple test points 100s around feature point j on feature point j is set anisotropically.

[0066] The influence of the initial graphic density of multiple test points 100s around feature point j on feature point j in anisotropic settings refers to the fact that the initial graphic density of test points 100s that are equidistant from feature point j in each direction around feature point j has different influence on feature point j.

[0067] Specifically, in this embodiment, in the test layout 100, with feature point j as the center point, a matrix composed of test windows 100a corresponding to multiple test points 100s around it is selected as the initial test window 100A. The initial test window 100A is used to build the model and obtain the effective graphic density of feature point j, taking into account the influence of the initial graphic density of the test points 100s around feature point j in the initial test window 100A.

[0068] Model building through the initial test window 100A is beneficial for improving model building efficiency and saving computing power.

[0069] Typically, a square matrix is ​​formed by selecting test windows 100a corresponding to multiple test points 100s around feature point j as the center point. This helps to improve the uniformity of the effective graphic density of feature point j by processing the influence of the initial graphic density of each feature point j and the initial graphic density of multiple test points 100s around feature point j on feature point j.

[0070] Compared to the approach of isotropically setting the initial pattern density of multiple test points around a feature point to influence the feature point, in this embodiment, due to the anisotropy during the etching process, the influence of the test points 100s around feature point j on the size of feature point j is anisotropic. Therefore, the influence of the initial pattern density of multiple test points 100s around feature point j on feature point j can be flexibly adjusted according to the specific distribution of the test pattern 110 in obtaining the effective pattern density of feature point j. This makes the etching deviation compensation model adaptable to test patterns 110 of various shapes. At the same time, it can establish the etching deviation compensation model more accurately, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[0071] In this embodiment, taking any test point 100s as feature point j, the effective graphic density of feature point j is obtained based on the initial graphic density of each feature point j and the influence of the initial graphic density of multiple test points 100s around feature point j on feature point j. This includes: performing a weighted average processing on the initial graphic density of feature point j and multiple test points 100s around it to obtain the effective graphic density of feature point j, wherein the weights of test points 100s that are equidistant from feature point j are configured respectively.

[0072] By performing a weighted average of the initial graphic density of feature point j and its surrounding test points 100s, the influence of the surrounding test points 100s on feature point j can be taken into account, thus obtaining the effective graphic density of feature point j. The weights of the test points 100s equidistant from feature point j are configured separately, which allows for flexible adjustment of the weights of each test point 100s in obtaining the effective graphic density of feature point j according to the specific distribution of the test graphic 110. This makes the etching deviation compensation model adaptable to test graphics 110 of various shapes, and at the same time, it can establish the etching deviation compensation model more accurately, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[0073] In this embodiment, the initial graphic density of feature point j and its surrounding test points 100s is weighted and averaged to obtain the effective graphic density of feature point j. This includes: convolving a filter function with the graphic density distribution of feature point j and its surrounding test points 100s to obtain the effective graphic density of feature point j. The filter function has different values ​​at the same distance from feature point j.

[0074] The filter function is convolved with the graphic density distribution of feature point j and multiple test points 100s around it. By using convolution, the influence of multiple test points 100s around feature point j on feature point j can be calculated by weighted averaging. The function value of the filter function at the same distance from feature point j is the weight value of the initial graphic density of test point 100s at the corresponding position. The function value of the filter function at the same distance from feature point j is different. Therefore, the weight value of each test point 100s in the process of obtaining the effective graphic density of feature point j can be flexibly obtained according to the specific distribution of the test graphic 110. This makes the etching deviation compensation model adaptable to test graphics 110 of various shapes. At the same time, it can establish the etching deviation compensation model more accurately, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[0075] In this embodiment, the filter function is convolved with the feature point j and the graphic density distribution of multiple test points 100s around it. The filter function includes a Gaussian function, which has a first variance parameter along the x-axis and a second variance parameter along the y-axis. The x-axis and y-axis are perpendicular to each other.

[0076] The Gaussian function has a first variance parameter along the x-axis and a second variance parameter along the y-axis. Compared to a Gaussian function with only one variance parameter, the Gaussian function has two variance parameters, increasing the adjustable parameters and allowing for flexible selection of the two variance parameters. This makes it suitable for various distributions of test patterns 110. Furthermore, in the process of establishing the etching deviation compensation model, based on the initial pattern density distribution of multiple test points 100s around feature point j, the two parameters of the Gaussian function can be adjusted to obtain a more accurate Gaussian function. This allows for a more precise allocation of the weight of the initial pattern density of the multiple test points 100s around feature point j in the weighted average processing, thus establishing a more accurate etching deviation compensation model and improving the effect of etching deviation compensation using the etching deviation compensation model.

[0077] In this embodiment, a Gaussian function is established with feature point j as the origin.

[0078] As an example, in this embodiment, the expression for the Gaussian function includes: Where, σx Let σ be the first variance parameter. y Here, (x0, y0) represents the second variance parameter, (x0, y0) represents the coordinates of the feature point, x represents the abscissa of the feature point and the test point along the x-axis, and y represents the ordinate of the feature point and the test point along the y-axis.

[0079] It should be noted that the expression for the Gaussian function is not limited to the expression mentioned above.

[0080] In this embodiment, in the Gaussian function, σ x With σ y They are not equal.

[0081] σ x With σ y If they are not equal, then the two adjustable parameters of the Gaussian function are not equal, which is beneficial for adapting to the asymmetric arrangement of test patterns 100s. In the process of establishing the etching deviation compensation model, it is beneficial to establish the etching deviation compensation model more accurately, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[0082] As an example, in this embodiment, the expression for obtaining the effective graphic density of feature point j includes:

[0083] in, For effective graphic density, Den local (x,y) represents the initial graphic density, and f(x,y) represents the filtering function. This is the convolution operator.

[0084] It should be noted that in this embodiment, each test point 100s in the test layout 100 is a feature point j, and the effective graphic density is obtained to obtain the effective graphic density distribution of the test layout 100.

[0085] It should be noted that in this embodiment, the filtering function is not limited to the formation of a Gaussian function. The filtering function also includes other forms of functions. For example, the filtering function can also be a Cauchy (Lorentz) distribution function. Specifically, the expression is: Where, γ x For the first parameter, γ y The second parameter is (x0, y0), where (x0, y0) are the coordinates of the feature point, x is the abscissa of the feature point and the test point along the x-axis, and y is the ordinate of the feature point and the test point along the y-axis.

[0086] Execute step S4: Obtain the actual etched pattern corresponding to test pattern 110.

[0087] Obtain the actual etched pattern corresponding to the test pattern 110, which is used to calibrate the estimated size of feature point j.

[0088] Due to the etching process and the arrangement of the test pattern 110, the actual etched pattern obtained has a size error compared with the test pattern 110. Therefore, the estimated size of feature point j is calibrated by using the actual etched pattern corresponding to the test pattern 110, so as to obtain the compensation value of etching deviation and compensate the test pattern 110, thereby alleviating the CD loading effect of the pattern.

[0089] Execute step S5: Measure the actual etched pattern to obtain the actual size deviation of the corresponding feature point j.

[0090] The actual size deviation refers to the CD loading effect caused by the CD loading effect on the actual pattern obtained by etching.

[0091] The actual size deviation of the corresponding feature point j is obtained and used as a benchmark for obtaining the etching deviation compensation value corresponding to feature point j.

[0092] Execute step S6: Based on the effective graphic density, obtain the estimated size deviation of feature point j.

[0093] Among them, the predicted size deviation refers to the CD loading effect caused by the CD loading effect in the final graphic predicted by the model.

[0094] The estimated size deviation of feature point j is obtained and used to determine whether the estimated size deviation meets the preset conditions, thereby determining whether the etching deviation compensation model has been established.

[0095] In this embodiment, the estimated size deviation of feature point j is obtained based on the effective graphic density, including: performing polynomial fitting on the effective graphic density of multiple feature points j to obtain a fitting function between the estimated size deviation and the effective graphic density; and obtaining the estimated size deviation corresponding to feature point j through the fitting function.

[0096] By performing polynomial fitting to obtain a fitting function, an unknown parameter can be inferred from the existing data, thus enabling effective prediction of unknown values. Specifically, it can obtain a fitting function for the effective graphic density from the existing effective graphic density of multiple feature points j, and thus predict the estimated size deviation corresponding to feature point j through the fitting function.

[0097] In this embodiment, polynomial fitting is performed on the effective graphic density of multiple feature points j to obtain a fitting function between the estimated size deviation and the effective graphic density, including: setting the initial polynomial coefficients of the fitting function; and obtaining the initial estimated size deviation based on the initial polynomial coefficients.

[0098] As an example, in this embodiment, the expression of the fitting function includes: in, K represents the effective graph density of the j-th feature point. n The coefficients are the initial polynomial coefficients.

[0099] In this embodiment, the initial estimated size deviation is obtained based on the initial polynomial coefficients, that is, the initial estimated size deviation L is obtained based on the fitting function of the initial polynomial coefficients. j .

[0100] Execute step S7: Based on the estimated size deviation and the actual size deviation, determine whether the estimated size deviation meets the preset conditions.

[0101] After obtaining the estimated size deviation corresponding to feature point j by fitting the function, it is determined whether the estimated size deviation meets the preset conditions in order to determine whether the compensation model has been successfully established.

[0102] In this embodiment, the method of least squares is used to determine whether the estimated size deviation meets the preset conditions based on the estimated size deviation and the actual size deviation.

[0103] The least squares method can find the best function match for the data by minimizing the sum of squared errors, thereby obtaining the polynomial coefficients in the fitted function.

[0104] In this embodiment, the least squares method is used to determine whether the estimated size deviation meets the preset conditions, including: obtaining the sum of squares of the differences between the initial estimated size deviation and the actual size deviation of multiple feature points j.

[0105] The sum of squares of the differences between the initial estimated size deviation and the actual size deviation of multiple feature points j is obtained to determine whether the fitting function is complete.

[0106] As an example, in this embodiment, the expression is used. Obtain the sum of squares of the differences between the initial estimated size deviation and the actual size deviation of multiple feature points, L j* This represents the actual dimensional deviation.

[0107] In this embodiment, it is determined whether the sum of squares is less than or equal to a preset threshold.

[0108] Execute step S8. When the estimated size deviation does not meet the preset conditions, adjust the influence of the initial graphic density of multiple test points 100s around feature point j on feature point j, and return to execute the effective graphic density of feature point j by taking any test point 100s as feature point j and obtaining the effective graphic density of feature point j based on the initial graphic density of each feature point j and the influence of the initial graphic density of multiple test points 100s around feature point j on feature point j.

[0109] Specifically, in this embodiment, when the sum of squares is greater than a preset threshold, the estimated size deviation does not meet the preset condition. In adjusting the influence of the initial graphic density of multiple test points 100s around feature point j on feature point j, the initial polynomial coefficients are also adjusted, and the process is returned to execute. Taking any test point 100s as feature point j, the effective graphic density of feature point j is obtained based on the initial graphic density of each feature point j and the influence of the initial graphic density of multiple test points 100s around feature point j on feature point j.

[0110] For feature point j, the actual value of CD loading (i.e., the actual size deviation L) j* ) and fitted value (i.e., predicted size deviation L) j There is a certain difference between them. When the sum of squares is greater than a preset threshold, it indicates that the difference between them is still relatively large, i.e., L. j* With L j If the sum of squares of the differences is large, it indicates that the established etching deviation compensation model cannot predict the CD loading effect well, thus indicating that the established etching deviation compensation model cannot play a relatively accurate etching deviation compensation role. Therefore, it is necessary to adjust the initial polynomial coefficients and return to execute the process of taking any test point 100s as feature point j, and obtaining the effective pattern density of feature point j based on the initial pattern density of each feature point j and the influence of the initial pattern density of multiple test points 100s around feature point j on feature point j. This process is iterated until the sum of squares is less than or equal to the preset threshold.

[0111] It should be noted that in this embodiment, when the sum of squares is greater than the preset threshold, the initial polynomial coefficients can be adjusted according to actual needs. Then, the process returns to obtain the initial estimated size deviation based on the initial polynomial coefficients and performs iterative processing within the fitting function step. After multiple iterations, if the sum of squares is still greater than the preset threshold, the influence of the initial polynomial coefficients and the initial graphic density of multiple test points 100s around feature point j on feature point j can be adjusted simultaneously. Then, the process returns to take any test point 100s as feature point j and obtains the effective graphic density of feature point j based on the initial graphic density of each feature point j and the influence of the initial graphic density of multiple test points 100s around feature point j on feature point j. This helps to save iterative computation power, improve iterative computation efficiency, and thus improve the efficiency of establishing the etching deviation compensation model.

[0112] As an example, Figure 7 The fitting function between the estimated dimensional deviation and the effective pattern density is shown. Figure 7 (dummy line in the image), and actual size deviation ( Figure 7The scatter points in the data are used to iterate the fitting function, resulting in a high correlation between the final fitting function and the actual size deviation.

[0113] Specifically, in this embodiment, when the estimated size deviation does not meet the preset conditions, the influence of the initial graphic density of multiple test points 100s around feature point j on feature point j is adjusted, including: adjusting the weight of the initial graphic density of multiple test points 100s around feature point j.

[0114] Accordingly, in this embodiment, when the estimated size deviation does not meet the preset conditions, the influence of the initial graphic density of multiple test points 100s around feature point j on feature point j is adjusted, including adjusting the parameters of the filtering function.

[0115] Step S9: When the estimated size deviation meets the preset conditions, the etching deviation compensation model is established.

[0116] Specifically, in this embodiment, when the sum of squares is less than or equal to a preset threshold, the estimated size deviation meets the preset condition, the initial polynomial coefficients are obtained as polynomial coefficients, and the etching deviation compensation model is established.

[0117] For feature point j, the actual value of CD loading (i.e., the actual size deviation L) j* ) and fitted value (i.e., predicted size deviation L) j There is a certain difference between the two. When the sum of squares is less than or equal to a preset threshold, it means that the difference between the two is as small as possible, i.e., L. j* With L j If the sum of squares of the differences is as small as possible, it can indicate that the established etching deviation compensation model can predict the CD loading effect well, thus indicating that the established etching deviation compensation model can play a more accurate etching deviation compensation role.

[0118] In this embodiment, after establishing the etching deviation compensation model, the method further includes: verifying the etching deviation compensation model.

[0119] Specifically, in this embodiment, the etching deviation compensation model is verified, including: setting the etching deviation compensation value corresponding to the test pattern 110 according to the etching deviation compensation model.

[0120] Due to the etching process and the arrangement of the test pattern 110, the actual etched pattern obtained has a size error compared with the test pattern 110. Therefore, according to the etching deviation compensation model, the etching deviation compensation value corresponding to the test pattern 110 is set, so as to obtain the etching deviation compensation value and compensate the test pattern 110, thereby alleviating the CD loading effect of the pattern.

[0121] Accordingly, in this embodiment, the predicted size of the test pattern 110 is obtained based on the etching deviation compensation value.

[0122] The predicted size is the size of the test pattern 110 obtained after size compensation by applying the etching deviation compensation value obtained through the established etching deviation compensation model.

[0123] In this embodiment, the actual size corresponding to the test graphic 110 is obtained.

[0124] Obtain the actual dimensions corresponding to test pattern 110 to determine whether the etching deviation compensation model has passed verification.

[0125] Specifically, in this embodiment, the etching deviation compensation model is verified based on the predicted size and the actual size.

[0126] In this embodiment, determining whether the etching deviation compensation model passes verification based on the predicted size and the actual size includes: obtaining the difference between the predicted size and the actual size; and determining whether the difference is less than or equal to a preset difference threshold.

[0127] The difference between the predicted size and the actual size represents the error between the pattern obtained by actual etching and the pattern predicted by the etching deviation compensation model. Therefore, by judging whether the difference is less than or equal to the preset difference threshold, it can be determined whether the etching deviation compensation model has passed the verification.

[0128] In this embodiment, when the etching deviation compensation model passes the verification, no correction is made to the etching deviation compensation model. That is, when the difference is less than or equal to the preset difference threshold, no correction is made to the etching deviation compensation model.

[0129] In this embodiment, when the etching deviation compensation model fails the verification, the etching deviation compensation model is corrected. That is, when the difference is greater than the preset difference threshold, the etching deviation compensation model is corrected.

[0130] When the difference is greater than the preset difference threshold, the etching deviation compensation model needs to be further corrected until the difference is less than or equal to the preset difference threshold.

[0131] Specifically, by adjusting the influence of the initial graphic density of multiple test points around the feature point on the feature point, the expression of the etching deviation compensation model is iterated multiple times and continuously fitted until the difference is less than or equal to the preset difference threshold, thus obtaining the final etching deviation compensation model.

[0132] Accordingly, the present invention also provides a system for establishing an etching deviation compensation model. Figure 8 This is a functional block diagram of an embodiment of the etching deviation compensation model establishment system of the present invention.

[0133] In this embodiment, the etching deviation compensation model establishment system 50 includes: a layout providing module 501, used to provide a test layout, the test layout including a test pattern, and the test layout including multiple test points; an initial pattern density acquisition module 502, used to obtain the initial pattern density of each test point according to the test pattern; an effective pattern density acquisition module 503, used to take any test point as a feature point, and obtain the effective pattern density of the feature point according to the initial pattern density of each feature point and the influence of the initial pattern density of multiple test points around the feature point on the feature point, wherein the influence of the initial pattern density of multiple test points around the feature point on the feature point is set anisotropically; an actual etching pattern acquisition module 504, used to acquire the actual etching pattern corresponding to the test pattern; and an actual size deviation acquisition module 505. 05 is used to measure the actual etched pattern and obtain the actual size deviation of the corresponding feature point; the estimated size deviation acquisition module 506 is used to obtain the estimated size deviation of the feature point based on the effective pattern density; the judgment module 507 is used to determine whether the estimated size deviation meets the preset conditions based on the estimated size deviation and the actual size deviation; when the estimated size deviation does not meet the preset conditions, the influence of the initial pattern density of multiple test points around the feature point on the feature point is adjusted, and the process returns to execute the process of taking any test point as the feature point and obtaining the effective pattern density of the feature point based on the initial pattern density of each feature point and the influence of the initial pattern density of multiple test points around the feature point on the feature point; the model establishment module 508 is used to complete the establishment of the etching deviation compensation model when the estimated size deviation meets the preset conditions.

[0134] The layout providing module 501 is used to provide a test layout, which includes a test pattern and multiple test points.

[0135] The test pattern is the target pattern transferred onto the wafer. The test pattern is used to create a test photomask, which is then used for photolithography to form the corresponding actual exposure pattern on the wafer.

[0136] In this embodiment, the test layout includes multiple test points.

[0137] The test points are the points used to measure the graphic density.

[0138] Specifically, in this embodiment, the position of the test point is represented by coordinates (x, y).

[0139] In this embodiment, a test layout is provided, which includes test windows that correspond one-to-one with the test points.

[0140] The test density of test points is obtained by calculating the density of the test pattern in the test window.

[0141] In this embodiment, the test point is represented by the coordinates of the center point of the test window. In other embodiments, the test point may also be represented by the coordinates of other points on the test window.

[0142] The initial graphic density acquisition module 502 is used to obtain the initial graphic density of each test point based on the test graphic.

[0143] The initial graphic density of each test point is obtained, which is used to obtain the effective graphic density of the feature points in the subsequent process.

[0144] In this embodiment, the initial graphic density of each test point is obtained based on the test graphic, including: obtaining the ratio of the area of ​​the test graphic in the test window to the total area of ​​the test window as the initial graphic density of the test point.

[0145] Each test window has a one-to-one corresponding test point, that is, the graphic density of the test graphic in the test window is the initial graphic density of the coordinates (x, y) of the test point.

[0146] The effective graphic density acquisition module 503 is used to obtain the effective graphic density of a feature point by taking any test point as the feature point and based on the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points around the feature point on the feature point. The influence of the initial graphic density of multiple test points around the feature point on the feature point is set anisotropically.

[0147] The influence of the initial graphic density of multiple test points around the feature point on the feature point in anisotropic settings refers to the fact that the initial graphic density of test points equidistant from the feature point in different directions around the feature point has a different influence on the feature point.

[0148] Specifically, in this embodiment, in the test layout, the matrix formed by the test windows corresponding to multiple test points around the feature point as the center point is selected as the initial test window. The initial test window is used to build the model and obtain the effective graphic density of the feature point. The influence of the initial graphic density of the test points around the feature point in the initial test window is taken into account.

[0149] Building a model through the initial test window helps improve model building efficiency and saves computing power.

[0150] Typically, a square matrix is ​​formed by selecting test windows corresponding to multiple test points around a feature point as the center point. This helps to improve the uniformity of the effective graphic density of the feature point by processing the influence of the initial graphic density of each feature point and the initial graphic density of multiple test points around the feature point on the feature point.

[0151] Compared to the approach of isotropically setting the initial pattern density of multiple test points around a feature point to influence the feature point's size, in this embodiment, due to the anisotropy during the etching process, the influence of the test points around the feature point on the feature point's size is anisotropic. Therefore, anisotropically setting the initial pattern density of multiple test points around the feature point to influence the feature point's size can flexibly adjust the influence of each test point in obtaining the effective pattern density of the feature point according to the specific distribution of the test pattern. This makes the etching deviation compensation model adaptable to test patterns of various shapes, and at the same time, it can establish the etching deviation compensation model more accurately, thereby improving the effect of using the etching deviation compensation model for etching deviation compensation.

[0152] In this embodiment, taking any test point as a feature point, the effective graphic density of the feature point is obtained based on the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points around the feature point on the feature point. This includes: performing a weighted average of the initial graphic densities of the feature point and multiple test points around it to obtain the effective graphic density of the feature point, wherein the weights of the test points equidistant from the feature point are configured respectively.

[0153] By performing a weighted average of the initial graphic density of the feature point and its surrounding test points, the influence of the surrounding test points on the feature point can be taken into account, thus obtaining the effective graphic density of the feature point. The weights of the test points equidistant from the feature point are configured separately, which allows for flexible adjustment of the weights of each test point in obtaining the effective graphic density of the feature point according to the specific distribution of the test graphic. This makes the etching deviation compensation model adaptable to test graphics of various shapes, and at the same time, it can establish the etching deviation compensation model more accurately, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[0154] In this embodiment, the initial graphic density of the feature point and multiple test points around it is weighted and averaged to obtain the effective graphic density of the feature point. This includes: convolving the feature point and the graphic density distribution of multiple test points around it with a filter function to obtain the effective graphic density of the feature point. The filter function has different values ​​at the same distance from the feature point.

[0155] By convolving a filter function with the feature point and the graphic density distribution of multiple test points around it, the influence of the multiple test points around the feature point on the feature point can be weighted and averaged. The function value of the filter function at the same distance from the feature point is the weight value of the initial graphic density of the test point at the corresponding position. The function value of the filter function at the same distance from the feature point is different. This allows the weight value of each test point in the process of obtaining the effective graphic density of the feature point to be flexibly obtained according to the specific distribution of the test graphic. This makes the etching deviation compensation model adaptable to test graphics of various shapes. At the same time, it can establish the etching deviation compensation model more accurately, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[0156] In this embodiment, the filter function is used to convolve with the feature point and the graphic density distribution of multiple test points around it. The filter function includes a Gaussian function, which has a first variance parameter along the x-axis and a second variance parameter along the y-axis. The x-axis and y-axis are perpendicular to each other.

[0157] The Gaussian function has a first variance parameter along the x-axis and a second variance parameter along the y-axis. Compared to schemes with only one variance parameter, the Gaussian function has two variance parameters, increasing its adjustability. This allows for flexible selection of two variance parameters, adapting to various test patterns. Furthermore, during the establishment of the etching deviation compensation model, based on the initial pattern density distribution of multiple test points around the feature point, the two parameters of the Gaussian function can be adjusted to obtain a more accurate Gaussian function. This allows for a more precise allocation of the weights of the initial pattern densities of the multiple test points around the feature point in the weighted average processing, leading to a more accurate establishment of the etching deviation compensation model and ultimately improving the effectiveness of etching deviation compensation.

[0158] In this embodiment, a Gaussian function is established with the feature point as the origin.

[0159] As an example, in this embodiment, the expression for the Gaussian function includes: Where, σ x Let σ be the first variance parameter. y Here, (x0, y0) represents the second variance parameter, (x0, y0) represents the coordinates of the feature point, x represents the abscissa of the feature point and the test point along the x-axis, and y represents the ordinate of the feature point and the test point along the y-axis.

[0160] It should be noted that the expression for the Gaussian function is not limited to the expression mentioned above.

[0161] In this embodiment, in the Gaussian function, σ x With σy They are not equal.

[0162] σ x With σ y If they are not equal, then the two adjustable parameters of the Gaussian function are not equal, which is beneficial for adapting to test patterns with asymmetric arrangement. In the process of establishing the etching deviation compensation model, it is beneficial to establish the etching deviation compensation model more accurately, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[0163] As an example, in this embodiment, the expression for obtaining the effective graphic density of feature points includes: in, For effective graphic density, Den local (x,y) represents the initial graphic density, and f(x,y) represents the filtering function. This is the convolution operator.

[0164] It should be noted that in this embodiment, each test point in the test layout is a feature point, and the effective graphic density is obtained to obtain the effective graphic density distribution of the test layout.

[0165] It should be noted that in this embodiment, the filtering function is not limited to the formation of a Gaussian function. The filtering function also includes other forms of functions. For example, the filtering function can also be a Cauchy (Lorentz) distribution function. Specifically, the expression is: Where, γ x For the first parameter, γ y The second parameter is (x0, y0), where (x0, y0) are the coordinates of the feature point, x is the abscissa of the feature point and the test point along the x-axis, and y is the ordinate of the feature point and the test point along the y-axis.

[0166] The actual etching pattern acquisition module 504 is used to acquire the actual etching pattern corresponding to the test pattern.

[0167] Obtain the actual etched pattern corresponding to the test pattern, which is used to calibrate the estimated size of the feature points.

[0168] Due to the etching process and the arrangement of the test pattern, the actual etched pattern obtained has a size error compared to the test pattern. Therefore, the estimated size of the feature points is calibrated by using the actual etched pattern corresponding to the test pattern, so as to obtain the compensation value of the etching deviation and compensate the test pattern, thereby alleviating the CD loading effect of the pattern.

[0169] The actual size deviation acquisition module 505 is used to measure the actual etched pattern and obtain the actual size deviation of the corresponding feature points.

[0170] The actual size deviation refers to the CD loading effect caused by the CD loading effect on the actual pattern obtained by etching.

[0171] The actual size deviation of the corresponding feature point is obtained and used as a benchmark for obtaining the etching deviation compensation value of the feature point.

[0172] The estimated size deviation acquisition module 506 is used to obtain the estimated size deviation of feature points based on the effective graphic density.

[0173] Among them, the predicted size deviation refers to the CD loading effect caused by the CD loading effect in the final graphic predicted by the model.

[0174] The estimated size deviation of the feature points is obtained, which is used to determine whether the estimated size deviation meets the preset conditions, thereby determining whether the etching deviation compensation model has been established.

[0175] In this embodiment, obtaining the estimated size deviation of feature points based on the effective graphic density includes: performing polynomial fitting on the effective graphic density of multiple feature points to obtain a fitting function between the estimated size deviation and the effective graphic density; and obtaining the estimated size deviation corresponding to the feature points through the fitting function.

[0176] By performing polynomial fitting to obtain a fitting function, an unknown parameter can be inferred from existing data, thus enabling effective prediction of unknown values. In other words, it is possible to obtain a fitting function for the effective graphic density from the effective graphic density of multiple existing feature points, and thus predict the estimated size deviation corresponding to the feature points through the fitting function.

[0177] In this embodiment, polynomial fitting is performed on the effective graphic density of multiple feature points to obtain a fitting function between the estimated size deviation and the effective graphic density, including: setting the initial polynomial coefficients of the fitting function; and obtaining the initial estimated size deviation based on the initial polynomial coefficients.

[0178] As an example, in this embodiment, the expression of the fitting function includes: in, K is the effective graph density of the i-th feature point. n The coefficients are the initial polynomial coefficients.

[0179] In this embodiment, the initial estimated size deviation is obtained based on the initial polynomial coefficients, that is, the initial estimated size deviation L is obtained based on the fitting function of the initial polynomial coefficients. j .

[0180] The judgment module 507 is used to determine whether the estimated size deviation meets the preset conditions based on the estimated size deviation and the actual size deviation. When the estimated size deviation does not meet the preset conditions, the module adjusts the influence of the initial graphic density of multiple test points around the feature point on the feature point, and returns to execute the effective graphic density of the feature point by taking any test point as the feature point and based on the influence of the initial graphic density of each feature point and the initial graphic density of multiple test points around the feature point on the feature point.

[0181] After obtaining the estimated size deviation corresponding to the feature points by fitting the function, it is determined whether the estimated size deviation meets the preset conditions, so as to determine whether the compensation model has been successfully established.

[0182] In this embodiment, the method of least squares is used to determine whether the estimated size deviation meets the preset conditions based on the estimated size deviation and the actual size deviation.

[0183] The least squares method can find the best function match for the data by minimizing the sum of squared errors, thereby obtaining the polynomial coefficients in the fitted function.

[0184] In this embodiment, the least squares method is used to determine whether the estimated size deviation meets the preset conditions, including: obtaining the sum of squares of the differences between the initial estimated size deviation and the actual size deviation of multiple feature points.

[0185] The sum of squares of the differences between the initial estimated size deviation and the actual size deviation of multiple feature points is obtained to determine whether the fitting function is complete.

[0186] As an example, in this embodiment, the expression is used. Obtain the sum of squares of the differences between the initial estimated size deviation and the actual size deviation of multiple feature points, L j* This represents the actual dimensional deviation.

[0187] In this embodiment, it is determined whether the sum of squares is less than or equal to a preset threshold.

[0188] In this embodiment, when the estimated size deviation does not meet the preset conditions, the influence of the initial graphic density of multiple test points around the feature point on the feature point is adjusted, and the process returns to execute the process of taking any test point as the feature point and obtaining the effective graphic density of the feature point based on the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points around the feature point on the feature point.

[0189] Specifically, in this embodiment, when the sum of squares is greater than a preset threshold, the estimated size deviation does not meet the preset condition. In adjusting the influence of the initial graphic density of multiple test points around the feature point on the feature point, the initial polynomial coefficients are also adjusted, and the process returns to execute with any test point as the feature point. Based on the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points around the feature point on the feature point, the effective graphic density of the feature point is obtained.

[0190] For feature points, the actual value of CD loading (i.e., the actual size deviation L) j* ) and fitted value (i.e., predicted size deviation L) j There is a certain difference between them. When the sum of squares is greater than a preset threshold, it indicates that the difference between them is still relatively large, i.e., L. j* With L j If the sum of squares of the differences is large, it indicates that the established etching deviation compensation model cannot predict the CD loading effect well, thus indicating that the established etching deviation compensation model cannot achieve a relatively accurate etching deviation compensation effect. Therefore, it is necessary to adjust the initial polynomial coefficients and return to execute the process. Taking any test point as the feature point, the effective pattern density of the feature point is obtained based on the initial pattern density of each feature point and the influence of the initial pattern density of multiple test points around the feature point on the feature point. This process is iterated until the sum of squares is less than or equal to the preset threshold.

[0191] It should be noted that in this embodiment, when the sum of squares is greater than the preset threshold, the initial polynomial coefficients can be adjusted according to actual needs. Then, the process returns to execute the initial estimated size deviation obtained from the initial polynomial coefficients and iterative processing within the fitting function step. After multiple iterations, if the sum of squares is still greater than the preset threshold, the influence of the initial polynomial coefficients and the initial graphic density of multiple test points around the feature point on the feature point can be adjusted simultaneously. Then, the process returns to execute the process using any test point as the feature point. Based on the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points around the feature point on the feature point, the effective graphic density of the feature point is obtained. This helps to save iterative computational power, improve iterative computation efficiency, and thus improve the efficiency of establishing the etching deviation compensation model.

[0192] Specifically, in this embodiment, when the estimated size deviation does not meet the preset conditions, the influence of the initial graphic density of multiple test points around the feature point on the feature point is adjusted, including: adjusting the weight of the initial graphic density of multiple test points around the feature point.

[0193] Accordingly, in this embodiment, when the estimated size deviation does not meet the preset conditions, the influence of the initial graphic density of multiple test points around the feature point on the feature point is adjusted, including adjusting the parameters of the filtering function.

[0194] The model building module 508 is used to build an etching deviation compensation model when the estimated size deviation meets the preset conditions.

[0195] Specifically, in this embodiment, when the sum of squares is less than or equal to a preset threshold, the estimated size deviation meets the preset condition, the initial polynomial coefficients are obtained as polynomial coefficients, and the etching deviation compensation model is established.

[0196] For feature points, the actual value of CD loading (i.e., the actual size deviation L) j* ) and fitted value (i.e., predicted size deviation L) j There is a certain difference between the two. When the sum of squares is less than or equal to a preset threshold, it means that the difference between the two is as small as possible, i.e., L. j* With L j If the sum of squares of the differences is as small as possible, it can indicate that the established etching deviation compensation model can predict the CD loading effect well, thus indicating that the established etching deviation compensation model can play a more accurate etching deviation compensation role.

[0197] In this embodiment, after establishing the etching deviation compensation model, the method further includes: verifying the etching deviation compensation model.

[0198] Specifically, in this embodiment, the etching deviation compensation model is verified by setting the etching deviation compensation value corresponding to the test pattern according to the etching deviation compensation model.

[0199] Due to the etching process and the arrangement of the test patterns, the actual etched pattern and the test pattern have size errors. Therefore, according to the etching deviation compensation model, the etching deviation compensation value corresponding to the test pattern is set, so as to obtain the etching deviation compensation value and compensate the test pattern, thereby alleviating the CDloading effect of the pattern.

[0200] Accordingly, in this embodiment, the predicted size of the test pattern is obtained based on the etching deviation compensation value.

[0201] The predicted size is the size of the test pattern obtained after size compensation by applying the etching deviation compensation value obtained through the established etching deviation compensation model.

[0202] In this embodiment, the actual size corresponding to the test graphic is obtained.

[0203] Obtain the actual dimensions corresponding to the test pattern to determine whether the etching deviation compensation model has passed verification.

[0204] Specifically, in this embodiment, the etching deviation compensation model is verified based on the predicted size and the actual size.

[0205] In this embodiment, determining whether the etching deviation compensation model passes verification based on the predicted size and the actual size includes: obtaining the difference between the predicted size and the actual size; and determining whether the difference is less than or equal to a preset difference threshold.

[0206] The difference between the predicted size and the actual size represents the error between the pattern obtained by actual etching and the pattern predicted by the etching deviation compensation model. Therefore, by judging whether the difference is less than or equal to the preset difference threshold, it can be determined whether the etching deviation compensation model has passed the verification.

[0207] In this embodiment, when the etching deviation compensation model passes the verification, no correction is made to the etching deviation compensation model. That is, when the difference is less than or equal to the preset difference threshold, no correction is made to the etching deviation compensation model.

[0208] In this embodiment, when the etching deviation compensation model fails the verification, the etching deviation compensation model is corrected. That is, when the difference is greater than the preset difference threshold, the etching deviation compensation model is corrected.

[0209] When the difference is greater than the preset difference threshold, the etching deviation compensation model needs to be further corrected until the difference is less than or equal to the preset difference threshold.

[0210] Specifically, by adjusting the influence of the initial graphic density of multiple test points around the feature point on the feature point, the expression of the etching deviation compensation model is iterated multiple times and continuously fitted until the difference is less than or equal to the preset difference threshold, thus obtaining the final etching deviation compensation model.

[0211] Accordingly, embodiments of the present invention also provide an etching deviation compensation model, including an etching deviation compensation model obtained by using the etching deviation compensation model establishment method provided in embodiments of the present invention.

[0212] As can be seen from the foregoing embodiments, compared to the scheme of isotropically setting the influence of the initial pattern density of multiple test points around the feature point on the feature point, in the embodiments of the present invention, due to the anisotropy in the etching process, the influence of the test points around the feature point on the size of the feature point is anisotropic. Therefore, the influence of the initial pattern density of multiple test points around the feature point on the feature point can be flexibly adjusted according to the specific distribution of the test pattern to obtain the effective pattern density of the feature point. This makes the etching deviation compensation model adaptable to test patterns of various shapes. At the same time, it can establish the etching deviation compensation model more accurately, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[0213] Accordingly, embodiments of the present invention also provide an etching deviation compensation method, including an etching deviation compensation method based on an etching deviation compensation model established by the etching deviation compensation model establishment method provided in embodiments of the present invention.

[0214] As can be seen from the foregoing embodiments, compared to the scheme of isotropically setting the influence of the initial pattern density of multiple test points around the feature point on the feature point, in the embodiments of the present invention, due to the anisotropy in the etching process, the influence of the test points around the feature point on the size of the feature point is anisotropic. Therefore, the influence of the initial pattern density of multiple test points around the feature point on the feature point can be flexibly adjusted according to the specific distribution of the test pattern to obtain the effective pattern density of the feature point. This makes the etching deviation compensation model adaptable to test patterns of various shapes. At the same time, it can establish the etching deviation compensation model more accurately, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[0215] This invention also provides a device that can implement the etching deviation compensation model establishment method provided in this invention through the above-described etching deviation compensation model establishment method in the form of a loading program. An optional hardware structure of the terminal device provided in this invention can be as follows: Figure 9 As shown, it includes: at least one processor 01, at least one communication interface 02, at least one memory 03, and at least one communication bus 04.

[0216] In this embodiment, the number of processor 01, communication interface 02, memory 03, and communication bus 04 is at least one, and processor 01, communication interface 02, and memory 03 communicate with each other through communication bus 04. Communication interface 02 can be an interface of a communication module for network communication, such as the interface of a GSM module. Processor 01 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. Memory 03 may include high-speed RAM and may also include non-volatile memory (NVM), such as at least one disk storage device. Memory 03 stores one or more computer instructions, which are executed by processor 01 to implement the etching deviation compensation model establishment method provided in this embodiment of the present invention.

[0217] It should be noted that the aforementioned terminal device may also include other devices (not shown) that may not be essential to understanding the content disclosed in the embodiments of the present invention; given that these other devices may not be essential for understanding the content disclosed in the embodiments of the present invention, the embodiments of the present invention will not describe them one by one.

[0218] This invention also provides a storage medium storing one or more computer instructions for implementing the etching deviation compensation model establishment method provided in this invention.

[0219] In the etching deviation compensation model establishment method provided in this embodiment of the invention, compared with the scheme of isotropically setting the influence of the initial pattern density of multiple test points around the feature point on the feature point, in this embodiment of the invention, due to the anisotropy in the etching process, the influence of the test points around the feature point on the size of the feature point is anisotropic. Therefore, the influence of the initial pattern density of multiple test points around the feature point on the feature point can be flexibly adjusted according to the specific distribution of the test pattern to obtain the effective pattern density of the feature point. This makes the etching deviation compensation model adaptable to test patterns of various shapes. At the same time, it can establish the etching deviation compensation model more accurately, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[0220] The embodiments of the present invention described above are combinations of elements and features of the present invention. Unless otherwise stated, elements or features may be considered optional. Individual elements or features may be practiced without combination with other elements or features. Furthermore, embodiments of the present invention may be constructed by combining some elements and / or features. The order of operations described in the embodiments of the present invention may be rearranged. Some constructions of any embodiment may be included in another embodiment and may be replaced by corresponding constructions of another embodiment. It will be apparent to those skilled in the art that claims in the appended claims that are not expressly referenced in each other may be combined to form embodiments of the present invention, or may be included as new claims in amendments made after the filing of this application.

[0221] Embodiments of the present invention can be implemented by various means, such as hardware, firmware, software, or combinations thereof. In a hardware configuration, the method according to an exemplary embodiment of the present invention can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, etc. In a firmware or software configuration, embodiments of the present invention can be implemented in the form of modules, processes, functions, etc. Software code can be stored in memory units and executed by a processor. The memory units are located inside or outside the processor and can send data to and receive data from the processor via various known means.

[0222] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is accorded the widest scope consistent with the principles and novel features disclosed herein.

[0223] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A method for establishing an etching deviation compensation model, characterized in that, include: A test layout is provided, the test layout including test patterns and multiple test points; Based on the test pattern, the initial pattern density of each test point is obtained; Using any test point as a feature point, the effective graphic density of the feature point is obtained based on the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points around the feature point on the feature point. The influence of the initial graphic density of multiple test points around the feature point on the feature point is set anisotropically. Obtain the actual etched pattern corresponding to the test pattern; Measure the actual etched pattern to obtain the actual size deviation of the corresponding feature points; Based on the effective graphic density, the estimated size deviation of the feature points is obtained; Based on the estimated size deviation and the actual size deviation, determine whether the estimated size deviation meets the preset conditions; When the estimated size deviation does not meet the preset condition, adjust the influence of the initial graphic density of multiple test points around the feature point on the feature point, and return to execute the effective graphic density of the feature point by taking any test point as the feature point and according to the influence of the initial graphic density of each feature point and the initial graphic density of multiple test points around the feature point on the feature point. When the estimated size deviation meets the preset conditions, the etching deviation compensation model is established.

2. The method for establishing the etching deviation compensation model as described in claim 1, characterized in that, The test layout is provided, wherein the test layout includes test windows that correspond one-to-one with the test points; Based on the test pattern, the initial pattern density of each test point is obtained, including: obtaining the ratio of the area of ​​the test pattern in the test window to the total area of ​​the test window as the initial pattern density of the test point.

3. The method for establishing the etching deviation compensation model as described in claim 1, characterized in that, Using any test point as a feature point, the effective graphic density of the feature point is obtained based on the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points around the feature point on the feature point. This includes: performing a weighted average of the initial graphic densities of the feature point and multiple test points around it to obtain the effective graphic density of the feature point, wherein the weights of the test points equidistant from the feature point are configured respectively. When the estimated size deviation does not meet the preset condition, the influence of the initial graphic density of multiple test points around the feature point on the feature point is adjusted, including: adjusting the weight of the initial graphic density of multiple test points around the feature point.

4. The method for establishing the etching deviation compensation model as described in claim 3, characterized in that, The effective graphic density of the feature point is obtained by performing a weighted average of the initial graphic density of the feature point and multiple test points around it. This includes: convolving the feature point and the graphic density distribution of multiple test points around it with a filter function to obtain the effective graphic density of the feature point, wherein the filter function is different at the same distance from the feature point. When the estimated size deviation does not meet the preset condition, the influence of the initial graphic density of multiple test points around the feature point on the feature point is adjusted, including adjusting the parameters of the filtering function.

5. The method for establishing the etching deviation compensation model as described in claim 4, characterized in that, In the convolution of the feature point and the graphic density distribution of multiple test points around it using a filtering function, the filtering function includes a Gaussian function, which has a first variance parameter along the x-axis and a second variance parameter along the y-axis, and the x-axis and y-axis are perpendicular to each other.

6. The method for establishing the etching deviation compensation model as described in claim 5, characterized in that, The expression for the Gaussian function includes: Where, σ x Let σ be the first variance parameter. y Here, (x0, y0) represents the second variance parameter, (x0, y0) represents the coordinates of the feature point, x represents the abscissa of the feature point and the test point along the x-axis, and y represents the ordinate of the feature point and the test point along the y-axis.

7. The method for establishing the etching deviation compensation model as described in claim 6, characterized in that, In the Gaussian function, σ x With σ y They are not equal.

8. The method for establishing the etching deviation compensation model as described in claim 1, characterized in that, The method for obtaining the estimated size deviation of the feature points based on the effective graphic density includes: performing polynomial fitting on the effective graphic density of multiple feature points to obtain a fitting function between the estimated size deviation and the effective graphic density; and obtaining the estimated size deviation corresponding to the feature points through the fitting function.

9. The method for establishing the etching deviation compensation model as described in claim 8, characterized in that, Based on the estimated size deviation and the actual size deviation, the method of least squares is used to determine whether the estimated size deviation meets the preset conditions.

10. The method for establishing the etching deviation compensation model as described in claim 9, characterized in that, To obtain a fitting function between the estimated size deviation and the effective graphic density by performing polynomial fitting on the effective graphic density of multiple feature points, the method includes: setting the initial polynomial coefficients of the fitting function. The initial estimated size deviation is obtained based on the initial polynomial coefficients; Determining whether the estimated size deviation meets the preset conditions using the least squares method includes: obtaining the sum of squares of the differences between the initial estimated size deviation and the actual size deviation of multiple feature points; Determine whether the sum of squares is less than or equal to a preset threshold; When the sum of squares is less than or equal to a preset threshold, the estimated size deviation satisfies the preset condition, the initial polynomial coefficients are obtained as the polynomial coefficients, and the etching deviation compensation model is established. When the sum of squares is greater than a preset threshold, the estimated size deviation does not meet the preset condition. In adjusting the influence of the initial graphic density of multiple test points around the feature point on the feature point, the initial polynomial coefficients are also adjusted, and the process is returned to execute. Taking any test point as the feature point, the effective graphic density of the feature point is obtained based on the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points around the feature point on the feature point.

11. The method for establishing the etching deviation compensation model as described in claim 10, characterized in that, The expression of the fitting function includes: in, K represents the effective graph density of the j-th feature point. n The coefficients are the initial polynomial coefficients.

12. The method for establishing the etching deviation compensation model as described in claim 10, characterized in that, Using expressions Obtain the sum of squares of the differences between the initial estimated size deviation and the actual size deviation of multiple feature points, L j* This represents the actual dimensional deviation.

13. The method for establishing the etching deviation compensation model as described in claim 1, characterized in that, After establishing the etching deviation compensation model, the method further includes: setting the etching deviation compensation value corresponding to the test pattern according to the etching deviation compensation model; Based on the etching deviation compensation value, the predicted size of the test pattern is obtained; Obtain the actual dimensions corresponding to the test graphic; Based on the predicted size and the actual size, determine whether the etching deviation compensation model has passed the verification. Once the etching deviation compensation model is verified, no correction is made to the etching deviation compensation model. If the etching deviation compensation model fails to pass verification, the etching deviation compensation model shall be corrected.

14. The method for establishing the etching deviation compensation model as described in claim 13, characterized in that, Based on the predicted size and the actual size, determine whether the etching deviation compensation model has passed the verification, including: obtaining the difference between the predicted size and the actual size; and determining whether the difference is less than or equal to a preset difference threshold.

15. A system for establishing an etching deviation compensation model, characterized in that, include: A layout providing module is used to provide a test layout, the test layout including a test pattern and multiple test points; The initial graphic density acquisition module is used to obtain the initial graphic density of each test point based on the test graphic. An effective graphic density acquisition module is used to obtain the effective graphic density of a feature point by taking any test point as a feature point and based on the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points around the feature point on the feature point. The influence of the initial graphic density of multiple test points around the feature point on the feature point is set anisotropically. The actual etching pattern acquisition module is used to acquire the actual etching pattern corresponding to the test pattern; The actual size deviation acquisition module is used to measure the actual etched pattern and obtain the actual size deviation of the corresponding feature points; The estimated size deviation acquisition module is used to obtain the estimated size deviation of the feature points based on the effective graphic density. The judgment module is used to determine whether the estimated size deviation meets the preset conditions based on the estimated size deviation and the actual size deviation; when the estimated size deviation does not meet the preset conditions, the module adjusts the influence of the initial graphic density of multiple test points around the feature point on the feature point, and returns to execute the effective graphic density of the feature point by taking any test point as the feature point and based on the influence of the initial graphic density of each feature point and the initial graphic density of multiple test points around the feature point on the feature point. The model building module is used to build the etching deviation compensation model when the estimated size deviation meets the preset conditions.

16. An etching deviation compensation model established using the method described in any one of claims 1-14.

17. An etching deviation compensation method based on an etching deviation compensation model established by the method as described in any one of claims 1-14.

18. A device, characterized in that, It includes at least one memory and at least one processor, the memory storing one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method for establishing an etching deviation compensation model as described in any one of claims 1-14.

19. A storage medium, characterized in that, The storage medium stores one or more computer instructions, which are used to implement the method for establishing the etching deviation compensation model as described in any one of claims 1-14.

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