Model and establishment method and system thereof, compensation method, equipment and storage medium

Abstract

The invention discloses a model, an establishment method and system thereof, a compensation method, equipment and a storage medium, and the establishment method comprises the steps: providing a test layout which comprises a test pattern and a plurality of test points; obtaining an initial pattern density of each test point according to the test pattern; taking any test point as a feature point, and obtaining the effective graph density of the feature point according to the initial graph density of each feature point and the influence of the initial graph density of a plurality of test points around the feature point on the feature point, setting the influence degree of the initial graph density of a plurality of test points around the feature point on the feature point in an anisotropic manner; the actual size deviation of the corresponding feature points is obtained; pre-estimated dimensional deviation of the feature points is obtained; according to the estimated dimensional deviation and the actual dimensional deviation, judging whether the estimated dimensional deviation meets a preset condition or not; and when the estimated dimensional deviation meets a preset condition, completing the establishment of the etching deviation compensation model. According to the method, the etching deviation compensation model is established more accurately.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of semiconductor manufacturing, and in particular to an engraving model and a method and system for establishing the same, a compensation method, a device, and a storage medium. Background Art

[0002] As semiconductor size decreases, due to the limitation of equipment resolution or stricter requirements on graphics, the same layer of graphics may be split into multiple masks to define the graphics of different areas. This will cause drastic changes in the density of graphics in different areas on the same mask. This process will bring significant CD loading effect in areas with large differences in graphic density. Even with advanced technology equipment, this CD loading effect is a long-range effect of tens to hundreds of microns.

[0003] However, at present, there are very strict restrictions on the size changes of key structures in the back-end (FEOL). For example, in the fin and gate loop, if the self-aligned multiple patterning method is used, such CD loading will cause a relatively large size span (pitch walking), which may affect the process window of the subsequent film layer, device performance and reliability. Therefore, this CDloading effect is an important problem that cannot be ignored and needs to be solved. Summary of the invention

[0004] The problem solved by the embodiments of the present invention is to provide a model and a method and system for establishing the model, a compensation method, a device and a storage medium, so as to improve the effect of etching deviation compensation using an etching deviation compensation model.

[0005] To solve the above problems, an embodiment of the present invention provides a method for establishing an etching deviation compensation model, comprising: providing a test layout, the test layout comprising a test pattern, the test layout comprising a plurality of test points; obtaining an initial pattern density of each test point according to the test pattern; taking any test point as a feature point, obtaining an 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 a 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 an actual etching pattern corresponding to the test pattern; measuring the actual etching pattern, Obtain the actual size deviation of the corresponding feature point; obtain the estimated size deviation of the feature point according to the effective graphic density; determine whether the estimated size deviation meets the preset conditions according to 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 graphic density of multiple test points around the feature point on the feature point, and return to execute with any test point as the feature point, according to the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points located around the feature point on the feature point, obtain the effective graphic density of 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, wherein the test layout includes test windows corresponding one to one with the test points; and according to the test pattern, an initial graphic density of each test point is obtained, including: obtaining a ratio of an area of ​​the test pattern in the test window to a 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, obtaining the effective graphic density of the feature point according to the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points located around the feature point on the feature point, including: performing weighted averaging processing 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, wherein the weights of the test points with the same distance from the feature point are respectively configured; when the estimated size deviation does not meet the preset conditions, adjusting the degree of influence of the initial graphic density of multiple test points around the feature point on the feature point, including: adjusting the weights of the initial graphic density of multiple test points around the feature point.

[0008] Optionally, weighted averaging 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, including: using a filter function to convolve the graphic density distribution of the feature point and multiple test points around it 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, adjusting the degree of influence of the initial graphic density of multiple test points around the feature point on the feature point, including: adjusting the parameters of the filter function.

[0009] Optionally, a filter function is used in a graphic density distribution convolution of a feature point and multiple test points around the feature point, where the filter function includes a Gaussian function having a first variance parameter along the x-axis and a second variance parameter along the y-axis, and the x-axis and the y-axis are perpendicular to each other.

[0010] Optionally, the expression of the Gaussian function includes: Among them, σ x is the first variance parameter, σ y is the second variance parameter, (x 0 ,y 0 ) are the coordinates of the feature point, x is the horizontal coordinate of the feature point and the test point along the x-axis, and y is the vertical coordinate of the feature point and the test point along the y-axis.

[0011] Optional, Gaussian function, σ x With σ y Not equal.

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

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

[0014] Optionally, a polynomial fitting is performed on the effective graphic density of multiple feature points to obtain a fitting function of the estimated size deviation and the effective graphic density, including: setting the initial polynomial coefficients of the fitting function; obtaining the initial estimated size deviation according to the initial polynomial coefficients; using the least squares method to determine whether the estimated size deviation meets the preset conditions, including: obtaining the sum of the squares of the differences between the initial estimated size deviation and the actual size deviation of multiple feature points; determining whether the sum of the squares is less than or equal to a preset threshold; when the sum of the squares is less than or equal to the preset threshold, the estimated size deviation meets the preset conditions, the initial polynomial coefficients are obtained as the polynomial coefficients, and the establishment of the etching deviation compensation model is completed; when the sum of the squares is greater than the preset threshold, the estimated size deviation does not meet the preset conditions, and the initial graphic density of multiple test points around the feature point is adjusted to adjust the degree of influence on the feature point, the initial polynomial coefficients are also adjusted, and the execution is returned to take any test point as the feature point, and the effective graphic density of the feature point is obtained according to the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points located around the feature point on the feature point.

[0015] Optionally, the expression of the fitting function includes: in, is the effective graphic density of the jth feature point, K n is the nth initial polynomial coefficient.

[0016] Optionally, use the expression Get 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 is the actual size deviation.

[0017] Optionally, after the establishment of the etching deviation compensation model is completed, it also includes: setting an etching deviation compensation value corresponding to the test pattern according to the etching deviation compensation model; obtaining a predicted size of the test pattern according to the etching deviation compensation value; obtaining an actual size corresponding to the test pattern; judging whether the etching deviation compensation model has passed verification according to the predicted size and the actual size; when the etching deviation compensation model has passed verification, no correction processing is performed on the etching deviation compensation model; when the etching deviation compensation model has not passed verification, correction processing is performed on the etching deviation compensation model.

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

[0019] Correspondingly, an embodiment of the present invention further provides a system for establishing an etching deviation compensation model, including: a layout providing module, used to provide a test layout, the test layout includes a test pattern, and the test layout includes multiple test points; an initial pattern density acquisition module, used to obtain the initial pattern density of each test point according to the test pattern; an effective pattern density acquisition module, 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 located around the feature point on the feature point, wherein the influence degree 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, used to obtain the actual etching pattern corresponding to the test pattern; an actual size deviation acquisition module The acquisition module is used to measure the actual etching 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 according to the effective pattern density; the judgment module is used to judge whether the estimated size deviation meets the preset conditions according to 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 graphic density of multiple test points around the feature point on the feature point, and return to execute any test point as the feature point, according to the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points located around the feature point on the feature point, to obtain the effective graphic density of the feature point; the model establishment module is used to complete the establishment of the etching deviation compensation model when the estimated size deviation meets the preset conditions.

[0020] Correspondingly, an embodiment of the present invention further provides an etching deviation compensation model, including an etching deviation compensation model obtained by using the method for establishing an etching deviation compensation model provided by an embodiment of the present invention.

[0021] Correspondingly, an embodiment of the present invention further provides an etching deviation compensation method, including an etching deviation compensation method based on an etching deviation compensation model established by the method for establishing an etching deviation compensation model provided by the embodiment of the present invention.

[0022] Correspondingly, an embodiment of the present invention also provides a device, including at least one memory and at least one processor, the memory stores one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the etching deviation compensation method provided by the embodiment of the present invention.

[0023] Correspondingly, an embodiment of the present invention further provides a storage medium, wherein the storage medium stores one or more computer instructions, and the one or more computer instructions are used to implement the etching deviation compensation method provided by the embodiment of the present invention.

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

[0025] In the method for establishing an etching deviation compensation model provided by an embodiment of the present invention, any test point is taken as a feature point, and the effective graphic density of the feature point is obtained according to 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, wherein the influence degree of the initial graphic density of multiple test points around the feature point on the feature point is set anisotropically; compared with the scheme of isotropically setting the influence degree of the initial graphic density of multiple test points around the feature point on the feature point, in the embodiment 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, then the influence degree of the initial graphic density of multiple test points around the feature point on the feature point is set anisotropically, and the influence degree of each test point in obtaining the effective graphic density of the feature point can be flexibly adjusted according to the specific distribution of the test pattern, so that the etching deviation compensation model is adapted to test patterns of various shapes, and at the same time, the etching deviation compensation model can be established more accurately, 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 with the scheme in which the Gaussian function has only one variance parameter, the Gaussian function has two variance parameters, which increases the adjustable parameters of the Gaussian function, is conducive to flexible selection of two variance parameters, and is suitable for test patterns of various distributions. Moreover, in the process of establishing the etching deviation compensation model, according to the initial graphic density distribution of multiple test points around the feature point, a more accurate Gaussian function can be obtained by adjusting the two parameters of the Gaussian function, thereby more accurately allocating the weight of the initial graphic density of the feature point and the multiple test points around it in the weighted averaging processing, and more accurately establishing the etching deviation compensation model, thereby improving the effect of etching deviation compensation using the etching deviation compensation model. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figures 1 to 4 It is a step diagram corresponding to the establishment and application of an etching deviation compensation model;

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

[0029] Figure 6 to Figure 7 It is a schematic diagram corresponding to each step in an embodiment of a method for establishing an etching deviation compensation model of the present invention;

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

[0031] Fig. 9 It is a hardware structure diagram of an embodiment of the device provided by the present invention. DETAILED DESCRIPTION

[0032] At present, the effect of etching deviation compensation using etching deviation compensation model needs to be improved. This paper analyzes the reasons why the effect of etching deviation compensation needs to be improved by combining the establishment and application of an etching deviation compensation model.

[0033] Figures 1 to 4 It is a step diagram corresponding to the establishment and application of an etching deviation compensation model.

[0034] As can be seen from the background technology, due to the limitation of equipment resolution or more stringent requirements on graphics, the same layer of graphics may be split into multiple masks to define the graphics of different areas, which will cause drastic changes in the density of graphics in different areas on the same mask. This process will bring significant CD loading effect in areas with large differences in graphic density. Even with advanced technology equipment, this CD loading effect is a long-range effect of tens to hundreds of microns.

[0035] like Figure 1 and Figure 2 As shown, Figure 1 For the layout layer, along Figure 1 The density of the graphics in the X-direction layer is quite different. Therefore, the size of the actual graphics obtained by etching along the X-direction will gradually decrease unsteadily from close to the blank area to far away from the blank area along the X-direction until the density of the graphics is relatively stable in the middle of the graphics area. Figure 2 As shown, the X-axis is along Figure 1 The X-axis is the distance between the graphic position and the graphic area boundary, the Y-axis is the actual graphic size, and the difference between the actual graphic size at the edge and the actual graphic size that tends to be stable is the 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, it is necessary to compensate for the etching deviation of the pattern. In the establishment of the traditional CD loading etching compensation model, an isotropic Gaussian function or a response function that is inversely proportional to the distance (that is, the function values ​​at positions with equal distances from the center point are equal) is generally used as a filter function. The final effective pattern density is obtained by convolving the discrete local pattern density with the filter function. Then, the compensation model (such as Figure 3 shown).

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

[0038] The filter function used in the traditional compensation model establishment method is isotropic, and the weight coefficients of environmental graphics in different directions with the same distance from the center point to the effective graphic density are the same. Figure 4 and Figure 5 From the actual CD Loading results, this method ignores the anisotropy in the etching process and cannot accurately represent the weight coefficients of graphics in different directions, so the prediction accuracy of CD loading is not enough.

[0039] In order to solve the technical problem, an embodiment of the present invention provides a method for establishing an etching deviation compensation model. Figure 5 , showing a flow chart of an embodiment of a method for establishing an 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: providing a test layout, wherein the test layout includes a test pattern and a plurality of test points;

[0042] Step S2: obtaining the initial pattern density of each test point according to the test pattern;

[0043] Step S3: 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 multiple test points around the feature point on the feature point, wherein the influence degree of the initial pattern density of multiple test points around the feature point on the feature point is set anisotropically;

[0044] Step S4: obtaining the actual etching pattern corresponding to the test pattern;

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

[0046] Step S6: obtaining the estimated size deviation of the feature point according to the effective pattern density;

[0047] Step S7: judging whether the estimated size deviation meets a preset condition according to the estimated size deviation and the actual size deviation;

[0048] Step S8: when the estimated size deviation does not meet the preset condition, adjusting the influence of the initial graphic density of multiple test points around the feature point on the feature point, and returning to execute taking any test point as the feature point, obtaining the effective graphic density of the feature point according to 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 condition, the establishment of the etching deviation compensation model is completed.

[0050] In the method for establishing an etching deviation compensation model provided in an embodiment of the present invention, compared with a solution of isotropically setting the influence of the initial graphic density of multiple test points around a feature point on the feature point, in an embodiment 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, so the influence of the initial graphic density of multiple test points around the feature point on the feature point is anisotropically set, and the influence of each test point in obtaining the effective graphic density of the feature point can be flexibly adjusted according to the specific distribution of the test pattern, so that the etching deviation compensation model is adapted to test patterns of various shapes. At the same time, the etching deviation compensation model can be established more accurately, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[0051] In order to make the above-mentioned purposes, features and advantages of the embodiments of the present invention more obvious and understandable, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0052] Figure 6 to Figure 7 It is a schematic diagram corresponding to each step in an embodiment of a method for establishing an etching deviation compensation model of the present invention.

[0053] refer to Figure 6 , executing step S1: providing a test layout 100, the test layout 100 includes a test pattern 110, and the test layout 100 includes a plurality of test points 100s.

[0054] The test pattern 110 is a target pattern transferred onto the wafer. The test pattern 110 is used to make a test mask, so that a photolithography process is performed using the test mask to form a corresponding actual exposure pattern on the wafer.

[0055] In this embodiment, the test layout 100 includes a plurality of test points 100s.

[0056] The test point 100s is a point used for pattern density measurement.

[0057] Specifically, in this embodiment, the position of the test point 100s is represented by coordinates (x, y).

[0058] In this embodiment, a test layout 100 is provided, and the test layout 100 includes test windows 100a corresponding to the test points 100s one by one.

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

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

[0061] Execute step S2: according to the test pattern 110, obtain the initial pattern density of each test point 100s.

[0062] The initial pattern density of each test point for 100s is obtained, which is used to subsequently obtain the effective pattern density of the feature points.

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

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

[0065] Continue to refer Figure 5 , execute step S3: take any test point 100s as the feature point j, and obtain the effective graphic density of the feature point j according to the initial graphic density of each feature point j and the influence of the initial graphic density of multiple test points 100s located around the feature point j on the feature point j, wherein the influence degree of the initial graphic density of multiple test points 100s around the feature point j on the feature point j is set anisotropically.

[0066] Among them, the degree of influence of the initial graphic density of multiple test points 100s around the feature point j in anisotropically setting the feature point j means that in various directions around the feature point j, the initial graphic density of the test points 100s with the same distance from the feature point j has different influence settings on the feature point j.

[0067] Specifically, in this embodiment, in the test layout 100, with the feature point j as the center point, a matrix consisting 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 establish a model. In obtaining the effective graphic density of the feature point j, the influence of the initial graphic density of the test points 100s around the feature point j in the initial test window 100A is taken into account.

[0068] Establishing the model through the initial test window 100A is beneficial to improving the efficiency of model establishment and saving computing power.

[0069] Usually, taking feature point j as the center point and selecting a matrix consisting of test windows 100a corresponding to multiple test points 100s around it as a square matrix is ​​beneficial to improving the uniformity of the effective graphic density of feature point j obtained by processing the initial graphic density of each feature point j and the influence of the initial graphic density of multiple test points 100s located around the feature point j on the feature point j.

[0070] Compared with the scheme of isotropically setting the influence of the initial graphic density of multiple test points around the feature point on the feature point, in this embodiment, due to the anisotropy in the etching process, the test points 100s around the feature point j have anisotropic influence on the size of the feature point j. Therefore, the influence of the initial graphic density of multiple test points 100s around the feature point j on the feature point j is anisotropically set. The influence of each test point 100s in obtaining the effective graphic density of the feature point j can be flexibly adjusted according to the specific distribution of the test pattern 110, so that the etching deviation compensation model is adapted to test patterns 110 of various shapes. At the same time, the etching deviation compensation model can be established more accurately, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[0071] In this embodiment, any test point 100s is taken as a feature point j, and the effective graphic density of the feature point j is obtained according to the initial graphic density of each feature point j and the influence of the initial graphic density of multiple test points 100s located around the feature point j on the feature point j, including: performing weighted averaging processing on the initial graphic density of the feature point j and the multiple test points 100s around it to obtain the effective graphic density of the feature point j, wherein the weights of the test points 100s that are equal to the feature point j are configured respectively.

[0072] By performing weighted averaging processing on the initial graphic density of the feature point j and multiple test points 100s around it, the influence of multiple test points 100s around the feature point j on the feature point j can be taken into account to obtain the effective graphic density of the feature point j, wherein the weights of the test points 100s that are equal to the feature point j are configured separately, and the weights of each test point 100s in obtaining the effective graphic density of the feature point j can be flexibly adjusted according to the specific distribution of the test pattern 110, so that the etching deviation compensation model is adapted to test patterns 110 of various shapes. At the same time, the etching deviation compensation model can be established 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 the feature point j and multiple test points 100s around it is weighted averaged to obtain the effective graphic density of the feature point j, including: using a filter function and the graphic density distribution convolution of the feature point j and multiple test points 100s around it to obtain the effective graphic density of the feature point j, wherein the function value of the filter function at the same distance from the feature point j is different.

[0074] A filter function is convolved with the graphic density distribution of feature point j and multiple test points 100s around it. The convolution method can be used to calculate the weighted average of the influence of multiple test points 100s around feature point j on feature point j. The function value of the filter function at the same distance from the feature point j is the weight value of the initial graphic density of the test point 100s at the corresponding position. The function value of the filter function at the same distance from the feature point j is different. The weight value of each test point 100s in the process of obtaining the effective graphic density of the feature point j can be flexibly obtained according to the specific distribution of the test graphic 110, so that the etching deviation compensation model is adapted to test graphics 110 of various shapes. At the same time, the etching deviation compensation model can be established more accurately, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[0075] In this embodiment, a filter function is used in the convolution of the graphic density distribution of the feature point j and multiple test points 100s around it. The filter function includes a Gaussian function. The Gaussian function has a first variance parameter along the x-axis and a second variance parameter along the y-axis. The x-axis and the 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 with a solution in which the Gaussian function has only one variance parameter, the Gaussian function has two variance parameters, which increases the adjustable parameters of the Gaussian function, facilitates flexible selection of the two variance parameters, and is adapted to various distributions of test graphics 110. Moreover, in the process of establishing the etching deviation compensation model, according to the initial graphic density distribution of multiple test points 100s around the feature point j, a more accurate Gaussian function can be obtained by adjusting the two parameters of the Gaussian function, thereby more accurately allocating the weight of the initial graphic density of the multiple test points 100s around the feature point j and the weighted averaging processing of the initial graphic density of the multiple test points 100s around it, and more accurately establishing the etching deviation compensation model, thereby 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 of the Gaussian function includes: Among them, σ x is the first variance parameter, σ y is the second variance parameter, (x 0 ,y 0 ) is the coordinate of the feature point, x is the horizontal coordinate of the feature point and the test point along the x-axis, and y is the vertical coordinate of the feature point and the test point along the y-axis.

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

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

[0081] σ x With σ y If they are not equal, the two adjustable parameters of the Gaussian function are not equal, which is conducive to adapting to the asymmetrically arranged test pattern 100s. In the process of establishing the etching deviation compensation model, it is conducive to more accurately establishing the etching deviation compensation model, 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 the feature point j includes:

[0083] in, Den is the effective pattern density, local (x,y) is the initial graphic density, f(x,y) is the filter function, is the convolution operator symbol.

[0084] It should be noted that, in the present embodiment, each test point 100s in the test layout 100 is a feature point j, and the effective pattern density is acquired, so as to obtain the effective pattern density distribution of the test layout 100.

[0085] It should be noted that, in this embodiment, the filter function is not limited to the formation of a Gaussian function, and the filter function also includes other forms of functions. For example, the filter function can also be a Cauchy (Lorentz) distribution function. Specifically, the expression is: Among them, γ x is the first parameter, γ y is the second parameter, (x 0 ,y 0 ) is the coordinate of the feature point, x is the horizontal coordinate of the feature point and the test point along the x-axis, and y is the vertical coordinate of the feature point and the test point along the y-axis.

[0086] Execute step S4: obtain the actual etching pattern corresponding to the test pattern 110.

[0087] The actual etching pattern corresponding to the test pattern 110 is obtained for calibrating the estimated size of the feature point j.

[0088] Due to the etching process and the arrangement of the test pattern 110, there is an error in the size of the actual etched pattern obtained and the test pattern 110. Therefore, the estimated size of the feature point j is calibrated using the actual etched pattern corresponding to the test pattern 110, so as to obtain the compensation value of the 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 corresponding to the feature point j.

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

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

[0092] Execute step S6: obtain the estimated size deviation of feature point j according to the effective pattern density.

[0093] The estimated size deviation refers to the CDloading of the final graphics predicted by the model due to the CD loading effect.

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

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

[0096] Performing polynomial fitting to obtain a fitting function can infer an unknown parameter from the existing data, so that the unknown value can be effectively predicted, that is, the fitting function of the effective graphic density can be obtained from the effective graphic density of multiple existing feature points j, so that the estimated size deviation corresponding to the feature point j can be predicted 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 of the estimated size deviation and the effective graphic density, including: setting initial polynomial coefficients of the fitting function; and obtaining an initial estimated size deviation according to the initial polynomial coefficients.

[0098] As an example, in this embodiment, the expression of the fitting function includes: in, is the effective graphic density of the jth feature point, K n is the nth initial polynomial coefficient.

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

[0100] Execute step S7: determine whether the estimated size deviation meets a preset condition based on the estimated size deviation and the actual size deviation.

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

[0102] In this embodiment, in determining whether the estimated size deviation meets a preset condition based on the estimated size deviation and the actual size deviation, the least square method is used to determine whether the estimated size deviation meets the preset condition.

[0103] The least squares method can find the best function matching the data by minimizing the sum of squares of errors, thereby obtaining the polynomial coefficients in the fitting 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 deviations and the actual size deviations of multiple feature points j.

[0105] Obtain the sum of squares of the differences between the initial estimated size deviations and the actual size deviations of multiple feature points j, which is used to determine whether the fitting function is completed.

[0106] As an example, in this embodiment, using the expression Obtain the sum of squares of the differences between the initial estimated size deviations and the actual size deviations of multiple said feature points, L j* is the actual size 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 degree of the initial graphic density of multiple test points 100s around the feature point j on the feature point j, and return to execute with any one of the test points 100s as the feature point j. According to the initial graphic density of each feature point j and the influence of the initial graphic density of multiple test points 100s located around the feature point j on the feature point j, obtain the effective graphic density of the feature point j.

[0109] Specifically, in this embodiment, when the sum of squares is greater than the preset threshold, the estimated size deviation does not meet the preset conditions. When adjusting the influence degree of the initial graphic density of multiple test points 100s around the feature point j on the feature point j, the initial polynomial coefficients are also adjusted, and return to execute with any one of the test points 100s as the feature point j. According to the initial graphic density of each feature point j and the influence of the initial graphic density of multiple test points 100s located around the feature point j on the feature point j, obtain the effective graphic density of the feature point j.

[0110] For the feature point j, there is a certain difference between the actual value of CD loading (i.e., the actual size deviation L j* ) and the fitted value (i.e., the estimated size deviation L j ). When the sum of squares is greater than the preset threshold, it means that the difference between the two is still large, that is, L j* and L jIf the sum of the squares of the differences is large, it means that the established etching bias compensation model cannot predict the CD loading effect well, and thus the established etching bias compensation model cannot achieve a more accurate etching bias compensation effect. Therefore, it is necessary to adjust the initial polynomial coefficients and return to execute any test point 100s as the feature point j. According to the initial graphic density of each feature point j and the influence of the initial graphic density of multiple test points 100s located around the feature point j on the feature point j, the effective graphic density of the feature point j is obtained, and continuous iterative processing is performed until the sum of the squares is less than or equal to the preset threshold.

[0111] It should be noted that, in the present embodiment, when the sum of squares is greater than a preset threshold, it is also possible, according to actual needs, to first adjust the initial polynomial coefficients, and return to execute to obtain the initial estimated size deviation based on the initial polynomial coefficients, and perform continuous iterative processing within the fitting function step. After multiple iterative processing, if the sum of squares is still greater than the preset threshold, the initial polynomial coefficients and the influence of the initial graphic density of multiple test points 100s around the feature point j on the feature point j can be adjusted at the same time, and return to execute with any test point 100s as the feature point j, and obtain the effective graphic density of the feature point j according to the initial graphic density of each feature point j and the influence of the initial graphic density of multiple test points 100s located around the feature point j on the feature point j, which is conducive to saving iterative computing power and improving iterative computing efficiency, thereby improving the efficiency of establishing the etching deviation compensation model.

[0112] As an example, Figure 7 The fitting function of the estimated size deviation and the effective pattern density is shown ( Figure 7 The dotted straight line in the figure), and the actual size deviation ( Figure 7 By continuously iterating the fitting function, the final obtained fitting function has a high correlation with the actual size deviation value.

[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 the feature point j on the feature point j is adjusted, including: adjusting the weight of the initial graphic density of multiple test points 100s around the feature point j.

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

[0115] Execute step S9: when the estimated size deviation meets the preset condition, the establishment of the etching deviation compensation model is completed.

[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, and the initial polynomial coefficients are obtained as the polynomial coefficients to complete the establishment of the etching deviation compensation model.

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

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

[0119] Specifically, in this embodiment, verifying the etching deviation compensation model includes: setting an 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, there is an error in the size of the actual etched pattern obtained and 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 that the compensation value of the etching deviation can be obtained, and the test pattern 110 is compensated, so as to alleviate the CD loading effect of the pattern.

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

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

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

[0124] The actual size corresponding to the test pattern 110 is obtained to determine whether the etching deviation compensation model passes the verification.

[0125] Specifically, in this embodiment, it is determined whether the etching deviation compensation model has passed the verification based on the predicted size and the actual size.

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

[0127] The difference between the predicted dimension and the actual dimension characterizes the error situation between the pattern actually obtained by etching and the pattern predicted by the etching deviation compensation model. Therefore, by determining whether the difference is less than or equal to the preset difference threshold, it is determined whether the etching deviation compensation model passes verification.

[0128] In this embodiment, when the etching deviation compensation model passes 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 to pass verification, correction is made to the etching deviation compensation model, that is, when the difference is greater than the preset difference threshold, correction is made to the etching deviation compensation model.

[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 degree of the initial pattern 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, and the final etching deviation compensation model is obtained.

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

[0133] In this embodiment, the etching deviation compensation model establishment system 50 includes: a layout providing module 501, which is used to provide a test layout, wherein the test layout includes a test pattern, and the test layout includes a plurality of test points; an initial pattern density acquisition module 502, which is used to obtain the initial pattern density of each test point according to the test pattern; an effective pattern density acquisition module 503, which is 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 a 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; an actual etching pattern acquisition module 504, which is used to obtain the actual etching pattern corresponding to the test pattern; an actual size deviation acquisition module 505, which is used to obtain the actual etching pattern corresponding to the test pattern; and an actual size deviation acquisition module 506, which is used to obtain the actual etching pattern corresponding to the test pattern. 05, used to measure the actual etching pattern and obtain the actual size deviation of the corresponding feature point; the estimated size deviation acquisition module 506, used to obtain the estimated size deviation of the feature point according to the effective pattern density; the judgment module 507, used to judge whether the estimated size deviation meets the preset conditions according to 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 graphic density of multiple test points around the feature point on the feature point, and return to execute any test point as the feature point, according to the initial graphic density of each feature point, and the influence of the initial graphic density of multiple test points located around the feature point on the feature point, to obtain the effective graphic density of the feature point; the model establishment module 508, 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, the test layout includes a test pattern, and the test layout includes a plurality of test points.

[0135] The test pattern is a target pattern transferred to the wafer. The test pattern is used to make a test mask, so that a photolithography process is performed using the test mask to form a corresponding actual exposure pattern on the wafer.

[0136] In this embodiment, the test layout includes a plurality of test points.

[0137] Test points are points used to perform pattern density measurements.

[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, and the test layout includes test windows corresponding to the test points one by one.

[0140] The test density of the test points is obtained by calculating the density of the test patterns 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 can also be represented by the coordinates of other position points of the test window.

[0142] The initial pattern density acquisition module 502 is used to obtain the initial pattern density of each test point according to the test pattern.

[0143] The initial pattern density of each test point is obtained, which is used to subsequently obtain the effective pattern density of the feature point.

[0144] In this embodiment, the initial pattern density of each test point is obtained according to the test pattern, 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.

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

[0146] The effective graphic density acquisition module 503 is used to take any test point as a feature point, and obtain the effective graphic density of the feature point according to the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points located around the feature point on the feature point, wherein the degree of influence of the initial graphic density of multiple test points around the feature point on the feature point is set anisotropically.

[0147] The degree of influence of the initial graphic density of multiple test points around the feature point in anisotropic manner on the feature point means that the degree of influence of the initial graphic density of the test points with equal distance from the feature point on the feature point is set differently in various directions around the feature point.

[0148] Specifically, in this embodiment, in the test layout, a matrix consisting of test windows corresponding to multiple test points around a feature point is selected as the initial test window with the feature point as the center point. The initial test window is used to establish a model. In obtaining 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 the model through the initial test window is helpful to improve the efficiency of model building and save computing power.

[0150] Usually, taking the feature point as the center point and selecting the matrix composed of test windows corresponding to multiple test points around it as a square matrix is ​​beneficial to improving the uniformity of the effective graphic density of the feature point obtained by processing the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points located around the feature point on the feature point.

[0151] Compared with the solution of the influence degree of the initial graphic density of multiple test points around the feature point set isotropically on the feature point, in this embodiment, 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, setting the influence degree of the initial graphic density of multiple test points around the feature point anisotropically can flexibly adjust the influence degree of each test point on the effective graphic density of the feature point according to the specific distribution of the test pattern, so that the etching deviation compensation model can adapt to various shapes of test patterns. At the same time, the etching deviation compensation model can be established more accurately, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[0152] In this embodiment, taking any test point as the feature point, according to 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, including: performing weighted average processing on the initial graphic densities of the feature point and multiple test points around it to obtain the effective graphic density of the feature point, where the weights of the test points at equal distances from the feature point are respectively configured.

[0153] Performing weighted average processing on the initial graphic densities of the feature point and multiple test points around it can take into account the influence of multiple test points around the feature point to obtain the effective graphic density of the feature point. Among them, the weights of the test points at equal distances from the feature point are respectively configured, so that the weights of each test point in obtaining the effective graphic density of the feature point can be flexibly adjusted according to the specific distribution of the test pattern, so that the etching deviation compensation model can adapt to various shapes of test patterns. At the same time, the etching deviation compensation model can be established more accurately, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[0154] In this embodiment, performing weighted average processing on the initial graphic densities of the feature point and multiple test points around it to obtain the effective graphic density of the feature point includes: convolving the graphic density distribution of the feature point and multiple test points around it with a filtering function to obtain the effective graphic density of the feature point, where the function values of the filtering function at the same distance from the feature point are different.

[0155] A filter function is convolved with the graphic density distribution of a feature point and multiple test points around the feature point. The convolution method can be used to calculate the weighted average of the influence of multiple test points around the feature point on the feature point. 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. The weight value of each test point in the process of obtaining the effective graphic density of the feature point can be flexibly obtained according to the specific distribution of the test graphic, thereby making the etching deviation compensation model adaptable to test graphics of various shapes. At the same time, the etching deviation compensation model can be established more accurately, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[0156] In this embodiment, a filter function is used in the convolution of the graphic density distribution of the feature point and multiple test points around it. The filter function includes a Gaussian function. The Gaussian function has a first variance parameter along the x-axis and a second variance parameter along the y-axis. The x-axis and the 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 with a solution in which the Gaussian function has only one variance parameter, the Gaussian function has two variance parameters, which increases the adjustable parameters of the Gaussian function, is conducive to flexible selection of the two variance parameters, and is adapted to various distributions of test patterns. Moreover, in the process of establishing the etching deviation compensation model, according to the initial graphic density distribution of multiple test points around the feature point, a more accurate Gaussian function can be obtained by adjusting the two parameters of the Gaussian function, thereby more accurately allocating the weight of the initial graphic density of the feature point and the multiple test points around it in the weighted averaging processing, and more accurately establishing the etching deviation compensation model, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[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 of the Gaussian function includes: Among them, σ x is the first variance parameter, σ y is the second variance parameter, (x 0 ,y 0 ) is the coordinate of the feature point, x is the horizontal coordinate of the feature point and the test point along the x-axis, and y is the vertical coordinate of the feature point and the test point along the y-axis.

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

[0161] In this embodiment, in the Gaussian function, σ x With σ y Not equal.

[0162] σ x With σ y If they are not equal, the two adjustable parameters of the Gaussian function are not equal, which is conducive to adapting to asymmetrically arranged test patterns. In the process of establishing the etching deviation compensation model, it is conducive to more accurately establishing the etching deviation compensation model, 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 the feature point includes: in, Den is the effective pattern density, local (x,y) is the initial graphic density, f(x,y) is the filter function, is the convolution operator symbol.

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

[0165] It should be noted that, in this embodiment, the filter function is not limited to the formation of a Gaussian function, and the filter function also includes other forms of functions. For example, the filter function can also be a Cauchy (Lorentz) distribution function. Specifically, the expression is: Among them, γ x is the first parameter, γ y is the second parameter, (x 0 ,y 0 ) is the coordinate of the feature point, x is the horizontal coordinate of the feature point and the test point along the x-axis, and y is the vertical coordinate 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] The actual etching pattern corresponding to the test pattern is obtained to calibrate the estimated size of the feature point.

[0168] Due to the etching process and the arrangement of the test pattern, there is an error in the size of the actual etched pattern and the test pattern. Therefore, the estimated size of the feature point is calibrated 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 etching pattern and obtain the actual size deviation of the corresponding feature point.

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

[0171] The actual size deviation of the corresponding feature point is obtained, which is used as a basis for obtaining the etching deviation compensation value corresponding to the feature point.

[0172] The estimated size deviation acquisition module 506 is used to obtain the estimated size deviation of the feature point according to the effective pattern density.

[0173] The estimated size deviation refers to the CDloading of the final graphics predicted by the model due to the CD loading effect.

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

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

[0176] Performing polynomial fitting to obtain a fitting function can infer an unknown parameter from existing data, thereby effectively predicting unknown values, that is, it is possible to obtain a fitting function of the effective graphic density from the existing effective graphic density of multiple feature points, so that the estimated size deviation corresponding to the feature point can be predicted 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 of the estimated size deviation and the effective graphic density, including: setting initial polynomial coefficients of the fitting function; and obtaining an initial estimated size deviation according to the initial polynomial coefficients.

[0178] As an example, in this embodiment, the expression of the fitting function includes: in, is the effective graphic density of the feature point, K n is the nth initial polynomial coefficient.

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

[0180] The judgment module 507 is used to judge 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 degree of influence of the initial graphic density of multiple test points around the feature point on the feature point, and return to execute with any test point as the feature point, and 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 located around the feature point on the feature point.

[0181] After obtaining the estimated size deviation corresponding to the feature point through the fitting function, it is determined whether the estimated size deviation meets the preset conditions to determine whether the compensation model is successfully established.

[0182] In this embodiment, in determining whether the estimated size deviation meets a preset condition based on the estimated size deviation and the actual size deviation, the least square method is used to determine whether the estimated size deviation meets the preset condition.

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

[0184] In this embodiment, the least square 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 completed.

[0186] As an example, in this embodiment, using the expression Obtain the sum of squares of the differences between the initial estimated size deviation and the actual size deviation of the plurality of feature points, L j* This is the actual size 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 execution returns to 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 located 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 conditions, and the initial graphic density of multiple test points around the feature point is adjusted to determine the degree of influence on the feature point. The initial polynomial coefficients are also adjusted, and the execution is returned to 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 located around the feature point on the feature point.

[0190] For the feature point, the actual value of CD loading (i.e. the actual size deviation L j* ) and the fitting value (i.e. the estimated size deviation L j ) There is a certain difference between them. When the sum of squares is greater than the preset threshold, it means that the difference between the two is still large, that is, L j* With L j If the sum of the squares of the differences is large, it means that the established etching bias compensation model cannot predict the CD loading effect well, and thus the established etching bias compensation model cannot achieve a more accurate etching bias compensation effect. Therefore, it is necessary to adjust the initial polynomial coefficients and return to execute any test point as the feature point. According to the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points located around the feature point on the feature point, the effective graphic density of the feature point is obtained, and continuous iterative processing is performed until the sum of the 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, it is also possible, according to actual needs, to first adjust the initial polynomial coefficients, and return to execute to obtain the initial estimated size deviation based on the initial polynomial coefficients, and perform continuous iterative processing within the fitting function step. After multiple iterative processing, if the sum of squares is still greater than the preset threshold, the initial polynomial coefficients and the degree of influence of the initial graphic density of multiple test points around the feature point on the feature point can be adjusted at the same time, and return to execute with any test point as the feature point, according to the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points located around the feature point on the feature point, to obtain the effective graphic density of the feature point, which is conducive to saving iterative computing power and improving iterative computing efficiency, thereby improving 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] Correspondingly, in this embodiment, when the predicted size deviation does not meet the preset condition, the influence degree 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 establishment module 508 is used to complete the establishment of the etching deviation compensation model when the predicted size deviation meets the preset condition.

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

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

[0197] In this embodiment, after the establishment of the etching deviation compensation model, it further includes: verifying the etching deviation compensation model.

[0198] Specifically, in this embodiment, verifying the etching deviation compensation model includes: 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, there is an error in the size between the actually etched pattern and the test pattern. Therefore, according to the etching deviation compensation model, the etching deviation compensation value corresponding to the test pattern is set, so that the compensation value of the etching deviation can be obtained to compensate the test pattern, thereby alleviating the CD loading effect of the pattern.

[0200] Correspondingly, in this embodiment, the predicted size of the test pattern is obtained according to the etching deviation compensation value.

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

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

[0203] The actual size corresponding to the test pattern is obtained to determine whether the etching deviation compensation model has passed the verification.

[0204] Specifically, in this embodiment, it is determined whether the etching deviation compensation model has passed the verification based on the predicted size and the actual size.

[0205] In this embodiment, judging whether the etching deviation compensation model passes the verification according to the predicted size and the actual size includes: obtaining the difference between the predicted size and the actual size; and judging 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 actual etching pattern and the pattern predicted by the etching deviation compensation model, so as to judge whether the etching deviation compensation model has passed the verification by judging whether the difference is less than or equal to the preset difference threshold.

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

[0208] In this embodiment, when the etching deviation compensation model fails to pass 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 a 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, so as to obtain the final etching deviation compensation model.

[0211] Correspondingly, an embodiment of the present invention further provides an etching deviation compensation model, including an etching deviation compensation model obtained by using the method for establishing an etching deviation compensation model provided by an embodiment of the present invention.

[0212] It can be seen from the aforementioned embodiments that compared with the scheme of isotropically setting the influence of the initial graphic 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, so the influence of the initial graphic density of multiple test points around the feature point on the feature point is anisotropically set, and the influence of each test point in obtaining the effective graphic density of the feature point can be flexibly adjusted according to the specific distribution of the test pattern, so that the etching deviation compensation model is adapted to test patterns of various shapes. At the same time, the etching deviation compensation model can be established more accurately, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[0213] Correspondingly, an embodiment of the present invention further provides an etching deviation compensation method, including an etching deviation compensation method based on an etching deviation compensation model established by the method for establishing an etching deviation compensation model provided by the embodiment of the present invention.

[0214] It can be seen from the aforementioned embodiments that compared with the scheme of isotropically setting the influence of the initial graphic 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, so the influence of the initial graphic density of multiple test points around the feature point on the feature point is anisotropically set, and the influence of each test point in obtaining the effective graphic density of the feature point can be flexibly adjusted according to the specific distribution of the test pattern, so that the etching deviation compensation model is adapted to test patterns of various shapes. At the same time, the etching deviation compensation model can be established more accurately, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[0215] The embodiment of the present invention further provides a device, which can implement the method for establishing an etching deviation compensation model provided by the embodiment of the present invention by loading the method for establishing an etching deviation compensation model in the form of a program. An optional hardware structure of the terminal device provided by the embodiment of the present invention can be as follows: Fig. 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 the processor 01, communication interface 02, and memory 03 complete mutual communication through the communication bus 04. The communication interface 02 can be an interface of a communication module for network communication, such as an interface of a GSM module. The processor 01 may be a central processing unit CPU, or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement an embodiment of the present invention. The memory 03 may include a high-speed RAM memory, and may also include a non-volatile memory (NVM), such as at least one disk storage. Among them, the memory 03 stores one or more computer instructions, and the one or more computer instructions are executed by the processor 01 to implement the method for establishing the etching deviation compensation model provided in the embodiment of the present invention.

[0217] It should be noted that the above-mentioned terminal device may also include other devices (not shown) that may not be necessary for understanding the contents disclosed in the embodiments of the present invention; given that these other devices may not be necessary for understanding the contents disclosed in the embodiments of the present invention, the embodiments of the present invention will not introduce them one by one.

[0218] An embodiment of the present invention further provides a storage medium, wherein the storage medium stores one or more computer instructions, and the one or more computer instructions are used to implement the method for establishing an etching deviation compensation model provided by the embodiment of the present invention.

[0219] In the method for establishing an etching deviation compensation model provided in an embodiment of the present invention, compared with a scheme of isotropically setting the influence of the initial graphic density of multiple test points around a feature point on the feature point, in an embodiment 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, so the influence of the initial graphic density of multiple test points around the feature point on the feature point is anisotropically set, and the influence of each test point in obtaining the effective graphic density of the feature point can be flexibly adjusted according to the specific distribution of the test pattern, so that the etching deviation compensation model is adapted to test patterns of various shapes. At the same time, the etching deviation compensation model can be established more accurately, thereby improving the effect of etching deviation compensation using the etching deviation compensation model.

[0220] The above-mentioned embodiments of the present invention are combinations of elements and features of the present invention. Unless otherwise mentioned, elements or features may be considered as optional. Each element or feature may be put into practice without being combined with other elements or features. In addition, 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 configurations of any one embodiment may be included in another embodiment, and may be replaced by the corresponding configuration of another embodiment. It is obvious to those skilled in the art that claims that do not have a clear reference relationship to each other in the attached claims may be combined into embodiments of the present invention, or may be included as new claims in the amendment after submitting this application.

[0221] Embodiments of the present invention may be implemented by various means such as hardware, firmware, software or a combination thereof. In a hardware configuration, the method according to an exemplary embodiment of the present invention may 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 may be implemented in the form of modules, processes, functions, etc. The software code may be stored in a memory unit and executed by a processor. The memory unit is located inside or outside the processor and may send data to the processor 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 implement or use the present invention. Various modifications to these embodiments will be 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 present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0223] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the scope defined by the claims.

Claims

1. A method for establishing an etching deviation compensation model, It is characterized in that include: Providing a test layout, wherein the test layout includes a test pattern, and the test layout includes a plurality of test points; According to the test pattern, obtaining the initial pattern density of each test point; 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 multiple test points around the feature point on the feature point, wherein the influence degree of the initial pattern density of multiple test points around the feature point on the feature point is set anisotropically; Obtaining an actual etching pattern corresponding to the test pattern; Measuring the actual etched pattern to obtain actual size deviation of corresponding feature points; Obtaining an estimated size deviation of the feature point according to the effective pattern density; According to the estimated size deviation and the actual size deviation, determining whether the estimated size deviation meets a preset condition; When the estimated size deviation does not meet the preset condition, adjusting the influence of the initial graphic density of multiple test points around the feature point on the feature point, and returning to execute taking any test point as the feature point, and obtaining the effective graphic density of the feature point according to 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; When the estimated size deviation meets the preset condition, the establishment of the etching deviation compensation model is completed.

2. The method for establishing the etching deviation compensation model according to claim 1, It is characterized in that Providing the test layout, wherein the test layout includes test windows corresponding to the test points one by one; According to 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 according to claim 1, It is characterized in that Taking any test point as a feature point, obtaining the effective graphic density of the feature point according to the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points located around the feature point on the feature point, including: performing weighted average processing on the initial graphic density of the feature point and the multiple test points around it to obtain the effective graphic density of the feature point, wherein the weights of the test points that are equal to the feature point are respectively configured; When the estimated size deviation does not meet the preset condition, adjusting the influence of the initial graphic density of multiple test points around the feature point on the feature point includes: adjusting the weight of the initial graphic density of multiple test points around the feature point.

4. The method for establishing an etching deviation compensation model as claimed in claim 3, It is characterized in that Performing weighted average processing 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, including: using a filter function to convolve with the graphic density distribution of the feature point and multiple test points around it 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, adjusting the influence of the initial graphic density of multiple test points around the feature point on the feature point includes: adjusting the parameters of the filter function.

5. The method for establishing an etching deviation compensation model according to claim 4, It is characterized in that A filter function is used to convolve the graphic density distribution of the feature point and multiple test points around it, wherein the filter function includes a Gaussian function having a first variance parameter along the x-axis and a second variance parameter along the y-axis, and the x-axis and the y-axis are perpendicular to each other.

6. The method for establishing an etching deviation compensation model according to claim 5, It is characterized in that The expression of the Gaussian function includes: Among them, σ x is the first variance parameter, σ y is the second variance parameter, (x 0 ,y 0 ) is the coordinate of the feature point, x is the horizontal coordinate of the feature point and the test point along the x-axis, and y is the vertical coordinate of the feature point and the test point along the y-axis.

7. The method for establishing an etching deviation compensation model according to claim 6, It is characterized in that In the Gaussian function, σ x With σ y Not equal.

8. The method for establishing an etching deviation compensation model according to claim 1, It is characterized in that According to the effective graphic density, the estimated size deviation of the feature point is obtained, including: performing polynomial fitting on the effective graphic density of multiple feature points to obtain a fitting function of the estimated size deviation and the effective graphic density; and obtaining the estimated size deviation corresponding to the feature point through the fitting function.

9. The method for establishing an etching deviation compensation model according to claim 8, It is characterized in that In determining whether the estimated size deviation satisfies a preset condition according to the estimated size deviation and the actual size deviation, a least square method is used to determine whether the estimated size deviation satisfies the preset condition.

10. The method for establishing an etching deviation compensation model according to claim 9, It is characterized in that Performing polynomial fitting on the effective graphic density of the plurality of feature points to obtain a fitting function of the estimated size deviation and the effective graphic density, including: setting initial polynomial coefficients of the fitting function; Obtaining an initial estimated size deviation according to the initial polynomial coefficients; Determining whether the estimated size deviation meets a preset condition by using the least square method includes: obtaining the sum of squares of the differences between the initial estimated size deviation and the actual size deviation of the plurality of feature points; Determining whether the sum of squares is less than or equal to a preset threshold; When the square sum is less than or equal to a preset threshold, the estimated size deviation meets the preset condition, the initial polynomial coefficients are obtained as the polynomial coefficients, and the establishment of the etching deviation compensation model is completed; When the sum of squares is greater than a preset threshold, the estimated size deviation does not meet the preset condition, and the influence of the initial graphic density of multiple test points around the feature point on the feature point is adjusted. The initial polynomial coefficient is also adjusted, and the execution returns to any test point as the feature point. According to the initial graphic density of each feature point and the influence of the initial graphic density of multiple test points located around the feature point on the feature point, the effective graphic density of the feature point is obtained.

11. The method for establishing an etching deviation compensation model according to claim 10, It is characterized in that The expression of the fitting function includes: in, is the effective graphic density of the jth feature point, K n is the nth initial polynomial coefficient.

12. The method for establishing an etching deviation compensation model according to claim 10, It is 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 the plurality of feature points, L j* This is the actual size deviation.

13. The method for establishing an etching deviation compensation model according to claim 1, It is characterized in that After the etching deviation compensation model is established, the method further includes: setting an etching deviation compensation value corresponding to a test pattern according to the etching deviation compensation model; According to the etching deviation compensation value, the predicted size of the test pattern is obtained; Get the actual size of the test pattern; According to the predicted size and the actual size, determine whether the etching deviation compensation model has passed the verification; When the etching deviation compensation model passes the verification, no correction processing is performed on the etching deviation compensation model; When the etching deviation compensation model fails to pass the verification, the etching deviation compensation model is corrected.

14. The method for establishing an etching deviation compensation model according to claim 13, It is characterized in that According to the predicted size and the actual size, judging whether the etching deviation compensation model passes the verification includes: obtaining the difference between the predicted size and the actual size; judging whether the difference is less than or equal to a preset difference threshold.

15. A system for establishing an etching deviation compensation model. It is characterized in that include: A layout providing module, used for providing a test layout, wherein the test layout includes a test pattern, and the test layout includes a plurality of test points; An initial pattern density acquisition module, used to obtain the initial pattern density of each test point according to the test pattern; An effective graphic density acquisition module is used to take any test point as a feature point, and obtain the effective graphic density of the feature point according to 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, wherein the influence degree of the initial graphic density of multiple test points around the feature point on the feature point is set anisotropically; An actual etching pattern acquisition module, used to acquire an actual etching pattern corresponding to the test pattern; An actual size deviation acquisition module is used to measure the actual etching pattern and obtain the actual size deviation of the corresponding feature point; An estimated size deviation acquisition module, used to obtain the estimated size deviation of the feature point according to the effective pattern density; A judgment module, configured to judge whether the estimated size deviation meets a preset condition according to the estimated size deviation and the actual size deviation; 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 taking any test point as the feature point, and obtaining the effective graphic density of the feature point according to 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 model building module is used to complete the building of the etching deviation compensation model when the estimated size deviation meets the preset condition.

16. An etching deviation compensation model established by the method according to any one of claims 1 to 14.

17. An etching deviation compensation method based on an etching deviation compensation model established by the method according to any one of claims 1 to 14.

18. A device, It is characterized in that It comprises at least one memory and at least one processor, wherein the memory stores 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 to 14.

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

Images