X-ray-based joint analysis method, joint analysis device, and electronic device

Abstract

The application belongs to the technical field of sample analysis, and particularly relates to a combined analysis method based on X-rays, a combined analysis device and an electronic device. The method comprises: performing short-range optimization processing on a to-be-tested sample based on each candidate optimization strategy to obtain a short-range optimization result corresponding to each candidate optimization strategy, wherein the optimization sequence of multiple test technologies in different candidate optimization strategies is different, and the test conditions of different test technologies are the same; determining a target optimization strategy from the multiple candidate optimization strategies based on a comprehensive evaluation index of each candidate optimization strategy, wherein the comprehensive evaluation index is calculated based on the short-range optimization result; and performing normal optimization processing on the to-be-tested sample using the target optimization strategy to obtain parameter description information of the to-be-tested sample, wherein the optimization time length of the normal optimization processing is greater than the optimization time length of the short-range optimization processing.

Description

Technical Field

[0001] This application belongs to the field of sample analysis technology, and in particular relates to an X-ray-based combined analysis method, combined analysis device and electronic equipment. Background Technology

[0002] With the rapid development of microelectronics, nanoelectronics, photovoltaics, and advanced thin film materials, the demand for non-destructive, refined, and in-depth analysis of nanoscale thin films and multilayer thin film structures continues to increase. The industry urgently needs to simultaneously obtain multi-dimensional physical information such as sample thickness, density, interface roughness, component concentration, and elemental depth distribution. Currently, grazing incidence X-ray fluorescence (GIXRF) and X-ray reflectivity (XRR) are two mainstream non-destructive thin film characterization methods. GIXRF has excellent elemental depth analysis capabilities and can effectively characterize component information such as elemental distribution and doping dosage in thin films. XRR, on the other hand, has significant advantages in detecting thin film structural parameters and can accurately invert structural information such as film thickness, interface roughness, and density.

[0003] However, existing technologies have high experimental uncertainties during joint analysis, which ultimately limits the accuracy and stability of the analysis of thin film structural parameters and composition parameters, and reduces the efficiency of detection and analysis, failing to meet the requirements of high-precision and highly adaptable non-destructive testing and analysis. Summary of the Invention

[0004] In view of this, embodiments of this application provide an X-ray-based combined analysis method, combined analysis device, and electronic device to solve the problems of high experimental uncertainty, low analytical accuracy, limited stability, and low detection and analysis efficiency in existing combined analysis methods.

[0005] A first aspect of this application provides an X-ray-based joint analysis method, comprising: performing short-range optimization processing on a sample to be tested based on each candidate optimization strategy to obtain a short-range optimization result corresponding to each candidate optimization strategy, wherein the optimization order of multiple testing techniques in different candidate optimization strategies is different, and the testing conditions of different testing techniques are the same, the testing techniques include X-ray reflectance detection technology and grazing incidence X-ray fluorescence detection technology, and the short-range optimization result characterizes the comprehensive operating status information of the entire short-range optimization process; determining a target optimization strategy from multiple candidate optimization strategies based on a comprehensive evaluation index of each candidate optimization strategy, wherein the comprehensive evaluation index is calculated based on the short-range optimization result; and performing normal optimization processing on the sample to be tested using the target optimization strategy to obtain parameter description information of the sample to be tested, wherein the optimization time of the normal optimization processing is longer than the optimization time of the short-range optimization processing.

[0006] According to an embodiment of this application, the short-range optimization result includes at least one of short-range fitting error, parameter update trajectory, parameter boundary activity, parameter stability, parameter correlation, number of iterations, number of forward model calls, and computational cost; wherein, the comprehensive evaluation index is calculated based on the short-range optimization result, including: for any candidate optimization strategy, determining the comprehensive evaluation index of the candidate optimization strategy based on at least one of the short-range fitting error, parameter update trajectory, parameter boundary activity, parameter stability, parameter correlation, number of iterations, number of forward model calls, and computational cost.

[0007] According to an embodiment of this application, a comprehensive evaluation index for the candidate optimization strategy is determined based on at least one of the following: short-range fitting error, parameter update trajectory, parameter boundary activity, parameter stability, parameter correlation, iteration count, forward model call count, and computational cost. This includes: determining the short-range fitting error based on a first measurement curve generated by the X-ray reflectivity detection technology and a second measurement curve generated by the grazing incidence X-ray fluorescence detection technology, wherein the first measurement curve reflects the difference in reflectivity between the measurement and optimization processes; and the second measurement curve reflects the difference in fluorescence intensity between the measurement and optimization processes; determining the parameter boundary activity based on the obtained boundary dwell ratio, continuous boundary dwell time, and out-of-bounds correction ratio during the execution of different candidate optimization strategies; determining the computational cost based on the obtained iteration count index and iteration time index during the execution of different candidate optimization strategies; and obtaining the comprehensive evaluation index for the candidate optimization strategy by weighted summation of the short-range fitting error, the parameter boundary activity, and the computational cost.

[0008] According to an embodiment of this application, determining the short-range fitting error based on a first measurement curve generated by the X-ray reflectance detection technology and a second measurement curve generated by the grazing incidence X-ray fluorescence detection technology includes: for the X-ray reflectance detection technology, generating a first measurement curve based on the measured reflectance at each detection angle and the calculated reflectance obtained from short-range optimization simulation; for the grazing incidence X-ray fluorescence detection technology, generating a second measurement curve based on the measured fluorescence intensity at each detection angle and the calculated fluorescence intensity obtained from short-range optimization simulation; and determining the short-range fitting error based on the fitting error between the first measurement curve and the second measurement curve.

[0009] According to an embodiment of this application, determining a target optimization strategy from a plurality of candidate optimization strategies includes: determining the candidate optimization strategy corresponding to the optimal value among the plurality of comprehensive evaluation indicators as the target optimization strategy.

[0010] According to an embodiment of this application, the target optimization strategy is used to perform normal optimization processing on the sample to be tested to obtain parameter description information of the sample to be tested, including: determining the measurement methods of different testing technologies based on the target optimization strategy, and using the different testing technologies to perform measurement analysis on the sample to be tested to obtain the analysis results corresponding to each testing technology; and generating the parameter description information based on a unified parameter template and multiple analysis results.

[0011] According to an embodiment of this application, the short-range optimization process and the normal optimization process are optimized based on the following test conditions: the detectors of the different test technologies are controlled to collect data based on the same data acquisition conditions, wherein the same data acquisition conditions include scanning at the same incident angle, the same scanning position of the sample under test, the same spot position, and the same detector state parameters.

[0012] According to embodiments of this application, the candidate optimization strategies include: a first optimization strategy, characterized by first performing X-ray reflectance detection technology analysis, and then performing grazing incidence X-ray fluorescence detection technology analysis based on the analysis results of X-ray reflectance detection technology; a second optimization strategy, characterized by simultaneously performing X-ray reflectance detection technology and grazing incidence X-ray fluorescence detection technology analysis; and a third optimization strategy, characterized by first performing grazing incidence X-ray fluorescence detection technology analysis, and then performing X-ray reflectance detection technology analysis based on the analysis results of grazing incidence X-ray fluorescence detection technology analysis.

[0013] A second aspect of this application provides an X-ray-based co-analysis device, comprising: a short-range optimization module, configured to perform short-range optimization processing on a sample to be tested based on each candidate optimization strategy, obtaining a short-range optimization result corresponding to each candidate optimization strategy, wherein the optimization order of multiple testing techniques in different candidate optimization strategies is different, and the testing conditions of different testing techniques are the same, the testing techniques including X-ray reflectivity detection technology and grazing incidence X-ray fluorescence detection technology, and the short-range optimization result characterizing the comprehensive operating status information of the entire short-range optimization process; a strategy selection module, configured to determine a target optimization strategy from multiple candidate optimization strategies based on a comprehensive evaluation index of each candidate optimization strategy, wherein the comprehensive evaluation index is calculated based on the short-range optimization result; and a fitting optimization module, configured to perform normal optimization processing on the sample to be tested using the target optimization strategy, obtaining parameter description information of the sample to be tested, wherein the optimization time of the normal optimization processing is greater than the optimization time of the short-range optimization processing.

[0014] According to a third aspect of this application, an electronic device is provided, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0015] According to a fourth aspect of this application, a computer-readable storage medium is also provided, on which a computer program or instructions are stored, wherein the computer program or instructions, when executed by a processor, implement the steps of the above-described method.

[0016] According to a fifth aspect of this application, a computer program product is also provided, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0017] Compared with existing technologies, the beneficial effects of this application's embodiments are as follows: Unifying the test conditions for all candidate strategies avoids systematic errors introduced by factors such as sample repositioning, angle zero-point drift, light source intensity fluctuations, light spot position shifts, and changes in the environment and the sample's own state, ensuring the fairness of evaluation for strategies with different optimization sequences. By rapidly screening the optimal target strategy through short-range optimization, and leveraging the complementary constraints of XRR structural characterization and GIXRF elemental characterization, experimental uncertainties in thin film fitting analysis are effectively reduced, significantly improving the accuracy of sample structure and composition parameter analysis. Simultaneously, strategy screening is completed through short-time, short-range optimization, eliminating the need for long-term computation to verify all strategies throughout the process, effectively improving detection and analysis efficiency while ensuring detection accuracy. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart of the joint analysis method according to an embodiment of this application is shown; Figure 2 A flowchart illustrating the calculation of the comprehensive evaluation index in an embodiment of this application is shown; Figure 3 A flowchart illustrating the determination of short-range fitting error according to an embodiment of this application is shown; Figure 4 A structural block diagram of a chip test data processing apparatus according to an embodiment of this application is shown; Figure 5 A block diagram schematically illustrates an electronic device suitable for implementing the above-described method according to an embodiment of this application. Detailed Implementation

[0020] To make the inventive objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0022] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0023] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0024] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [the described condition or event] is detected," or "in response to detection of [the described condition or event]."

[0025] Furthermore, in the description of this application, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0026] This application provides an X-ray-based combined analysis method, combined analysis device, and electronic device to address the problems of high experimental uncertainty, low analytical accuracy, poor stability, and low detection and analysis efficiency in existing combined analysis methods.

[0027] In this embodiment, standardizing the test conditions for all candidate strategies avoids systematic errors introduced by factors such as sample repositioning, angle zero-point drift, light source intensity fluctuations, spot position shifts, and changes in the environment and the sample's own state, ensuring the fairness of the evaluation of strategies with different optimization orders. By rapidly selecting the optimal target strategy through short-range optimization, and leveraging the complementary constraints of XRR structural characterization and GIXRF elemental characterization, experimental uncertainties in thin film fitting analysis are effectively reduced, significantly improving the accuracy of sample structure and composition parameter analysis. Furthermore, strategy selection is completed through short-time, short-range optimization, eliminating the need for long-term computation to verify all strategies throughout the process, effectively improving detection and analysis efficiency while ensuring detection accuracy.

[0028] Figure 1 A flowchart of the joint analysis method according to an embodiment of this application is shown.

[0029] Please see Figure 1 An embodiment of a joint analysis method based on X-rays in this application may include operations S101 to S103: In operation S101, short-range optimization is performed on the sample to be tested based on each candidate optimization strategy to obtain the short-range optimization result corresponding to each candidate optimization strategy. The optimization order of multiple test techniques in different candidate optimization strategies is different, and the test conditions of different test techniques are the same. The test techniques include X-ray reflectance detection technology and grazing incidence X-ray fluorescence detection technology. The short-range optimization result characterizes the comprehensive operating status information of the entire short-range optimization process.

[0030] In operation S102, the target optimization strategy is determined from multiple candidate optimization strategies based on the comprehensive evaluation index of each candidate optimization strategy. The comprehensive evaluation index is calculated based on the short-range optimization results.

[0031] In operation S103, the target optimization strategy is used to perform normal optimization processing on the sample to be tested, and the parameter description information of the sample to be tested is obtained. The optimization time of normal optimization processing is longer than that of short-range optimization processing.

[0032] The combined analysis method of this embodiment can be applied in a variety of fields, such as nanomaterial analysis, semiconductor thin film metrology, memory material characterization, and photonics and optoelectronic devices. In nanomaterial analysis, it can realize ion implantation, multilayer film deposition, interface diffusion, film thickness uniformity and composition deviation monitoring. In semiconductor thin film metrology, it can realize non-destructive analysis of structures such as metal layers, barrier layers, diffusion layers and ultra-shallow junctions. In memory material characterization, it can analyze the film thickness and composition in multilayer stacked memory structures. In photonics and optoelectronic devices, it can perform non-destructive analysis of optical thin films, multilayer reflective films, transparent conductive films and so on.

[0033] X-ray reflectivity testing technology is a non-destructive characterization technique that uses low-angle incident X-rays to irradiate the sample surface and collects the reflection intensity curves of X-rays at different incident angles. Based on Fresnel's law of reflection and thin film interference model, it quantitatively analyzes the thickness, density, interface roughness, and electron density distribution of multilayer thin films on the substrate. It is often used for precision structural detection of nanoscale ultrathin films, metal coatings, and oxide layers.

[0034] Grazing incidence X-ray fluorescence detection technology uses X-rays incident at an extremely small grazing incidence angle to excite characteristic fluorescence signals in the sample surface region. Combined with the thin film interference effect, the fluorescence intensity of the target element as a function of the incident angle is collected, enabling non-destructive quantitative analysis of the elemental composition, layer thickness, interfacial diffusion, and surface contaminants of multilayer thin films.

[0035] The X-ray-based joint analysis method of this embodiment is applicable to the precise detection and analysis of the structure and composition parameters of multilayer thin film samples. The core of the method utilizes a combination of XRR and GIXRF X-ray testing techniques to complete sample detection and data fitting analysis. This method pre-constructs multiple different candidate optimization strategies. The core difference between all candidate optimization strategies lies in the order in which XRR and GIXRF test data are optimized. During the testing of all candidate strategies, all test conditions are strictly kept completely consistent, without changing any test-related conditions. This avoids data deviations caused by changes in conditions during the testing of different strategies, ensuring the cross-comparability of test data from each strategy group. In some embodiments, XRR and GIXRF tests can be performed separately. For example, XRR (or GIXRF) test data can be collected first, followed by GIXRF (or XRR) test data. In this case, it is only necessary to maintain the same test conditions.

[0036] For each candidate optimization strategy, a short-range optimization process is performed on the test sample, quickly completing algorithm iteration and preliminary fitting calculations, resulting in a unique short-range optimization result for each candidate strategy. This short-range optimization result comprehensively characterizes the overall operational status information of the short-range optimization process, covering multi-dimensional information such as fitting accuracy, parameter change status, boundary constraints, iteration operation status, and computational cost, providing complete and accurate data for subsequent strategy evaluation.

[0037] Based on the short-range optimization results of each group of candidate optimization strategies, corresponding comprehensive evaluation indicators were calculated. By comparing all indicator data horizontally, the target optimization strategy that best suits the current testing requirements of the sample was selected from multiple candidate strategies. Finally, the selected target optimization strategy was used to perform a complete normal optimization process on the sample. The overall computation time of normal optimization is much longer than that of short-range optimization, which enables sufficient iteration and accurate convergence of model parameters, effectively improving fitting accuracy. Finally, comprehensive and accurate parameter description information such as film thickness, density, interface roughness, and elemental composition content of the sample was output.

[0038] According to the embodiments of this application, unifying the test conditions for all candidate strategies can avoid systematic errors introduced by factors such as sample repositioning, angle zero-point drift, light source intensity fluctuations, light spot position shifts, and changes in the environment and the sample's own state, ensuring the fairness of the evaluation of strategies with different optimization orders. By rapidly screening the optimal target strategy through short-range optimization, and relying on the complementary constraints of XRR structural characterization and GIXRF elemental characterization, the experimental uncertainty in the thin film fitting analysis process is effectively reduced, significantly improving the accuracy of sample structure and composition parameter analysis. Simultaneously, strategy screening is completed through short-time, short-range optimization, eliminating the need for long-term computation to verify all strategies throughout the process, effectively improving detection and analysis efficiency while ensuring detection accuracy.

[0039] According to embodiments of this application, the short-range optimization result includes at least one of the following: short-range fitting error, parameter update trajectory, parameter boundary activity, parameter stability, parameter correlation, number of iterations, number of forward model calls, and computational cost.

[0040] According to an embodiment of this application, the comprehensive evaluation index is calculated based on the short-range optimization results, including: for any candidate optimization strategy, determining the comprehensive evaluation index of the candidate optimization strategy based on at least one of the following: short-range fitting error, parameter update trajectory, parameter boundary activity, parameter stability, parameter correlation, number of iterations, number of forward model calls, and computational cost.

[0041] This embodiment, based on the joint analysis method of the aforementioned embodiments, further refines the evaluation, screening, and short-range optimization execution process of candidate optimization strategies. During the short-range optimization process for each candidate optimization strategy, the system control module first determines the initial parameter set. This initial parameter set can be configured based on user-defined input parameter values, or it can be randomly generated within a system-preset parameter boundary range, ensuring both flexibility and standardization in the initial parameter configuration.

[0042] For each candidate optimization strategy, a short-range trial optimization operation with limited computation is performed according to the test logic of the corresponding strategy. The short-range trial optimization operation is terminated based on a preset short-range stopping condition. Specifically, the short-range optimization iteration is terminated immediately when any one or more of the following conditions are met: preset maximum number of iterations, preset objective function threshold, preset maximum running time, or the fitting error decrease is less than a preset threshold. The optimizer automatically determines the stopping criterion after each iteration and outputs the current optimal parameter value after termination.

[0043] After short-range optimization, the control module fully records the short-range optimization results for each candidate optimization strategy. These results include at least one of the following: short-range fitting error, parameter update trajectory, parameter boundary activity, parameter stability, parameter correlation, number of iterations, number of forward model calls, and computational cost. Subsequently, based on the recorded short-range optimization results, the system performs targeted calculations to obtain comprehensive evaluation indicators for each candidate optimization strategy. By comparing the comprehensive evaluation indicators of all strategies horizontally, the system selects the candidate optimization strategy with the optimal indicator as the target optimization strategy for subsequent formal optimization.

[0044] According to embodiments of this application, by standardizing initial parameter configurations and multi-dimensional short-range stopping conditions, the short-range optimization computation process of each candidate strategy is standardized, ensuring that the optimization process of all strategies is controllable and the data is comparable, avoiding evaluation distortion caused by iteration chaos, excessive computation, or premature termination. By jointly calculating a comprehensive evaluation index using multi-dimensional short-range optimization results, the system comprehensively considers fitting accuracy, parameter stability, iteration efficiency, and computational cost, solving the problem of the one-sidedness of single-index evaluation, significantly improving the objectivity and accuracy of optimal strategy selection, and providing reliable strategy support for subsequent accurate sample parameter fitting analysis.

[0045] Figure 2 A flowchart illustrating the calculation process of the comprehensive evaluation index in an embodiment of this application is shown.

[0046] like Figure 2 As shown, based on at least one of the following: short-range fitting error, parameter update trajectory, parameter boundary activity, parameter stability, parameter correlation, number of iterations, number of forward model calls, and computational cost, a comprehensive evaluation index for candidate optimization strategies is determined, including operations S201 to S204: In operation S201, the short-range fitting error is determined based on the first measurement curve generated by X-ray reflectance detection technology and the second measurement curve generated by grazing incidence X-ray fluorescence detection technology. The first measurement curve reflects the difference between reflectance during measurement and optimization; the second measurement curve reflects the difference between fluorescence intensity during measurement and optimization.

[0047] In operation S202, the parameter boundary activity is determined based on the proportion of residents near the boundary, the duration of continuous boundary residence, and the proportion of out-of-boundary correction obtained in the execution of different candidate optimization strategies.

[0048] In operation S203, the computational cost is determined based on the iteration number index and iteration time index obtained from the execution of different candidate optimization strategies.

[0049] In operation S204, the short-range fitting error, parameter boundary activity, and computational cost are weighted and summed to obtain a comprehensive evaluation index of the candidate optimization strategy.

[0050] This embodiment, based on the joint analysis method of the aforementioned embodiments, refines the specific calculation logic of the comprehensive evaluation index and accurately quantifies the advantages and disadvantages of each candidate optimization strategy by relying on multi-dimensional short-range optimization data.

[0051] First, a first measurement curve was acquired using X-ray reflectance detection technology. This curve directly reflects the difference between the actual measured reflectance of the sample and the reflectance fitted by the algorithm. Simultaneously, a second measurement curve was acquired using grazing incidence X-ray fluorescence detection technology. This curve reflects the difference between the actual measured fluorescence intensity of the sample and the fluorescence intensity fitted by the algorithm. By combining the fitting deviation data from both measurement curves, the short-range fitting error of this short-range optimization was accurately calculated. This directly reflects the fitting accuracy of each candidate strategy.

[0052] Secondly, the parameter operating status of each testing technique during the short-range optimization iteration process is statistically analyzed, and three core data types are extracted: the dwell ratio near the parameter boundary, the dwell time of continuous boundaries, and the out-of-bounds correction ratio. The parameter boundary activity is then calculated comprehensively. The ability of quantitative optimization parameters to adapt to constraints and dynamically adjust within a preset boundary range, and the activity of parameter boundaries. The calculation is shown in formula (1): (1) in, This represents the proportion of users staying near the boundary. The larger this indicator is, the more frequently the parameters will approach the boundary under this strategy. This represents the duration of continuous boundary dwell time. The larger this indicator is, the longer the parameters will remain close to the boundary under this strategy. This is the out-of-bounds correction ratio for candidate values, i.e., the out-of-bounds correction ratio. The larger this indicator is, the greater the tendency of the parameters to try to go out of bounds under this strategy. These are preset weighting coefficients.

[0053] Simultaneously, the iteration number index and iteration time index during the optimization process are collected, and the computational cost of the corresponding candidate optimization strategy is calculated by integrating these two types of data. The computational resource consumption and iteration efficiency of objective representation strategies, and computational cost. The calculation is shown in formula (2): (2) in, Let be the exponent of the number of iterations for the k-th candidate strategy in short-range trial optimization. Let be the iteration time exponent for the k-th strategy. These are preset weighting coefficients. This indicator... The larger the value, the greater the computational resource consumption under this strategy.

[0054] Finally, the three core parameters—short-range fitting error, parameter boundary activity, and computational cost—are weighted and summed to obtain a unique, quantifiable comprehensive evaluation index. The calculation is shown in formula (3): (3) Where k represents the candidate optimization strategy; This is the normalized joint goodness of fit, i.e., the short-range fitting error. As preset weighting coefficients, each weighting coefficient can be flexibly configured according to the detection accuracy requirements and computing resource requirements. The above weighted summation formula is used to standardize and quantify the evaluation of each candidate optimization strategy, thereby accurately selecting the optimal target optimization strategy suitable for the sample to be tested.

[0055] According to embodiments of this application, by stratifying and quantifying three core dimensions—fitting accuracy, parameter boundary adaptability, and computational resource consumption—the limitations of single-dimensional evaluation are eliminated. A standardized comprehensive evaluation index is obtained through multi-parameter weighted fusion, making the comparison of the merits of each candidate optimization strategy more comprehensive, objective, and accurate. By relying on dual test curves to accurately calibrate the fitting error and combining parameter boundary operation data to quantify parameter adaptability, subjective selection bias is effectively avoided, significantly improving the scientific nature of strategy selection. While ensuring the fitting accuracy of thin film detection, the stability of optimized parameters and computational cost are also considered, achieving bidirectional optimization of detection accuracy and computational efficiency, further enhancing the reliability and practicality of the overall X-ray joint analysis method.

[0056] Figure 3 A flowchart illustrating the determination of short-range fitting error according to an embodiment of this application is shown.

[0057] like Figure 3 As shown, based on the first measurement curve generated by X-ray reflectivity detection technology and the second measurement curve generated by grazing incidence X-ray fluorescence detection technology, the short-range fitting error is determined, including operations S301 to S303: In operation S301, for X-ray reflectivity detection technology, a first measurement curve is generated based on the measured reflectivity at each detection angle and the calculated reflectivity obtained from short-range optimization simulation.

[0058] In operation S302, for grazing incidence X-ray fluorescence detection technology, a second measurement curve is generated based on the measured fluorescence intensity at each detection angle and the calculated fluorescence intensity obtained from short-range optimized simulation.

[0059] In operation S303, the short-range fitting error is determined based on the fitting error between the first and second measurement curves.

[0060] This embodiment, based on the joint analysis method of the aforementioned embodiments, further refines the precise generation and calculation method of short-range fitting error, and realizes the differential and standardized error solution of XRR and GIXRF dual-technology measurement curves.

[0061] During the short-range optimization process, data were simultaneously collected for both detection technologies, and corresponding measurement curves were constructed. For X-ray reflectivity detection, the actual measured reflectance intensity of the sample at each detection angle was collected in real time, and the calculated reflectance intensity at the corresponding angle was obtained through simulation using a short-range optimization model. The first measurement curve was generated by fitting the measured data and the simulated data. For grazing-incidence X-ray fluorescence detection, the actual measured fluorescence intensity at each detection angle and the calculated fluorescence intensity obtained from the short-range optimization simulation were collected simultaneously, and the second measurement curve was generated by fitting these values.

[0062] The local fitting error is calculated based on the difference in deviation characteristics between the two measurement curves: For the second measurement curve corresponding to GIXRF, the local fitting error is determined by the square of the difference between the measured fluorescence intensity and the calculated fluorescence intensity; For the first measurement curve corresponding to XRR, two preferred calculation methods are adopted: the error can be calculated by the square of the logarithmic difference between the measured and calculated reflection intensities, or a weighted compensation can be introduced by a preset power of the incident angle, and the error is determined by the square of the difference between the weighted measured and calculated reflection intensities. This power of the incident angle can compensate for the inherent defect that the reflection intensity decreases as the incident angle increases, and improve the recognition accuracy of weak intensity differences in the large incident angle range.

[0063] Finally, the local fitting errors of XRR and GIXRF are integrated and normalized by combining the degrees of freedom and maximum calculated values ​​of each measurement curve to obtain a unified normalized joint fitting error, which serves as the final short-range fitting error. This error can be adapted to scenarios with different numbers of measurement points and data volumes of the two detection technologies, enabling joint error evaluation of cross-dimensional data. The larger the value of this normalized joint fitting error, the worse the optimization fitting effect of the corresponding candidate optimization strategy.

[0064] According to embodiments of this application, fitting error calculation rules are designed to address the signal characteristic differences between XRR and GIXRF detection technologies. Power-weighted compensation based on the incident angle eliminates detection bias caused by signal attenuation at large incident angles, effectively amplifying the ability to identify subtle signal differences. By normalizing the degrees of freedom and maximum values ​​of the hyperbolic local error, the imbalance in error evaluation caused by the different number and magnitude of the two types of detection data points is resolved, achieving fair and accurate joint evaluation of multi-source detection data. The precisely quantified fitting error accurately reflects the actual fitting quality of each candidate strategy, providing high-precision data support for subsequent comprehensive evaluation index calculation and optimal strategy selection, further improving the overall accuracy and reliability of multi-parameter joint analysis of thin films.

[0065] According to an embodiment of this application, determining a target optimization strategy from multiple candidate optimization strategies includes: determining the candidate optimization strategy corresponding to the optimal value (e.g., the maximum or minimum value) in the comprehensive evaluation index as the target optimization strategy.

[0066] This embodiment, based on the joint analysis method of any of the foregoing embodiments, further refines the specific screening and judgment rules for the target optimization strategy. After completing the short-range optimization calculation of all candidate optimization strategies, the system calculates the comprehensive evaluation index corresponding to each group of candidate optimization strategies based on the short-range optimization results of each candidate optimization strategy. Each candidate optimization strategy corresponds to a unique quantitative comprehensive evaluation index value.

[0067] Based on preset indicator direction rules, extreme indicator values ​​(maximum or minimum values) in the corresponding direction are selected, and candidate optimization strategies corresponding to these extreme indicator values ​​are identified. The preset indicator direction rules define the correspondence between the magnitude of the comprehensive evaluation indicator value and the merits of the candidate optimization strategies: when the rule is set that a larger value represents better performance, the candidate optimization strategy corresponding to the maximum value of the indicator value is selected as the target optimization strategy; when the rule is set that a smaller value represents better performance, the candidate optimization strategy corresponding to the minimum value of the indicator value is selected as the target optimization strategy.

[0068] According to an embodiment of this application, a target optimization strategy is used to perform normal optimization processing on the sample to be tested to obtain parameter description information of the sample to be tested, including: determining the measurement method of different testing technologies based on the target optimization strategy, and using different testing technologies to measure and analyze the sample to be tested to obtain the analysis results corresponding to each testing technology; and generating parameter description information based on a unified parameter template and multiple analysis results.

[0069] This embodiment refines the normal optimization process and sample parameter description information generation flow under the target optimization strategy based on the joint analysis method of the aforementioned embodiments. After selecting the optimal target optimization strategy, the measurement schemes corresponding to the XRR and GIXRF testing technologies are determined according to the target optimization strategy. The XRR and GIXRF testing technologies are then used to measure and analyze the sample under test, and XRR measurement data and GIXRF measurement data are collected respectively to obtain the corresponding sample analysis results.

[0070] This embodiment relies on a dedicated unified parameter module to achieve standardized modeling. The core design concept of this module is the decoupling of unified sample parameter characterization from differentiated computational parameters. Regardless of whether the user constructs a sample model of a single-layer thin film, multi-layer thin film, or other complex structures, the system uniformly uses fixed parameter types to standardize the sample description using a unified parameter template. The core parameters include the thickness of the nth sample layer. Mass fraction of element i in the nth layer sample and the roughness of the nth layer interface The mass fraction of the element can be either a fixed constant or a distribution function that varies with thickness. The system uses this unified parameter set to standardize and instantiate parameter models for samples with arbitrary structures, forming a unique and universal expression of user-side sample parameters.

[0071] Based on a unified sample model, the parameter system can automatically derive specialized physical parameters for the two detection technologies, including complex refractive index, electron density, and mass decay coefficient, according to the differentiated physical calculation requirements of XRR and GIXRF. These derived parameters are then independently transmitted to their respective calculation modules, driving the XRR calculator to generate a simulated sample reflectance curve and the GIXRF calculator to generate a simulated sample fluorescence intensity curve. This allows the same set of basic sample parameters to simultaneously drive two simulation calculation chains, ultimately integrating them to obtain a complete joint simulation result containing both reflectance and fluorescence intensity curves.

[0072] Finally, based on the standardized sample model generated by the unified parameter module and the joint simulation results of the dual-link system, the measured analysis data of XRR and GIXRF are integrated and fitted to output complete and unified parameter description information of the sample under test, thus completing the accurate and integrated analysis of the structure and composition parameters of complex thin film samples.

[0073] According to embodiments of this application, a decoupled architecture is adopted between unified parameter representation on the user side and dedicated parameters on the computation side. Thickness, interface roughness, and elemental mass fraction are used to model various thin film samples, abandoning independent modeling methods such as XRR and GIXRF, effectively avoiding the problems of parameter mismatch and data fragmentation in multi-technology modeling. The system can automatically derive the dedicated physical parameters required for two types of detection based on unified basic parameters, eliminating the need for manual repetitive modeling and parameter migration adaptation, simplifying the analysis process and reducing human error. Simultaneously, relying on a single sample model to synchronously drive the dual-technology computation link ensures that the parameters of the two detection systems are of the same origin and the model is unified, effectively improving the consistency and analytical efficiency of complex thin film sample structure and component parameter analysis.

[0074] According to embodiments of this application, short-range optimization and normal optimization are performed based on the following test conditions: detectors using different test technologies are controlled to acquire data under the same data acquisition conditions, wherein the same data acquisition conditions include scanning at the same incident angle, scanning position of the sample under test, position of the same spot, and the same detector state parameters.

[0075] This embodiment, based on the joint analysis method of any of the foregoing embodiments, further defines unified data acquisition and testing conditions for short-range optimization and normal optimization. This method utilizes the similar grazing incidence detection geometry and common physical basis of XRR and GIXRF to achieve synchronous data acquisition for both detection technologies. During the short-range optimization process of all candidate optimization strategies, and during the normal optimization process of the subsequent target optimization strategy, the detectors corresponding to XRR and GIXRF are controlled to follow completely consistent data acquisition conditions.

[0076] Specifically, maintaining a consistent incident angle scanning range and scanning rhythm, a fixed scanning position for the sample, an X-ray spot irradiation position, and consistent detector operating parameters throughout the entire process ensures that both the XRR reflectance curve and the GIXRF fluorescence intensity curve are acquired under identical instrument and sample conditions. This synchronous acquisition method completely abandons the step-by-step, independent acquisition mode, guaranteeing the homogeneity of the two types of detection data at the hardware acquisition level and eliminating data interference caused by external factors and equipment fluctuations.

[0077] According to the embodiments of this application, by unifying the data acquisition conditions of the two technologies, systematic errors caused by sample repositioning, angle drift, light source fluctuations, spot shifts, and environmental changes during step-by-step acquisition are completely avoided, significantly improving the consistency of XRR and GIXRF detection data. Relying on the complementary detection characteristics of the two technologies and combining the high-quality data from unified acquisition, the constraint capability of joint inversion is strengthened, effectively reducing the ambiguity and parameter correlation of single-technology fitting inversion, reducing experimental uncertainty, and ultimately constructing a self-consistent and complete sample physical model, significantly improving the stability and reliability of the quantitative analysis results of thin film parameters.

[0078] According to embodiments of this application, candidate optimization strategies include: a first optimization strategy, characterized by first performing X-ray reflectance detection technology analysis, and then performing grazing incidence X-ray fluorescence detection technology analysis based on the analysis results of X-ray reflectance detection technology; a second optimization strategy, characterized by simultaneously performing X-ray reflectance detection technology analysis and grazing incidence X-ray fluorescence detection technology analysis; and a third optimization strategy, characterized by first performing grazing incidence X-ray fluorescence detection technology analysis, and then performing X-ray reflectance detection technology analysis based on the analysis results of grazing incidence X-ray fluorescence detection technology analysis.

[0079] This embodiment is based on the joint analysis method of any of the foregoing embodiments, and specifically defines three types of candidate optimization strategies that can be screened. All strategies are executed under the same test and acquisition conditions, and only the timing logic of XRR and GIXRF detection and optimization is distinguished to adapt to the fitting analysis requirements of different thin film samples.

[0080] The first optimization strategy is a step-by-step progressive optimization mode. The execution logic is as follows: first, X-ray reflectance detection (XRR) analysis is performed; based on XRR measurement data, structural parameters such as sample thickness and interface roughness are preferentially solved and locked; then, based on the stabilized structural parameters and XRR analysis results, grazing incidence X-ray fluorescence (GIXRF) analysis is performed. This strategy can avoid interference caused by layer structure uncertainty, reduce parameter coupling problems in the GIXRF fitting process, and ensure the convergence stability of sample elemental composition parameters and depth distribution parameters.

[0081] The second optimization strategy is a simultaneous joint optimization mode, which simultaneously executes the analysis of X-ray reflectance detection technology and grazing incidence X-ray fluorescence detection technology. In the same round of optimization iteration, XRR structural measurement data and GIXRF component measurement data are introduced simultaneously to conduct bidirectional joint constraints on the sample shared model parameters. At the same time, the error terms of the two types of technologies are normalized and processed in a coordinated manner to achieve synchronous and collaborative fitting optimization of structural parameters and composition parameters.

[0082] The third optimization strategy is a reverse step-by-step progressive optimization mode. The execution logic is to first conduct analysis using grazing incidence X-ray fluorescence detection technology, and then determine the composition parameters such as the mass fraction of sample elements based on its optimization results (i.e., analysis results). After clarifying the basic information of sample components, X-ray reflectance detection technology analysis is then performed. This method can effectively avoid optical constant deviations caused by uncertainties in component parameters.

[0083] According to embodiments of this application, three candidate optimization strategies with differentiated analysis sequences are configured, covering a multivariate analysis mode of step-by-step progression and synchronous collaboration. This can adapt to the fitting optimization needs of different thin film samples, breaking through the limitations of single analysis strategies in adapting to different scenarios. By combining two technologies in different sequences, the complementary advantages of XRR structural characterization and GIXRF component characterization are fully utilized, suppressing model ambiguity, parameter coupling interference, and fitting bias from multiple dimensions, effectively reducing experimental uncertainty. The multi-strategy candidate screening mechanism significantly improves the adaptability and flexibility of the joint analysis method, comprehensively enhancing the accuracy, stability, and reliability of the fitting results of thin film structure and component parameters.

[0084] The above embodiments are not limited to XRR and GIXRF detection technologies, but can be extended to adapt to various similar X-ray characterization detection technologies, including: X-ray fluorescence (XRF), grazing incidence X-ray reflectivity (GIXRR), and grazing emission X-ray fluorescence (GEXRF). All of these extended detection technologies have the capability to characterize thin film structures or analyze elemental composition, and can be flexibly replaced and adapted according to the characteristics of the sample. They can also achieve accurate analysis of thin film sample structure and composition parameters through multi-strategy time-series optimization and joint constraint fitting, further improving the applicability and universal adaptability of this method.

[0085] Figure 4 A structural block diagram of a chip test data processing apparatus according to an embodiment of this application is shown.

[0086] like Figure 4 As shown, the X-ray-based joint analysis device 400 includes a short-range optimization module 410, a strategy selection module 420, and a fitting optimization module 430.

[0087] The short-range optimization module 410 is used to perform short-range optimization processing on the sample to be tested based on each candidate optimization strategy, and obtain the short-range optimization result corresponding to each candidate optimization strategy. The optimization order of multiple test technologies in different candidate optimization strategies is different, and the test conditions of different test technologies are the same. The test technologies include X-ray reflectivity detection technology and grazing incidence X-ray fluorescence detection technology. The short-range optimization result characterizes the comprehensive operating status information of the entire short-range optimization process.

[0088] The strategy selection module 420 is used to determine the target optimization strategy from multiple candidate optimization strategies based on the comprehensive evaluation index of each candidate optimization strategy, wherein the comprehensive evaluation index is calculated based on the short-range optimization results.

[0089] The fitting optimization module 430 is used to perform normal optimization processing on the test sample using the target optimization strategy to obtain the parameter description information of the test sample. The optimization time of the normal optimization processing is longer than the optimization time of the short-range optimization processing.

[0090] According to the embodiments of this application, unifying the test conditions for all candidate strategies can avoid systematic errors introduced by factors such as sample repositioning, angle zero-point drift, light source intensity fluctuations, light spot position shifts, and changes in the environment and the sample's own state, ensuring the fairness of the evaluation of strategies with different optimization orders. By rapidly screening the optimal target strategy through short-range optimization, and relying on the complementary constraints of XRR structural characterization and GIXRF elemental characterization, the experimental uncertainty in the thin film fitting analysis process is effectively reduced, significantly improving the accuracy of sample structure and composition parameter analysis. Simultaneously, strategy screening is completed through short-time, short-range optimization, eliminating the need for long-term computation to verify all strategies throughout the process, effectively improving detection and analysis efficiency while ensuring detection accuracy.

[0091] According to embodiments of this application, the short-range optimization result includes at least one of the following: short-range fitting error, parameter update trajectory, parameter boundary activity, parameter stability, parameter correlation, number of iterations, number of forward model calls, and computational cost.

[0092] According to an embodiment of this application, the comprehensive evaluation index is obtained through the index determination module.

[0093] The index determination module is used to determine a comprehensive evaluation index for any candidate optimization strategy based on at least one of the following: short-range fitting error, parameter update trajectory, parameter boundary activity, parameter stability, parameter correlation, number of iterations, number of forward model calls, and computational cost.

[0094] According to an embodiment of this application, the indicator determination module includes an error determination unit, an activity determination unit, a cost determination unit, and an indicator determination unit.

[0095] The error determination unit is used to determine the short-range fitting error based on the first measurement curve generated by X-ray reflectance detection technology and the second measurement curve generated by grazing incidence X-ray fluorescence detection technology. The first measurement curve reflects the difference between reflectance during measurement and optimization; the second measurement curve reflects the difference between fluorescence intensity during measurement and optimization.

[0096] The activity determination unit is used to determine the activity of parameter boundaries based on the proportion of residents near the boundary, the duration of continuous boundary residence, and the proportion of out-of-boundary correction obtained in the execution of different candidate optimization strategies.

[0097] The cost determination unit is used to determine the computational cost based on the iteration number exponent and iteration time exponent obtained in the execution of different candidate optimization strategies.

[0098] The index determination unit is used to perform a weighted summation of short-range fitting error, parameter boundary activity, and computational cost to obtain a comprehensive evaluation index for candidate optimization strategies.

[0099] According to an embodiment of this application, the error determination unit includes a first curve generation subunit, a second curve generation subunit, and an error determination subunit.

[0100] The first curve generation subunit is used to generate a first measurement curve for X-ray reflectivity detection technology, based on the measured reflectivity at each detection angle and the calculated reflectivity obtained from short-range optimization simulation.

[0101] The second curve generation subunit is used to generate a second measurement curve for grazing incidence X-ray fluorescence detection technology, based on the measured fluorescence intensity at each detection angle and the calculated fluorescence intensity obtained from short-range optimized simulation.

[0102] The error determination subunit is used to determine the short-range fitting error based on the fitting error between the first and second measurement curves.

[0103] According to an embodiment of this application, the strategy selection module includes a strategy selection unit.

[0104] The strategy selection unit is used to determine the candidate optimization strategy corresponding to the optimal value among multiple comprehensive evaluation indicators as the target optimization strategy.

[0105] According to an embodiment of this application, the fitting optimization module includes an analysis unit and a generation unit.

[0106] The analysis unit is used to determine the measurement methods of different testing technologies based on the target optimization strategy, and to use the different testing technologies to measure and analyze the sample to be tested, so as to obtain the analysis results corresponding to each testing technology.

[0107] The generation unit is used to generate parameter description information based on a unified parameter template and multiple analysis results.

[0108] According to embodiments of this application, short-range optimization and normal optimization are performed based on the following test conditions: detectors using different test technologies are controlled to acquire data under the same data acquisition conditions, wherein the same data acquisition conditions include scanning at the same incident angle, scanning position of the sample under test, position of the same spot, and the same detector state parameters.

[0109] According to embodiments of this application, candidate optimization strategies include: a first optimization strategy, characterized by first performing X-ray reflectance detection technology analysis, and then performing grazing incidence X-ray fluorescence detection technology analysis based on the analysis results of X-ray reflectance detection technology; a second optimization strategy, characterized by simultaneously performing X-ray reflectance detection technology analysis and grazing incidence X-ray fluorescence detection technology analysis; and a third optimization strategy, characterized by first performing grazing incidence X-ray fluorescence detection technology analysis, and then performing X-ray reflectance detection technology analysis based on the analysis results of grazing incidence X-ray fluorescence detection technology analysis.

[0110] According to embodiments of this application, any multiple modules among the short-range optimization module 410, strategy selection module 420, and fitting optimization module 430 can be merged into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module. According to embodiments of this application, at least one of the short-range optimization module 410, strategy selection module 420, and fitting optimization module 430 can be at least partially implemented as hardware circuits, such as field-programmable gate arrays, programmable logic arrays, systems-on-a-chip, systems-on-a-substrate, systems-on-package, application-specific integrated circuits, or any other reasonable means of integrating or packaging circuits, or implemented in software, hardware, or firmware, or in any appropriate combination of any of these three implementation methods. Alternatively, at least one of the short-range optimization module 410, strategy selection module 420, and fitting optimization module 430 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0111] Figure 5 A block diagram schematically illustrates an electronic device suitable for implementing the above-described method according to an embodiment of this application.

[0112] like Figure 5 As shown, an electronic device 500 according to an embodiment of this application includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory 502 or a program loaded from a storage portion 508 into a random access memory 503. The processor 501 may include, for example, a general-purpose microprocessor, an instruction set processor and / or an associated chipset and / or a dedicated microprocessor. The processor 501 may also include onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for executing different steps of the method flow according to an embodiment of this application.

[0113] Random access memory 503 stores various programs and data required for the operation of electronic device 500. Processor 501, read-only memory 502, and random access memory 503 are interconnected via bus 504. Processor 501 executes various steps of the method flow according to embodiments of this application by executing programs in read-only memory 502 and / or random access memory 503. It should be noted that the programs may also be stored in one or more memories other than read-only memory 502 and random access memory 503. Processor 501 may also execute various steps of the method flow according to embodiments of this application by executing programs stored in said one or more memories.

[0114] According to embodiments of this application, the electronic device 500 may further include an input / output interface 505, which is also connected to a bus 504. The electronic device 500 may also include one or more of the following components connected to the input / output interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube, liquid crystal display, etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card, such as a local area network card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the input / output interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 510 as needed so that computer programs read from it can be installed into the storage section 508 as needed.

[0115] Embodiments of this application also provide a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0116] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof. In embodiments of this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this application, the computer-readable storage medium may include the read-only memory 502, and / or random access memory 503, and / or one or more memories other than read-only memory 502 and random access memory 503 described above.

[0117] Embodiments of this application also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the methods provided in the embodiments of this application.

[0118] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 509, and / or installed from a removable medium 511. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0119] In embodiments of this application, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by processor 501, it performs the functions defined in the system of this application embodiment. According to embodiments of this application, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0120] According to embodiments of this application, program code for executing the computer programs provided in the embodiments of this application can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0121] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0122] Those skilled in the art will understand that the features described in the various embodiments of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application.

Claims

1. A joint analysis method based on X-rays, characterized in that, include: Short-range optimization is performed on the sample to be tested based on each candidate optimization strategy to obtain short-range optimization results corresponding to each candidate optimization strategy. The optimization order of multiple test technologies in different candidate optimization strategies is different, and the test conditions of different test technologies are the same. The test technologies include X-ray reflectance detection technology and grazing incidence X-ray fluorescence detection technology. The short-range optimization results characterize the comprehensive operating status information of the entire short-range optimization process. Based on the comprehensive evaluation index of each of the candidate optimization strategies, a target optimization strategy is determined from the multiple candidate optimization strategies, wherein the comprehensive evaluation index is calculated based on the short-range optimization results; The target optimization strategy is used to perform normal optimization processing on the sample to be tested to obtain parameter description information of the sample to be tested, wherein the optimization time of the normal optimization processing is longer than the optimization time of the short-range optimization processing.

2. The joint analysis method according to claim 1, characterized in that, The short-range optimization results include at least one of the following: short-range fitting error, parameter update trajectory, parameter boundary activity, parameter stability, parameter correlation, number of iterations, number of forward model calls, and computational cost. The comprehensive evaluation index is calculated based on the short-range optimization results and includes: For any of the candidate optimization strategies, a comprehensive evaluation index is determined based on at least one of the following: short-range fitting error, parameter update trajectory, parameter boundary activity, parameter stability, parameter correlation, number of iterations, number of forward model calls, and computational cost.

3. The joint analysis method according to claim 2, characterized in that, Based on at least one of the following: short-range fitting error, parameter update trajectory, parameter boundary activity, parameter stability, parameter correlation, number of iterations, number of forward model calls, and computational cost, a comprehensive evaluation index for the candidate optimization strategy is determined, including: The short-range fitting error is determined based on the first measurement curve generated by the X-ray reflectance detection technology and the second measurement curve generated by the grazing incidence X-ray fluorescence detection technology. The first measurement curve reflects the difference in reflectance between the measurement and optimization processes; the second measurement curve reflects the difference in fluorescence intensity between the measurement and optimization processes. Based on the obtained boundary near-boundary dwell ratio, continuous boundary dwell duration and out-of-boundary correction ratio during the execution of different candidate optimization strategies, the parameter boundary activity is determined. The computational cost is determined based on the iteration number exponent and iteration time exponent obtained from the execution of different candidate optimization strategies. The short-range fitting error, the parameter boundary activity, and the computational cost are weighted and summed to obtain a comprehensive evaluation index for the candidate optimization strategy.

4. The joint analysis method according to claim 3, characterized in that, Based on the first measurement curve generated by the X-ray reflectivity detection technology and the second measurement curve generated by the grazing incidence X-ray fluorescence detection technology, the short-range fitting error is determined, including: For X-ray reflectivity detection technology, a first measurement curve is generated based on the measured reflectivity at each detection angle and the calculated reflectivity obtained from short-range optimization simulation. For the grazing incidence X-ray fluorescence detection technology, a second measurement curve is generated based on the measured fluorescence intensity at each detection angle and the calculated fluorescence intensity obtained from short-range optimized simulation. The short-range fitting error is determined based on the fitting error between the first measurement curve and the second measurement curve.

5. The joint analysis method according to any one of claims 1 to 4, characterized in that, Determining the target optimization strategy from a plurality of candidate optimization strategies includes: The candidate optimization strategy corresponding to the optimal value among the multiple comprehensive evaluation indicators is determined as the target optimization strategy.

6. The joint analysis method according to claim 1, characterized in that, The target optimization strategy is used to perform normal optimization processing on the sample to be tested, and the parameter description information of the sample to be tested is obtained, including: Based on the target optimization strategy, the measurement methods of different testing technologies are determined, and the different testing technologies are used to measure and analyze the sample to be tested, so as to obtain the analysis results corresponding to each testing technology. Based on a unified parameter template, the parameter description information is generated according to multiple analysis results.

7. The joint analysis method according to claim 1, characterized in that, The short-range optimization process and the normal optimization process are optimized based on the following test conditions: Detectors using different testing techniques acquire data under the same data acquisition conditions, which include scanning at the same incident angle, scanning at the same position of the sample under test, the same spot position, and the same detector state parameters.

8. The combined analysis method according to claim 1 or 7, characterized in that, The candidate optimization strategies include: The first optimization strategy is to first perform analysis using X-ray reflectance detection technology, and then perform analysis using grazing incidence X-ray fluorescence detection technology based on the analysis results of X-ray reflectance detection technology. The second optimization strategy characterizes the simultaneous execution of X-ray reflectance detection technology and grazing incidence X-ray fluorescence detection technology. The third optimization strategy involves first performing analysis using grazing incidence X-ray fluorescence detection technology, and then performing analysis using X-ray reflectance detection technology based on the analysis results of grazing incidence X-ray fluorescence detection technology.

9. A combined X-ray analysis device, characterized in that, include: The short-range optimization module is used to perform short-range optimization processing on the sample to be tested based on each candidate optimization strategy, and obtain the short-range optimization result corresponding to each candidate optimization strategy. The optimization order of multiple test technologies in different candidate optimization strategies is different, and the test conditions of different test technologies are the same. The test technologies include X-ray reflectance detection technology and grazing incidence X-ray fluorescence detection technology. The short-range optimization result characterizes the comprehensive operating status information of the entire short-range optimization process. The strategy selection module is used to determine the target optimization strategy from multiple candidate optimization strategies based on the comprehensive evaluation index of each candidate optimization strategy, wherein the comprehensive evaluation index is calculated based on the short-range optimization results; The fitting optimization module is used to perform normal optimization processing on the test sample using the target optimization strategy to obtain parameter description information of the test sample, wherein the optimization time of the normal optimization processing is longer than the optimization time of the short-range optimization processing.

10. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

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