Characteristic space-based morphology parameter extraction method and device, medium and electronic equipment

Abstract

The invention relates to the technical field of semiconductor detection, in particular to a feature space-based morphological parameter extraction method and device, a medium and electronic equipment, and the method mainly comprises the steps: carrying out feature space-based morphological parameter extraction on an actual measurement scattering spectrum of a to-be-detected nanostructure, and obtaining a first to-be-detected parameter of the to-be-detected nanostructure; on the basis of the parameter model of the to-be-measured nanostructure, to-be-measured morphology parameters of the to-be-measured nanostructure are determined; based on the to-be-measured morphology parameter and the first to-be-measured parameter, obtaining a theoretical scattering spectrum of the to-be-measured nanostructure; determining an evaluation function based on the actually measured scattering spectrum and the theoretical scattering spectrum; adjusting the to-be-measured morphology parameters based on the evaluation function to obtain target to-be-measured parameters of the to-be-measured nanostructure; according to the method, a reasonable initial value can be provided for a model-based parameter extraction method through a parameter extraction method of feature space information, so that convergence to comprehensive understanding is faster, the time required for optimization iteration is shortened, and accurate and efficient morphology parameter extraction is realized.

Description

Technical Field

[0001] The present invention belongs to the field of semiconductor detection technology, and in particular relates to a method, device, medium and electronic equipment for extracting morphological parameters based on feature space. Background Art

[0002] With the advancement of semiconductor manufacturing technology, the structural design of integrated circuit (IC) devices has gradually shifted from simple two-dimensional planar structures to more complex three-dimensional structures. These three-dimensional structures usually have smaller feature dimensions (CD) and more stacked layers, forming unique high aspect ratio (HAR) features.

[0003] At present, the mainstream IC device measurement technology includes two imaging technologies, namely critical dimension scanning electron microscopy (CD-SEM) and critical dimension atomic force microscopy (CD-AFM), as well as a measurement technology based on optical critical dimension (OCD). OCD has become the main means of online measurement in current IC manufacturing due to its advantages such as fast measurement speed, non-contact, non-destructive and easy online integration, and its ability to provide statistical information of the structure to be measured. One development trend of OCD is to adopt shorter detection wavelengths, such as EUV band and X-ray band. Among them, the critical dimension small angle X-ray scattering measurement technology (CD-SAXS) is listed as one of the main means of online measurement of next-generation IC devices in the International Semiconductor Technology Roadmap because of its ability to measure high-density, high aspect ratio structures and buried layer structures.

[0004] When measuring IC nanostructure morphology based on small-angle X-ray scattering, it is usually impossible to directly extract the parameters to be measured from the measured data. At present, the more conventional parameter extraction methods include parameter extraction methods based on fitting reciprocal space maps (RSM) and parameter extraction methods based on fitting scattering intensity slice maps. The parameter extraction method based on fitting reciprocal space maps usually relies on computationally intensive nonlinear regression algorithms to accurately match theoretical and measured data, involving a large amount of data, more redundant information, and low computational efficiency; the parameter extraction method based on fitting scattering intensity slice maps requires the collection of enough rotation angle information, which puts high requirements on the selection of measurement angles and measurement time, otherwise it may affect the robustness of the parameter extraction results. Both methods are model-based parameter extraction methods. The selection of the initial value of the parameter to be measured directly affects the convergence speed and direction of the optimization process. Therefore, a reasonable initial value is crucial, which can help the optimization algorithm find the correct solution more quickly and avoid falling into the local optimal solution. At the same time, with the increase in the number of parameters to be measured, the coupling effect between parameters becomes more obvious, which makes the selection of initial values ​​more critical to ensure the accuracy and stability of parameter extraction. Summary of the invention

[0005] In view of this, the present invention aims to provide a method, device, medium and electronic device for extracting morphological parameters based on feature space. The parameter extraction method combined with feature space information can provide reasonable initial values ​​for the parameter extraction method based on the model, thereby promoting the optimization algorithm to converge to a reasonable solution faster, shortening the time required for optimization iterations, and is particularly suitable for high-dimensional parameter space, so that the extraction of morphological parameters is both accurate and efficient, and has broad application prospects.

[0006] To achieve the above object, the technical solution created by the present invention is implemented as follows: A method for extracting morphological parameters based on feature space comprises: irradiating a nanostructure to be measured with X-rays at different incident angles; receiving X-rays scattered by the nanostructure to be measured with a detector to obtain a measured scattering spectrum; extracting morphological parameters based on feature space on the measured scattering spectrum to obtain a first parameter to be measured of the nanostructure to be measured; determining the morphological parameters to be measured of the nanostructure to be measured based on a parameter model of the nanostructure to be measured; obtaining a theoretical scattering spectrum of the nanostructure to be measured based on the morphological parameters to be measured and the first parameter to be measured; determining an evaluation function based on the measured scattering spectrum and the theoretical scattering spectrum; and adjusting the morphological parameters to be measured based on the evaluation function to obtain a target parameter to be measured of the nanostructure to be measured.

[0007] In some embodiments, before extracting the morphological parameters of the measured scattering spectrum based on the feature space to obtain the first parameter to be measured of the nanostructure to be measured, it also includes: determining the incident angle range, and determining the number of incident angles based on the incident angle range and the preset incident angle step; for the measured scattering spectrum corresponding to each incident angle, determining the scattering peak involved in the morphological parameter extraction.

[0008] In some embodiments, in the process of extracting morphological parameters based on feature space from the measured scattering spectrum to obtain the first parameter to be measured of the nanostructure to be measured: constructing the feature space based on at least one of the autocorrelation function, machine learning and the measured scattering spectrum; and obtaining the first parameter to be measured based on the feature space.

[0009] In some embodiments, based on the feature space, obtaining the first parameter to be measured includes: obtaining the corresponding first parameter based on the autocorrelation function; obtaining the corresponding second parameter based on machine learning; obtaining the corresponding third parameter based on the measured scattering spectrum; when the feature space includes one of the autocorrelation function, machine learning and the measured scattering spectrum, one of the corresponding first parameter, second parameter and third parameter is used as the first parameter to be measured; when the feature space includes at least two of the autocorrelation function, machine learning and the measured scattering spectrum, the first parameter to be measured is determined based on at least two of the corresponding first parameter, second parameter and third parameter.

[0010] In some embodiments, in the process of obtaining the corresponding first parameter based on the autocorrelation function: the autocorrelation function distribution of the nanostructure to be measured is obtained based on the measured scattering spectrum; then the electron density distribution of the nanostructure to be measured is obtained according to the autocorrelation function distribution, and the first parameter is determined based on the electron density distribution.

[0011] In some embodiments, the process of obtaining the distribution of the electron density of the nanostructure to be measured according to the autocorrelation function distribution is as follows: ; in, represents the structural spacing of the nanostructure to be measured at the jth position, represents the autocorrelation function distribution, represents the electron density distribution.

[0012] In some embodiments, in the process of obtaining the corresponding second parameter based on machine learning: the autocorrelation function distribution of the electron density distribution of the nanostructure to be measured is obtained based on the measured scattering spectrum; the mapping relationship between the autocorrelation function distribution and the structural information of the nanostructure to be measured is obtained through machine learning training to obtain the structural information; and the second parameter is determined based on the structural information.

[0013] In some embodiments, in the process of obtaining the corresponding third parameter based on the measured scattering spectrum: the third parameter is obtained based on the position information between the scattering peaks in the measured scattering spectrum.

[0014] In some embodiments, in the process of obtaining a theoretical scattering spectrum of the nanostructure to be measured based on the measured morphological parameter and the first measured parameter: the theoretical scattering spectrum is calculated by the following formula: ; in, Represents the morphological parameters to be measured, and the morphological parameters to be measured Including the first parameter to be measured, q represents the q vector at different reciprocal space positions, represents the theoretical scattering spectrum, represents the scale factor, represents the instrument coefficient, represents the shape factor, represents the structure factor, represents the roughness factor, Represents the background scattering intensity.

[0015] In some embodiments, in the process of adjusting the morphological parameters to be measured based on the evaluation function to obtain the target parameters to be measured of the nanostructure to be measured: taking the evaluation function as the optimization target, optimizing the morphological parameters to be measured using the optimization algorithm, so that the morphological parameters to be measured obtained when the evaluation function reaches the optimization termination condition are used as the target parameters to be measured.

[0016] A morphology parameter extraction device based on feature space comprises: an X-ray scattering pattern generation module, used for subjecting a nanostructure to be measured to X-ray scattering and obtaining a measured scattering pattern; a parameter extraction module, used for obtaining a first parameter to be measured and a morphology parameter to be measured of the nanostructure to be measured according to the measured scattering pattern and a parameter model of the nanostructure to be measured; and a target parameter generation module, used for obtaining a target parameter to be measured of the nanostructure to be measured according to the morphology parameter to be measured and the first parameter to be measured.

[0017] In some embodiments, the X-ray scattering pattern generation module includes: an X-ray source, used to emit X-rays to the nanostructure to be measured; a rotating worktable, used to carry and rotate the nanostructure to be measured so that the X-rays are irradiated to the nanostructure to be measured at different incident angles; and a detector, used to receive the X-rays scattered by the nanostructure to be measured to obtain a measured scattering pattern.

[0018] A readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the feature space-based morphology parameter extraction method provided by the present invention are implemented.

[0019] An electronic device comprises: a memory for storing a computer program; and a processor for implementing the steps of a feature space-based morphology parameter extraction method provided by the present invention when executing the computer program.

[0020] Compared with the prior art, the invention can achieve the following beneficial effects: In the feature space-based morphology parameter extraction method created by the present invention, first, the function distribution characteristics in the feature space are used to obtain information on some parameters to be measured (first parameters to be measured) of the nanostructure to be measured, and this information will be used as the initial value for subsequent parameter extraction using a model-based parameter extraction method; then, the model-based parameter extraction method is used to further accurately extract the morphology parameters to be measured of the nanostructure to be measured, and an evaluation function and an optimization algorithm are used to adjust the morphology parameters to be measured, thereby completing the solution process of the inverse scattering problem; compared with the traditional single model-based parameter extraction method, the method provided by the present invention can provide a reasonable initial value for the model-based parameter extraction method through a parameter extraction method based on feature space information, thereby promoting the optimization algorithm to converge to a reasonable solution faster, shortening the time required for optimization iteration, and is particularly suitable for high-dimensional parameter space, so that the extraction of morphology parameters is both accurate and efficient, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings constituting part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments and descriptions of the present invention are used to explain the present invention and do not constitute an improper limitation on the present invention. In the drawings: Figure 1 A schematic diagram of a flow chart of a method for extracting morphological parameters based on feature space according to an embodiment of the present invention; Figure 2 A schematic diagram of X-ray scattering measurement according to an embodiment of the present invention; Figure 3 A schematic diagram of obtaining a first parameter to be measured through an autocorrelation function according to an embodiment of the present invention; Figure 4 A schematic diagram of obtaining a first parameter to be measured by machine learning according to an embodiment of the present invention; Figure 5 A schematic diagram of obtaining a first parameter to be measured by measuring a scattering spectrum as described in an embodiment of the present invention; Figure 6 A schematic diagram of the structure of a morphology parameter extraction device based on feature space according to an embodiment of the present invention; Figure 7 A schematic diagram of the structure of an electronic device according to an embodiment of the present invention.

[0022] Description of reference numerals: 1. Nanostructure to be measured; 2. Detector; 3. X-ray scattering map generation module; 4. Parameter extraction module to be measured; 5. Target parameter generation module to be measured; 6. Electronic device; 7. External device; 8. Processing unit; 9. Bus; 10. Network adapter; 11. Display; 12. (I / O) interface; 13. System memory; 14. Random access memory; 15. Cache memory; 16. Storage system; 17. Utility; 18. Program module. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the invention more clear, the invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described here are only used to explain the invention and do not constitute a limitation of the invention.

[0024] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0025] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0026] In the description of the invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installation", "connection" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the invention can be understood according to specific circumstances.

[0027] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments.

[0028] like Figure 1 As shown, the method for extracting morphological parameters based on feature space described in the embodiment of the present invention includes: S1: Use X-rays to irradiate the nanostructure to be tested at different incident angles.

[0029] Specific, combined Figure 2 , place the nanostructure 1 to be measured on a rotatable sample stage so that the incident beam is aligned with the center of the rotation axis. During the scattering measurement, the nanostructure 1 to be measured needs to be rotated around q y Axis rotation through a range of angles S2: Using a detector to receive X-rays scattered by the nanostructure to be measured, and obtaining a measured scattering spectrum.

[0030] Understandable, combined Figure 2 , X-rays with different incident angles hit the nanostructure 1 to be measured, and the X-rays scattered by the nanostructure 1 to be measured are scattered at different angles. Enter the detector 2 and form a measured scattering spectrum, which includes two types of data, namely, q vectors at different reciprocal space positions and scattering intensities corresponding to the q vector positions.

[0031] In some embodiments, before extracting the morphological parameters of the measured scattering spectrum based on the feature space to obtain the first parameter of the nanostructure to be measured, the method further includes: Determining an incident angle range, and determining the number of incident angles according to the incident angle range and a preset incident angle step; For each incident angle corresponding to the measured scattering spectrum, the scattering peak involved in the extraction of morphological parameters is determined.

[0032] It is understandable that after step S2, it is also necessary to determine the scattering peak involved in the extraction of morphological parameters. This process includes: determining the incident angle range ( ), and according to the incident angle range ( ) and the preset incident angle step size , determine the number of incident angles as ; For each incident angle corresponding to the measured scattering spectrum, determine the scattering peak involved in the extraction of morphological parameters. The q vector range of the scattering peak involved in the extraction of morphological parameters is The determination method of the scattering peak involved in the extraction of morphological parameters includes selection based on relevant algorithms and artificial adaptation according to actual conditions, and the present invention does not limit this.

[0033] S3: extracting morphological parameters of the measured scattering spectrum based on the feature space to obtain the first parameter to be measured of the nanostructure to be measured.

[0034] In some embodiments, a feature space is constructed based on at least one of an autocorrelation function, machine learning, and a measured scattering spectrum; and a first parameter to be measured is obtained based on the feature space.

[0035] Specifically, the feature space can be constructed using the autocorrelation function alone, or using machine learning alone, or using the measured scattering pattern alone. Furthermore, the feature space can be constructed using the autocorrelation function and machine learning, or the autocorrelation function and the measured scattering pattern, or machine learning and the measured scattering pattern. Finally, the feature space can be constructed using the autocorrelation function, machine learning and the measured scattering pattern.

[0036] In a specific embodiment, based on the feature space, obtaining the first parameter to be measured includes: obtaining the corresponding first parameter based on the autocorrelation function; obtaining the corresponding second parameter based on machine learning; obtaining the corresponding third parameter based on the measured scattering spectrum; when the feature space includes one of the autocorrelation function, machine learning and the measured scattering spectrum, one of the corresponding first parameter, second parameter and third parameter is used as the first parameter to be measured; when the feature space includes at least two of the autocorrelation function, machine learning and the measured scattering spectrum, the first parameter to be measured is determined based on at least two of the corresponding first parameter, second parameter and third parameter.

[0037] Optionally, in a specific embodiment, in the process of obtaining the corresponding first parameter based on the autocorrelation function: obtaining the autocorrelation function distribution of the nanostructure to be measured based on the measured scattering spectrum; obtaining the electron density distribution of the nanostructure to be measured according to the autocorrelation function distribution; and determining the first parameter based on the electron density distribution.

[0038] Specifically, the autocorrelation function is used to perform inverse Fourier transform on the measured scattering spectrum to obtain the autocorrelation function distribution of the nanostructure to be measured, that is: ; Where r represents the structural spacing of the nanostructure to be measured, represents the autocorrelation function distribution, represents the measured scattering spectrum, represents inverse Fourier transform; Then, the electron density distribution of the nanostructure to be measured is obtained according to the distribution of the autocorrelation function, namely: ; in, represents the structural spacing of the nanostructure to be measured at the jth position, represents the electron density distribution.

[0039] Since the distribution of the electron density of the nanostructure to be tested directly reflects the distribution of the spacing between the structures to be tested, it can reveal the morphological characteristics of the nanostructure to be tested, such as Figure 3 As shown, Figure 3 (a) shows that when the nanostructure to be measured is a typical grating structure, the nanostructure to be measured is along q x The distribution of structural spacing in the direction, Figure 3 (b) shows the distribution of the autocorrelation function. Figure 3 Where c represents the line width of the nanostructure to be measured, d represents the structural spacing of the nanostructure to be measured, and e represents the structural period of the nanostructure to be measured. Figure 3It can be seen that there is a one-to-one correspondence between the distribution of the structure spacing and the distribution of the autocorrelation function. However, due to the lack of phase information, the characteristic space morphology parameter method using the autocorrelation function can only extract the morphology parameters of a part of the nanostructure to be measured, and cannot completely reconstruct the morphology of the nanostructure to be measured. Therefore, the first parameter obtained based on the autocorrelation function is a part of the morphology parameters to be measured. It should be noted that the morphology parameters to be measured include the first parameter to be measured (such as period, line width, etc.) and other morphology parameters (such as inclination, ellipticity, torsion, etc.). When the characteristic space only includes the autocorrelation function, the first parameter obtained by the autocorrelation function is the first parameter to be measured (such as period, line width, etc.).

[0040] Optionally, in a specific embodiment, in the process of obtaining the corresponding second parameter based on machine learning: the autocorrelation function distribution of the electron density distribution of the nanostructure to be measured is obtained based on the measured scattering spectrum; the mapping relationship between the autocorrelation function distribution and the structural information of the nanostructure to be measured is obtained through machine learning training to obtain the structural information; and the second parameter is determined based on the structural information.

[0041] Specifically, the autocorrelation function is used to perform inverse Fourier transform on the measured scattering spectrum to obtain the autocorrelation function distribution of the nanostructure to be measured, such as Figure 4 As shown, Figure 4 (a) shows the nanostructure to be tested along q x The distribution of structural spacing in the direction, Figure 4 (b) shows the distribution of the autocorrelation function. The distribution of the autocorrelation function of the nanostructure to be measured is related to the distribution of the autocorrelation function of the nanostructure to be measured along q x There is no one-to-one correspondence between the structural spacing distribution in the direction, so the mapping relationship between the autocorrelation function distribution and the structural information of the nanostructure to be tested is obtained through machine learning training to obtain the structural information. Figure 4 As shown, the mapping relationship obtained by machine learning is used to obtain the values ​​of line widths a, b, c and d at the four positions of the nanostructure to be measured. Further, the second parameter is determined based on the obtained structural information, wherein the second parameter is a part of the morphological parameters to be measured. It should be noted that the morphological parameters to be measured include the first parameters to be measured (such as period, line width, etc.) and other morphological parameters (such as inclination, ellipticity, torsion, etc.). When the feature space only includes machine learning, the second parameter obtained by machine learning is the first parameter to be measured.

[0042] Optionally, in a specific embodiment, in the process of obtaining the corresponding third parameter based on the measured scattering spectrum: the third parameter is obtained based on position information between scattering peaks in the measured scattering spectrum.

[0043] Exemplarily, since the distance between scattering peaks or the slope of the scattering peaks in the measured scattering spectrum can be directly related to some morphological parameters of the nanostructure to be measured, some morphological parameters (i.e., third parameters) can be directly extracted from the measured scattering spectrum. It should be noted that the morphological parameters to be measured include the first parameter to be measured (such as period, height, etc.) and other morphological parameters (such as inclination, ellipticity, torsion, etc.). When the feature space only includes the measured scattering spectrum, the third parameter obtained from the measured scattering spectrum is the first parameter to be measured. The result of obtaining the corresponding third parameter based on the measured scattering spectrum is as follows: Figure 5 As shown, Figure 5 (a) shows the extraction of the period parameter from the third parameter, Figure 5 (b) shows the height parameter extracted from the third parameter, Figure 5 (c) in FIG. 1 shows the side wall angle parameter extracted from the third parameter. Figure 5 As shown in (a) in the measured scattering spectrum, Direction measures the distance between two adjacent intensity peaks , we can get the nanostructure to be tested in The period parameter Lx of the direction: ; Similarly, we can use the measured scattering spectrum to Direction measures the distance between two adjacent intensity peaks , the nanostructure to be tested is obtained The period parameter Ly of the direction: ; like Figure 5 As shown in (b) in the measured scattering spectrum, Direction measures the distance between two adjacent intensity peaks , we can get the height parameter H of the nanostructure to be measured: ; like Figure 5 As shown in (c) in the figure, when the nanostructure to be measured is rotated to two specific angles, the scattering intensity of all diffraction orders reaches the maximum value, and each main diffraction axis ( Figure 5 The angles α between the peak slope line in (c) and the horizontal axis are the left and right side wall angles of the nanostructure to be measured.

[0044] Optionally, in another specific embodiment, the corresponding first parameter is obtained based on the autocorrelation function; the corresponding second parameter is obtained based on machine learning; and the corresponding third parameter is obtained based on the measured scattering spectrum. When the feature space includes at least two of the autocorrelation function, machine learning, and the measured scattering spectrum, the first parameter to be measured is determined based on at least two of the corresponding first parameter, the second parameter, and the third parameter.

[0045] Specifically, when at least two of the corresponding first parameter, second parameter, and third parameter are different, the union of at least two of the corresponding first parameter, second parameter, and third parameter can be used as the first parameter to be measured. For example, the feature space includes an autocorrelation function and machine learning, and the first parameter obtained by the autocorrelation function includes a 1 、b 1 、c 1 and d 1 , the second parameters obtained by machine learning include f 2 , g 2 and h 2 , then the union of the first and second parameters (a 1 、b 1 、c 1 d 1 、f 2 , g 2 、h 2 ) as the first parameter to be measured; Further, when at least two of the corresponding first parameter, second parameter and third parameter have the same parameter, the same parameters of at least two of the corresponding first parameter, second parameter and third parameter can be averaged first, and then the union of the averaged parameter and the different parameters of at least two of the corresponding first parameter, second parameter and third parameter is used as the first parameter to be measured. For example: when the first parameter obtained by the autocorrelation function includes a 1 、b 1 、c 1 and d 1 , the second parameters obtained by machine learning include b 2 , g 2 and d 2 , then first take the average of the same parameters of the first parameter and the second parameter ( and ), and then the union of the different parameters of the first parameter and the second parameter and the averaged parameters is taken as the first parameter to be measured (a 1 , 、c 1 , , g 2 ).

[0046] Furthermore, when the feature space includes autocorrelation function, machine learning and measured scattering spectrum, the calculation method for determining each parameter in the first parameter to be measured is consistent with the calculation method when two of the autocorrelation function, machine learning and measured scattering spectrum are used, and the present invention will not be elaborated in detail here.

[0047] S4: Determine the morphological parameters of the nanostructure to be measured based on the parameter model of the nanostructure to be measured.

[0048] Specifically, the parameter model of the nanostructure to be measured can be determined based on prior knowledge or other measurement methods (SEM, AFM methods). Exemplarily, the parameter model of the nanostructure to be measured includes a square hole structure, a circular hole structure, etc., and the morphological parameters to be measured in the model are determined based on the parameter model of the nanostructure to be measured. For example: for the nanostructure to be measured with a square hole structure, its morphological parameters to be measured include but are not limited to the electron density, hole width, hole height, side wall angle, inclination, ellipticity and torsion of the material; for the nanostructure to be measured with a circular hole structure, its morphological parameters to be measured include but are not limited to the electron density, hole size, hole depth, inclination, ellipticity and torsion of the material.

[0049] S5: Based on the measured morphological parameter and the first measured parameter, a theoretical scattering spectrum of the measured nanostructure is obtained.

[0050] Exemplarily, on the basis of the above-mentioned embodiment, the parameter model of the nanostructure to be measured is determined by the nanostructure to be measured, and then the morphological parameters to be measured of the nanostructure to be measured are determined by the parameter model of the nanostructure to be measured, and the first parameter to be measured (such as period, line width, line height, side wall angle) of the nanostructure to be measured is obtained by extracting the morphological parameters based on the feature space. At this time, the first parameter to be measured obtained by extracting the morphological parameters of the feature space is used as a known initial value. In addition, the unknown morphological parameters (i.e., other morphological parameters) need to be set as initial values, and then the theoretical scattering spectrum at this time is calculated by the following formula: ; in, represents the morphological parameters to be measured, and the morphological parameters to be measured include the first parameter to be measured, q represents the q vector at different reciprocal space positions, represents the theoretical scattering spectrum corresponding to the measured morphological parameter x, represents the scale factor, represents the instrument coefficient, represents the shape factor corresponding to the measured morphological parameter x, represents the structure factor, represents the roughness factor, Represents the background scattering intensity.

[0051] S6: Determine an evaluation function based on the measured scattering pattern and the theoretical scattering pattern.

[0052] It can be understood that in the process of determining the evaluation function based on the measured scattering spectrum and the theoretical scattering spectrum: the evaluation function is used to calculate the difference between the theoretical scattering spectrum and the measured scattering spectrum. The evaluation function includes but is not limited to the chi-square evaluation function, the logarithmic absolute error evaluation function, and the absolute error evaluation function.

[0053] Taking the chi-square evaluation function as an example, its evaluation function form is as follows: ; in, represents the difference between the theoretical scattering spectrum and the measured scattering spectrum, K represents the total number of scattering peaks in the measured scattering spectrum, represents the scattering intensity of the kth scattering peak in the measured scattering spectrum, represents the scattering intensity of the kth scattering peak in the theoretical scattering spectrum, Represents the weight factor.

[0054] S7: adjusting the morphological parameters to be measured based on the evaluation function to obtain target parameters to be measured of the nanostructure to be measured.

[0055] Specifically, the morphological parameters to be measured can be adjusted directly through the gap between the theoretical scattering spectrum and the measured scattering spectrum to obtain the target parameters to be measured. The morphological parameters to be measured can also be optimized through an optimization algorithm to obtain the target parameters to be measured, where the target parameters to be measured can be understood as the value of the target parameters to be measured.

[0056] In some embodiments, in the process of adjusting the morphological parameters to be measured based on the evaluation function to obtain the target parameters to be measured of the nanostructure to be measured, the evaluation function is used as the optimization target, and the optimization algorithm is used to optimize the morphological parameters to be measured, so that the morphological parameters to be measured obtained when the evaluation function reaches the optimization termination condition are used as the target parameters to be measured.

[0057] Among them, the optimization algorithm includes but is not limited to a genetic algorithm, a differential evolution algorithm, a covariance matrix adaptive evolution strategy, and a Markov chain-Monte Carlo algorithm. In this embodiment, the optimization algorithm is used to optimize the measured morphological parameters. When the number of iterations reaches a set number, or the value of the corresponding evaluation function obtained after optimization is lower than the preset minimum evaluation function value, the optimization termination condition is reached. The measured morphological parameters obtained at this time are used as the target measured parameters, that is, the value of the measured morphological parameters obtained when the optimization termination condition is reached is the value corresponding to the target measured parameters.

[0058] A device for extracting morphological parameters based on feature space, such as Figure 6As shown, the morphology parameter extraction method based on feature space according to the embodiment of the invention includes an X-ray scattering pattern generation module 3, a parameter extraction module 4 and a target parameter generation module 5. The X-ray scattering pattern generation module 3 is used to make the nanostructure to be measured perform X-ray scattering and obtain a measured scattering pattern; the parameter extraction module 4 is used to obtain a first parameter to be measured and a morphology parameter to be measured of the nanostructure to be measured according to the measured scattering pattern and the parameter model of the nanostructure to be measured; the target parameter generation module 5 is used to obtain a target parameter to be measured of the nanostructure to be measured according to the morphology parameter to be measured and the first parameter to be measured.

[0059] In some embodiments, the X-ray scattering pattern generation module 3 includes an X-ray source, a rotating table, and a detector. The X-ray source emits X-rays to the nanostructure to be measured, the rotating table carries and rotates the nanostructure to be measured, so that the X-rays irradiate the nanostructure to be measured at different incident angles, and the detector receives the X-rays scattered by the nanostructure to be measured to obtain a measured scattering pattern.

[0060] In some embodiments, in the parameter extraction module 4, the measured scattering pattern obtained by the X-ray scattering pattern generation module 3 is subjected to morphological parameter extraction based on the feature space to obtain the first parameter of the nanostructure to be measured, and the morphological parameter of the nanostructure to be measured is determined based on the parameter model of the nanostructure to be measured.

[0061] In some embodiments, in the target measured parameter generation module 5, a theoretical scattering spectrum of the nanostructure to be measured is obtained based on the measured morphological parameters and the first measured parameters; an evaluation function is determined based on the theoretical scattering spectrum and the measured scattering spectrum; the measured morphological parameters are continuously adjusted based on the evaluation function, and finally the target measured parameters of the nanostructure to be measured are obtained.

[0062] Accordingly, according to an embodiment of the present invention, the present invention also provides an electronic device, a readable storage medium and a computer program product.

[0063] Figure 7 It is a schematic diagram of the structure of an electronic device 6 provided in an embodiment of the present invention. Figure 7 A block diagram of an exemplary electronic device 6 suitable for implementing embodiments of the present invention is shown. Figure 7 The electronic device 6 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0064] like Figure 7As shown, the electronic device 6 is in the form of a general-purpose computing device. The electronic device 6 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0065] Components of the electronic device 6 may include, but are not limited to: one or more processors or processing units 8, a system memory 13, and a bus 9 connecting different system components (including the system memory 13 and the processing unit 8).

[0066] Bus 9 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnect (PCI) bus.

[0067] The electronic device 6 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 6, including volatile and non-volatile media, removable and non-removable media.

[0068] The system memory 13 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 14 and / or cache memory 15. The electronic device 6 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 16 may be used to read and write non-removable, non-volatile magnetic media ( Figure 7 not shown, usually called a "hard drive"). Although Figure 7 Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 9 via one or more data medium interfaces. The system memory 13 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0069] A program / utility 17 having a set (at least one) of program modules 18 may be stored, for example, in system memory 13, such program modules 18 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. Program modules 18 generally perform the functions and / or methods of the embodiments described herein.

[0070] The electronic device 6 may also communicate with one or more external devices 7 (e.g., keyboards, pointing devices, displays 11, etc.), one or more devices that enable a user to interact with the electronic device 6, and / or any device that enables the electronic device 6 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via an input / output (I / O) interface 12. Furthermore, the electronic device 6 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 10. Figure 7 As shown, the network adapter 10 communicates with other modules of the electronic device 6 via the bus 9. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 6, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0071] The processing unit 8 executes various functional applications and data processing by running the programs stored in the system memory 13, such as implementing the feature space-based morphology parameter extraction method provided in the embodiment of the present invention.

[0072] An embodiment of the present invention also provides a non-transitory computer-readable storage medium storing computer instructions, on which a computer program is stored, wherein when the program is executed by a processor, the feature space-based morphology parameter extraction method provided in all the inventive embodiments of the present application.

[0073] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, an apparatus, or a device.

[0074] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0075] The program code included in the computer-readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF, etc., or any suitable combination of the above. The computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or their combinations, and the programming language includes object-oriented programming languages ​​such as Java, Smalltalk, C++, and also includes conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer by any type of network including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect to the Internet).

[0076] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned feature space-based morphology parameter extraction method.

[0077] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the disclosure of the present invention can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document does not limit this.

[0078] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for extracting morphological parameters based on feature space, characterized in that: include: Using X-rays to illuminate the nanostructure to be tested at different incident angles; Using a detector to receive X-rays scattered by the nanostructure to be measured, and obtaining a measured scattering spectrum; Extracting the morphological parameters of the measured scattering spectrum based on the feature space to obtain the first parameter to be measured of the nanostructure to be measured; Determining the morphological parameters of the nanostructure to be measured based on the parameter model of the nanostructure to be measured; Based on the measured morphological parameter and the first measured parameter, a theoretical scattering spectrum of the measured nanostructure is obtained; Determining an evaluation function based on the measured scattering pattern and the theoretical scattering pattern; The morphological parameters to be measured are adjusted based on the evaluation function to obtain target parameters to be measured of the nanostructure to be measured.

2. The method for extracting morphological parameters based on feature space according to claim 1, characterized in that: Before extracting the morphological parameters of the measured scattering spectrum based on the feature space to obtain the first parameter of the nanostructure to be measured, the method further includes: Determining an incident angle range, and determining the number of incident angles according to the incident angle range and a preset incident angle step; For each incident angle corresponding to the measured scattering spectrum, the scattering peak involved in the extraction of morphological parameters is determined.

3. The method for extracting morphological parameters based on feature space according to claim 1, characterized in that: In the process of extracting the morphological parameters of the measured scattering spectrum based on the feature space to obtain the first parameter to be measured of the nanostructure to be measured: Constructing the feature space based on at least one of an autocorrelation function, machine learning, and the measured scattering spectrum; Based on the feature space, the first parameter to be measured is obtained.

4. The method for extracting morphological parameters based on feature space according to claim 3 is characterized in that: Based on the feature space, obtaining the first parameter to be measured includes: Obtaining a corresponding first parameter based on the autocorrelation function; Obtaining a corresponding second parameter based on machine learning; Obtaining a corresponding third parameter based on the measured scattering spectrum; When the feature space includes one of the autocorrelation function, machine learning and the measured scattering spectrum, one of the corresponding first parameter, the second parameter and the third parameter is used as the first parameter to be measured; When the feature space includes at least two of the autocorrelation function, machine learning and the measured scattering spectrum, the first parameter to be measured is determined based on at least two of the corresponding first parameter, the second parameter and the third parameter.

5. The method for extracting morphological parameters based on feature space according to claim 4, characterized in that: In the process of obtaining the corresponding first parameter based on the autocorrelation function: Obtaining the autocorrelation function distribution of the nanostructure to be measured based on the measured scattering spectrum; Obtaining the electron density distribution of the nanostructure to be measured according to the autocorrelation function distribution; The first parameter is determined based on the electron density distribution.

6. The method for extracting morphological parameters based on feature space according to claim 5, characterized in that: The process of obtaining the distribution of the electron density of the nanostructure to be measured according to the autocorrelation function distribution is as follows: ； in, represents the structural spacing of the nanostructure to be measured at the jth position, represents the autocorrelation function distribution, represents the electron density distribution.

7. The method for extracting morphological parameters based on feature space according to claim 4, characterized in that: In the process of obtaining the corresponding second parameter based on machine learning: Obtaining the autocorrelation function distribution of the electron density distribution of the nanostructure to be measured based on the measured scattering spectrum; Obtaining the mapping relationship between the autocorrelation function distribution and the structural information of the nanostructure to be measured through machine learning training to obtain the structural information; The second parameter is determined based on the structural information.

8. The method for extracting morphological parameters based on feature space according to claim 4, characterized in that: In the process of obtaining the corresponding third parameter based on the measured scattering spectrum: The third parameter is obtained based on the position information between the scattering peaks in the measured scattering spectrum.

9. The method for extracting morphological parameters based on feature space according to claim 1 or 3, characterized in that: In the process of obtaining the theoretical scattering spectrum of the nanostructure to be measured based on the measured morphological parameter and the first measured parameter: The theoretical scattering pattern is calculated by the following formula: ； in, represents the morphological parameters to be measured, and the morphological parameters to be measured Including the first parameter to be measured, q represents the q vector at different reciprocal space positions, represents the theoretical scattering spectrum, represents the scale factor, represents the instrument coefficient, represents the shape factor, represents the structure factor, represents the roughness factor, Represents the background scattering intensity.

10. The method for extracting morphological parameters based on feature space according to claim 1 or 3, characterized in that: In the process of adjusting the measured morphological parameters based on the evaluation function to obtain the target measured parameters of the measured nanostructure: The evaluation function is used as the optimization target, and the measured morphology parameters are optimized by using an optimization algorithm, so that the measured morphology parameters obtained when the evaluation function reaches the optimization termination condition are used as the target measured parameters.

11. A device for extracting morphological parameters based on feature space, characterized in that: The device comprises: An X-ray scattering pattern generation module is used to subject the nanostructure to be measured to X-ray scattering and obtain a measured scattering pattern; A parameter extraction module to be measured, used for obtaining a first parameter to be measured and a morphology parameter to be measured of the nanostructure to be measured according to the measured scattering spectrum and the parameter model of the nanostructure to be measured; The target parameter generation module is used to obtain the target parameter of the nanostructure to be measured according to the morphological parameter to be measured and the first parameter to be measured.

12. The device for step-by-step fitting of morphology parameters according to claim 1, characterized in that: The X-ray scattering diagram generating module comprises: An X-ray source, used for emitting X-rays toward the nanostructure to be measured; A rotating workbench, used for carrying and rotating the nanostructure to be measured, so that the X-rays are irradiated to the nanostructure to be measured at different incident angles; The detector is used to receive the X-rays scattered by the nanostructure to be measured and obtain the measured scattering spectrum.

13. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the feature space-based morphology parameter extraction method according to any one of claims 1 to 10 are implemented.

14. An electronic device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the feature space-based morphology parameter extraction method as claimed in any one of claims 1 to 10 when executing the computer program.

Images