X-ray fitting analysis method and x-ray fitting analysis system based on multi-physical variable model

Abstract

To provide an X-ray fitting analysis method and an X-ray fitting analysis system based on a multi-physical variable model.SOLUTION: The method acquires an X-ray measuring signal of a detection object by using an X-ray measuring device, and constructs a plurality of fitting models based on a target frame. An initial fitting process is performed to generate a plurality of initial fitting results and generate a plurality of initial parameter ranges. Performing a first joint fitting process to obtain a plurality of first fitting results that meet a first fitting condition; A classification process is performed based on the data format of the first fitting result to generate a plurality of classification fitting results, and the plurality of classification fitting results are counted to obtain a plurality of classification parameter ranges. A second joint fitting process is performed to obtain a plurality of second fitting results respectively corresponding to the plurality of structural parameters satisfying a second fitting condition.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an analysis method and an analysis system, and more particularly to an X-ray fitting analysis method and an X-ray fitting analysis system based on a multi-physics variable model. [Background technology]

[0002] Conventional 3D NAND flash memory typically has a high aspect ratio structure that is vertically stacked and interconnected. Therefore, channel hole etching is the most critical process step in its development and manufacturing, and any anomalies affect subsequent processes. For example, anomalies in the deposition of block / trap / tunnel layers and channel formation ultimately affect the device's functionality and reliability. Additionally, uniformity is crucial in process control.

[0003] To precisely control process parameters, the critical dimension (CD) of each wordline (WL) in a product is measured. However, traditional measurement methods, such as transmission electron microscope (TEM) or critical dimension scanning electron microscope (CD-SEM), which detect cross-sectional images, are mostly destructive. Optical critical dimension (OCD) spectroscopy is the primary non-destructive method for measuring CD. However, as the number of layers increases, many challenges arise. Summary of the Invention [Problem to be solved by the invention]

[0004] The technical problem to be solved by the present invention is to provide an X-ray fitting analysis method based on a multi-physics variable model to overcome the drawbacks of the prior art. [Means for solving the problem]

[0005] In order to solve the above technical problems, one technical solution adopted in the present invention is to provide an X-ray fitting analysis method based on a multi-physics variable model, which includes: acquiring a plurality of X-ray measurement signals of a detection object by measuring using at least one X-ray measurement device; and configuring a processing device to perform the following steps: constructing a plurality of fitting models based on a target frame of the detection object, where a plurality of structural parameters are defined in the target frame; performing an initial fitting process on each of the X-ray measurement signals using the plurality of fitting models to generate a plurality of initial fitting results for the plurality of structural parameters; compiling the plurality of initial fitting results to generate a plurality of initial parameter ranges; and performing a first co-fitting process on each of the X-ray measurement signals using the plurality of fitting models based on the plurality of initial parameter ranges. and performing a classification process on the plurality of first fitting results based on a data format of the plurality of first fitting results to generate a plurality of classified fitting results and statistically calculating the plurality of classified fitting results to obtain a plurality of classification parameter ranges. Based on the plurality of classification parameter ranges, performing a second joint fitting process on each of the plurality of X-ray measurement signals using the plurality of fitting models to obtain a plurality of second fitting results that satisfy a second fitting condition and respectively correspond to the plurality of structural parameters, and the second joint fitting process performing a fitting analysis on the entire range of the target frame.

[0006] In order to solve the above technical problems, another technical solution adopted in the present invention is to provide an X-ray fitting analysis method based on a multi-physics variable model, which includes: acquiring a plurality of X-ray measurement signals of a detection object by measuring with at least one X-ray measurement device, and configuring a processing device to perform the following steps: constructing a plurality of fitting models based on a target frame of the detection object, where a plurality of structural parameters are defined in the target frame; using the plurality of fitting models to perform an initial fitting process on the plurality of X-ray measurement signals respectively to generate a plurality of initial fitting results for the plurality of structural parameters; collecting the plurality of initial fitting results to generate a plurality of initial parameter ranges; and calculating a plurality of initial parameter ranges based on the plurality of initial parameters. generating a first set of parameters to be verified based on a range; inputting the first set of parameters to be verified into the plurality of fitting models to verify accuracy, and adjusting the parameters to be verified based on the accuracy and the plurality of initial parameter ranges until an optimized set of parameters is obtained when an optimization condition is satisfied; setting a plurality of weight values ​​for the optimized set of parameters, applying the plurality of weight values ​​to the optimized set of parameters and inputting them into the plurality of fitting models to verify weight accuracy; and adjusting the plurality of weight values ​​based on the weight accuracy until the weight accuracy satisfies a weight optimization condition, and applying the plurality of weight values ​​for which the weight optimization condition is satisfied to the optimized set of parameters to generate a weight optimized parameter set.

[0007] In order to solve the above technical problem, yet another technical solution adopted in the present invention is to provide an X-ray fitting analysis system based on a multi-physics variable model, which includes: acquiring a plurality of X-ray measurement signals of a detection object by measuring with at least one X-ray measurement device, and configuring a processing device to execute the following steps: constructing a plurality of fitting models based on a target frame of the detection object, where a plurality of structural parameters are defined in the target frame; using the plurality of fitting models to perform an initial fitting process on the plurality of X-ray measurement signals, respectively, to generate a plurality of initial fitting results for the plurality of structural parameters; collecting statistics of the plurality of initial fitting results to generate a plurality of initial parameter ranges; and calculating a plurality of initial parameter ranges based on the plurality of initial parameters. generating a first set of parameters to be verified based on a range; inputting the first set of parameters to be verified into the plurality of fitting models to verify accuracy, and adjusting the set of parameters to be verified based on the accuracy and the plurality of initial parameter ranges until an optimized set of parameters is obtained when an optimization condition is satisfied; setting a plurality of weight values ​​for the set of optimization parameters, applying the plurality of weight values ​​to the set of optimization parameters and inputting them into the plurality of fitting models to verify weight accuracy; and adjusting the plurality of weight values ​​based on the weight accuracy until the weight accuracy satisfies a weight optimization condition, and applying the plurality of weight values ​​for which the weight optimization condition is satisfied to the set of optimization parameters to generate a weight optimized parameter set.

[0008] In order to solve the above technical problems, yet another technical solution adopted in the present invention is to provide an X-ray fitting analysis system based on a multi-physical variable model. The X-ray fitting analysis system includes at least one X-ray measurement device and a processing device. The at least one X-ray measurement device measures a detection object to obtain a plurality of X-ray measurement signals. The processing device is configured to perform the following steps: the steps include constructing a plurality of fitting models based on a target frame of the object to be detected, and defining a plurality of structural parameters in the target frame; performing an initial fitting process on each of the X-ray measurement signals using the plurality of fitting models to generate a plurality of initial fitting results for the plurality of structural parameters; collecting statistics of the plurality of initial fitting results to generate a plurality of initial parameter ranges; generating a first set of parameters to be verified based on the plurality of initial parameter ranges; inputting the first set of parameters to be verified into the plurality of fitting models to verify accuracy; adjusting the set of parameters to be verified based on the accuracy and the plurality of initial parameter ranges until an optimized set of parameters is obtained when an optimization condition is satisfied; setting a plurality of weight values ​​for the optimized set of parameters, applying the plurality of weight values ​​to the optimized set of parameters and inputting them into the plurality of fitting models to verify weight accuracy; and adjusting the plurality of weight values ​​based on the weight accuracy until the weight accuracy satisfies a weight optimization condition, and applying the plurality of weight values ​​for which the weight optimization condition is satisfied to the optimized set of parameters to generate a weight optimized parameter set. [Effects of the Invention]

[0009] One of the beneficial effects of the present invention is that in the X-ray fitting analysis method and X-ray fitting analysis system based on a multi-physical variable model according to the present invention, a rough first joint fitting process is first performed to classify the fitting results and limit the range of structural parameters, and then a precise second joint fitting process is performed. This makes it possible to reduce the amount of calculation while maintaining a certain level of fitting accuracy when the detected object has multiple material layers, thereby maintaining the stability of the fitting model.

[0010] Furthermore, in the X-ray fitting analysis method and X-ray fitting analysis system based on a multi-physics variable model according to the present invention, a weight mechanism is also applied to perform optimization on the weight, thereby accelerating the optimization speed and reducing the degree of variation in the fitting results.

[0011] In order to further understand the features and technical contents of the present invention, please refer to the following detailed description of the present invention and the drawings, but the drawings provided are for reference and illustration purposes only and are not used to limit the present invention. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a first schematic diagram of an X-ray fitting analysis system based on a multi-physics variable model according to a first embodiment of the present invention; [Figure 2] FIG. 2 is a second schematic diagram of the X-ray fitting analysis system based on a multi-physics variable model according to the first embodiment of the present invention. [Figure 3] 1 is a flowchart of an X-ray fitting analysis method based on a multi-physics variable model according to a first embodiment of the present invention. [Figure 4] FIG. 2 is a first schematic diagram showing a target frame of a detection object according to the first embodiment of the present invention. [Figure 5] FIG. 2 is a second schematic diagram showing a target frame of an object to be detected according to the first embodiment of the present invention. [Figure 6]1 is a set of curve graphs of two initial fitting results according to a first embodiment of the present invention. [Figure 7] 4 is a set of curve graphs of two first fitting results obtained after performing a first joint fitting process according to a first embodiment of the present invention; [Figure 8] 1 shows classification fitting results obtained using a trained classification model according to the first embodiment of the present invention. [Figure 9] 10 shows two second fitting results obtained after performing a second joint fitting process according to the first embodiment of the present invention. [Figure 10] FIG. 10 is a schematic diagram comparing the results of an X-ray fitting analysis according to the first embodiment of the present invention with the results of a fitting analysis of a single physical model. [Figure 11] 10 is a flowchart of an X-ray fitting analysis method based on a multi-physics variable model according to a second embodiment of the present invention. [Figure 12] FIG. 10 is a schematic diagram of the first repetition of step S26 according to the second embodiment of the present invention. [Figure 13] FIG. 10 is a schematic diagram of the second repetition of step S26 according to the second embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0013] The following describes embodiments of the "X-ray fitting analysis method and system based on a multi-physics variable model" disclosed in the present invention through specific examples. Those skilled in the art can understand the advantages and effects of the present invention from the disclosed content. The present invention can be implemented or applied through other different specific embodiments, and various detailed descriptions herein can be modified and changed in various ways based on different perspectives and applications without departing from the spirit of the present invention. It should be noted that the drawings of the present invention are merely schematic and are not drawn to actual scale. The following embodiments will further explain the technical content of the present invention, but the disclosed content is not intended to limit the scope of protection of the present invention. Furthermore, the term "or" in this specification should be understood to include any one or more combinations of the related listed items, depending on the actual situation. [First Example]

[0014] Fig. 1 is a first schematic diagram of an X-ray fitting analysis system based on a multi-physics variable model according to a first embodiment of the present invention. Fig. 2 is a second schematic diagram of an X-ray fitting analysis system based on a multi-physics variable model according to the first embodiment of the present invention. As shown in Figs. 1 and 2, the first embodiment of the present invention provides an X-ray fitting analysis system 1 based on a multi-physics variable model, which includes a plurality of X-ray measurement devices 10 and a processing device 12.

[0015] In this embodiment, the X-ray measurement apparatus 10 may include an X-ray generator, an X-ray optical element group, an X-ray detector, and a sample stage. The X-ray generator may be used to generate an X-ray beam having a predetermined energy. The X-ray optical element group may guide and focus the X-ray beam on the detection object SP. The X-ray detector may be used to receive a signal generated by irradiating the detection object SP with the X-ray beam. However, the present invention is not limited to this. The X-ray measurement apparatus 10 may measure the detection object SP and acquire multiple X-ray measurement signals Sx. Although FIG. 1 shows multiple X-ray measurement apparatuses 10, in practice, a single X-ray measurement apparatus 10 may be configured to measure the detection object SP under multiple different measurement conditions. Alternatively, as shown in FIG. 2, multiple different X-ray measurement apparatuses 10 may be configured to measure the detection object SP. In this embodiment, such an X-ray measurement device 10 may be an X-ray reflectivity (XRR) measurement device, an X-ray fluorescence spectrometer (XRF) measurement device, a small-angle X-ray scattering (SAX) measurement device, an X-ray diffractometer (XRD) measurement device, or any other device capable of measurement using X-rays as a light source.

[0016] Each X-ray measurement signal Sx may include one or more of a transmission pattern, a reflection pattern, a diffraction pattern, and a scattering pattern. For example, when the X-ray measurement signal Sx includes a reflection pattern, X-rays may be incident on the detection object SP at a plurality of predetermined angles, and then the reflected X-rays may be received using a receiver of the X-ray measurement device 10, thereby performing fitting analysis on the X-ray measurement signal to acquire structural parameters of the detection object SP, including, for example, one or more of thickness, density, and roughness.

[0017] The processing device 12 may be, for example, a computer system including a processor and a memory, and may be configured to execute a stored instruction set or code to acquire the X-ray measurement signal and perform the fitting analysis. Furthermore, the processing device 12 may control the X-ray measurement device 10 to perform a corresponding measurement process on the detection target SP.

[0018] 3, which is a flowchart of an X-ray fitting analysis method based on a multi-physics variable model according to a first embodiment of the present invention. As shown in FIG. 3, the X-ray fitting analysis method may include the following steps.

[0019] Step S10: A plurality of X-ray measurement signals of the detection object are obtained by performing measurement using the X-ray measurement device 10. In this step, the number of X-ray measurement devices 10 may be one or more.

[0020] The processing unit 12 may then be configured to perform the following steps:

[0021] Step S11: Construct multiple fitting models based on the target frame of the detection object. FIGS. 4 and 5 are first and second schematic diagrams, respectively, showing the target frame of the detection object SP according to the first embodiment of the present invention. In step S11, as shown in FIG. 4, the target frame of the detection object SP may be, for example, a sample in which a pattern on a carbon film has not been transferred to a multi-material layer structure. Alternatively, as shown in FIG. 5, the target frame of the detection object SP may be, for example, a sample in which a pattern on a carbon film has already been transferred to a multi-material layer structure and the carbon film has been removed. Therefore, the target frame may have multiple material layers, and multiple structural parameters may be defined, including, for example, the thickness, density, or roughness of each material layer.

[0022] Step S12: Using the fitting models, an initial fitting process is performed on the measurement signals, respectively, to generate a plurality of initial fitting results for the structural parameters.

[0023] The initial fitting process uses the processing device 12 to execute a plurality of electromagnetic wave calculation engines corresponding to the fitting models, respectively, to perform spectrum fitting analysis on the corresponding X-ray measurement signals Sx based on the target frame, and obtain corresponding initial fitting results. The electromagnetic wave calculation engines may include, for example, one or more of a Finite-Difference Time-Domain (FDTD) algorithm, a Distorted Wave Born Approximation (DWBA) algorithm, a Rigorous Coupled Wave Analysis (RCWA) algorithm, a Discrete Dipole Approximation (DDP) algorithm, and a Boundary Element Method (BEM).

[0024] In detail, for example, data measured after X-ray beams of different energies are incident may be fitted using the electromagnetic wave calculation engine described above to obtain structural parameters of the target frame and corresponding accuracy. The accuracy may include the variance and error amount of a specific structural parameter, such as density. The variance may be, for example, the average value of the structural parameter generated after multiple fitting runs divided by the standard deviation. The error amount may be described using a cost function to evaluate the fit of the model to the fitting target data. The cost function may be, for example, the mean squared error (MSE) of the fitting result to the fitting target data. In this way, when multiple initial fitting runs are performed using one electromagnetic wave engine for one fitting model having N layers, one error amount and N variance numbers of one set of structural parameters (the density average / density standard deviation for each of the N material layers) can be generated. Therefore, when we have M fitting models, each fitted by M electromagnetic wave engines, we can generate M error quantities and M sets of N variations. See FIG. 6, which shows a set of curve graphs of two initial fitting results according to a first embodiment of the present invention. The top two curve graphs in FIG. 6 plot the X-ray measurement signal Sx and the fitted reflectivity versus angle. The bottom two curve graphs in FIG. 6 plot the resulting density (divided into real part rho and imaginary part irho) versus depth, with the lower data being the accuracy expressed in MSE (denoted as chisq), which is approximately 452192 and 688786 (decimals omitted), respectively.

[0025] Step S13: These initial fitting results are statistically analyzed to generate a number of initial parameter ranges.

[0026] Therefore, after performing initial fitting to obtain multiple sets of structural parameters, preliminary statistics may be performed to obtain initial parameter ranges for each structural parameter. It should be noted that as the number of layers of the detection target SP increases, too many floating parameters can make the fitting model very complex, further increasing the calculation time exponentially. In reality, simply modeling and fitting is not feasible for 3D NAND structures. Because the channel hole depth doubles with each node transition, the sensitivity of the critical dimension of the bottom layer becomes too weak. At the same time, the CDs of adjacent word lines may have the same spectral response and be indistinguishable, leading to correlation issues in the modeling. To improve the model stability, it is possible to simplify by combining the CD of the bottom layer with the CD of the middle layer, but this reduces the accuracy of the CD results for the bottom layer. Furthermore, changes in the relationship between the CD of the middle layer and the CD of the bottom layer further reduce accuracy. Therefore, in the present invention, a new fitting method is employed to perform analysis, reducing the amount of calculation and improving accuracy.

[0027] Step S14: Based on the initial parameter range, a first co-fitting process is performed on the measurement signals using the fitting model, respectively, to obtain a plurality of first fitting results that satisfy the first fitting condition.

[0028] In step S14, the first co-fitting process performs a rough fitting for a predetermined range of the target frame. For example, if the target frame is a NAND including 400 material layers, the predetermined range may be the odd layers of the 400 material layers. Therefore, in step S14, the first co-fitting process performs fitting for structural parameters such as density, thickness, and roughness of the odd layers.

[0029] In the first joint fitting process, a first group of parameters to be verified may be generated based on the initial parameter range obtained in step S13. The first group of parameters to be verified is structural parameters corresponding to the predetermined range (200 layers therein). The first group of parameters to be verified is input to the multiple fitting models to verify the first accuracy. For example, the first group of parameters to be verified is input to the fitting model and fitted using the corresponding electromagnetic wave calculation engine, and then an error amount and N variation numbers of the first group of parameters to be verified are generated, and it is determined whether the error amount and variation numbers are lower than the initial fitting result.

[0030] If the error amount and the number of variations are not lower than those of the initial fitting result, the first parameter group to be verified is adjusted, for example, by adjusting random numbers and cross-combinations based on the initial parameter range, and verification is performed again until the first fitting condition is satisfied, and the first parameter group to be verified that satisfies the first fitting condition is set as the first fitting result. Satisfying the first fitting condition means, for example, that the error amount and the number of variations are lower than those of the initial fitting result and that the difference is greater than a predetermined value.

[0031] For example, see Figure 7, which shows a set of two curve graphs of the first fitting results obtained after performing the first joint fitting process according to the first embodiment of the present invention. The upper two curve graphs in Figure 7 are plots of the X-ray measurement signal Sx and the reflectivity obtained after performing the first joint fitting process versus angle. The lower two curve graphs in Figure 7 are plots of the obtained density (divided into real part rho and imaginary part irho) versus depth, and the lower data are the accuracy expressed in MSE (denoted as chisq), which are approximately 15374 and 349764 (decimals omitted), respectively. Both are degraded compared to the initial fitting results.

[0032] Step S15: Based on the data format of the first fitting result, a classification process is performed on the first fitting result to generate a plurality of classified fitting results.

[0033] In this step, classification accuracy and efficiency may be improved by combining a decision tree algorithm and machine learning. Specifically, the first fitting results generated in step S14 may be input into a trained classification model to generate classification fitting results. The trained classification model is trained to classify these first fitting results using a classification tree analysis process or a regression tree analysis process, and the number of these first fitting results is greater than the number of classification results. For example, a classification tree analysis process or a regression tree analysis process may be trained to extract data features of the first fitting results, such as highs and lows, bends, peaks, and valleys on a curve graph, and similarity clustering may be performed on the first fitting results, while ranking the accuracy (including the amount of variation and cost function) to extract classifications with high accuracy. Therefore, the decision tree algorithm can determine the optimal classification and reduce the uncertainty of the fitting model structure. See FIG. 8, which shows classification fitting results obtained by a trained classification model according to a first embodiment of the present invention. It should be noted that although the variation characteristics of structural parameters (e.g., density) with respect to depth are used as the classification criteria for data types here, the present invention is not limited thereto, and other structural parameters such as thickness and roughness may also be used as the classification criteria in step S15.

[0034] Step S16: These classification fitting results are statistically analyzed to obtain multiple classification parameter ranges.

[0035] Taking Figure 8 as an example, after classification in step S15, the classification fitting results show five curve types, and five corresponding parameter ranges can be obtained. In this way, the range of each structural parameter can be limited, significantly reducing the amount of calculation required for fitting analysis. Furthermore, since the classification parameter ranges obtained in this step are based on the results of the first joint fitting process, their accuracy is optimized.

[0036] Step S17: Based on the classification parameter ranges, a second joint fitting process is performed on each of the measurement signals using the fitting models, and multiple second fitting results that satisfy the second fitting conditions and correspond to the structural parameters are obtained. In this step, the second joint fitting process performs fitting analysis on the entire range of the target frame. Although the number of layers to be calculated is greater than in the first joint fitting process, the range of structural parameters that need to be calculated is already limited, so the overall calculation amount can be significantly reduced while still maintaining a certain level of accuracy.

[0037] Similarly, in the second joint fitting process, a second set of parameters to be verified may be generated based on the initial parameter range obtained in step S16, and the second set of parameters to be verified may be input to the multiple fitting models described above to verify the second accuracy. For example, the second set of parameters to be verified may be input to the fitting model and fitted using the corresponding electromagnetic wave calculation engine, after which one error amount and N variation numbers of the second set of parameters to be verified are generated, and it is determined whether the error amount and the variation numbers are lower than the first fitting result.

[0038] If the error amount and the number of variations are not lower than those of the first fitting result, the second parameter group to be verified is adjusted, for example, by randomizing and cross-combining based on the classification parameter range, and verified again until the second fitting condition is met. The second parameter group to be verified that satisfies the second fitting condition is the second fitting result, which is the final fitting result. Satisfying the second fitting condition means, for example, that the error amount and the number of variations are lower than those of the first fitting result and the difference is greater than another predetermined value. See FIGS. 9 and 10. FIG. 9 shows two second fitting results obtained after performing the second joint fitting process according to the first embodiment of the present invention. FIG. 10 is a schematic diagram comparing the X-ray fitting analysis result according to the first embodiment of the present invention with the fitting analysis result of a single physical model.

[0039] The upper two curves in Fig. 9 are plots of the X-ray measurement signal Sx (thick line) and the reflectivity (thin line) obtained after the second joint fitting process versus angle. The lower two curves in Fig. 9 are plots of the obtained density (divided into real part rho and imaginary part irho) versus depth. The lower data are the accuracy expressed in MSE (denoted as chisq), which is approximately 6104 and 5740 (decimals omitted), respectively. Both are reduced compared to the initial fitting result and the first fitting result.

[0040] In the comparison results in Figure 10, structural parameters such as thickness, density (real part), density (imaginary part), and roughness are compared. The upper row is for the single-physics model, and the lower row is for the multi-physics model. Here, the histogram represents the average value, and the thin line represents the number of variations. As can be seen from Figure 10, the calculation using the multi-physics model results in a smaller amount of variation. [Second Example]

[0041] A second embodiment of the present invention provides another X-ray fitting analysis system based on a multi-physics variable model. Although the X-ray fitting analysis system of this embodiment differs in the details of fitting analysis, the system structure is basically the same as the system structure shown in Figures 2 and 3, so repeated explanation will be omitted here.

[0042] 11 is a flowchart of an X-ray fitting analysis method based on a multi-physics variable model according to a second embodiment of the present invention. As shown in FIG. 11, the second embodiment of the present invention provides an X-ray fitting analysis method based on a multi-physics variable model. The X-ray fitting analysis method includes the following steps:

[0043] Step S20: A plurality of X-ray measurement signals of the detection object are obtained by measuring using the X-ray measurement device.

[0044] The processing device is then configured to perform the following steps:

[0045] Step S21: Construct a plurality of fitting models based on the target frame of the object to be detected. As described in the first embodiment, a plurality of structural parameters are defined in the target frame.

[0046] Step S22: Using the fitting models, an initial fitting process is performed on the X-ray measurement signals, respectively, to generate a plurality of initial fitting results for the structural parameters.

[0047] Step S23: These initial fitting results are statistically analyzed to generate a number of initial parameter ranges.

[0048] Step S24: Based on these initial parameter ranges, a first group of parameters to be verified is generated.

[0049] Step S25: This first group of parameters to be verified is input into these fitting models to verify accuracy, and based on this accuracy and these initial parameter ranges, this group of parameters to be verified is adjusted until an optimized group of parameters is obtained when the optimization conditions are met.

[0050] Here, in steps S23 to S25, multiple parameter sets to be verified may be generated based on the initial parameter range obtained in step S23, and each parameter set to be verified may be input to the multiple fitting models described above to verify accuracy. For example, each parameter set to be verified may be input to a fitting model, and fitting may be performed using a corresponding electromagnetic wave calculation engine. An error amount and multiple corresponding variation numbers may be generated, and then it may be determined whether the error amount and the variation number satisfy an optimization condition. For example, the optimization condition may be that the error amount is lower than a predetermined error amount and the variation number is lower than a predetermined variation number. If the error amount and the variation number are not lower, the parameter set to be verified may be adjusted, for example, using random numbers and cross-combinations based on the initial parameter range, and verified again until the optimization condition is met. The multiple parameter sets to be verified that satisfy the optimization condition are designated as the optimized parameter set.

[0051] Step S26: Set multiple weights for the optimized parameter set, apply these weights to the optimized parameter set, and input these weights into the fitting model to verify the weight accuracy. For example, each parameter set to be verified that satisfies the optimization conditions includes structural parameters such as the thickness, density, and roughness of each material layer of the detection object. See FIG. 12, which is a schematic diagram of the first iteration of step S26 according to the second embodiment of the present invention. In the first iteration, each parameter set to be verified in the optimized parameter set may be multiplied by a weight value between 0 and 1 (note that the sum of all weight values ​​is 1). For example, there are two parameter sets to be verified, and by multiplying each by a weight value of 0.5, an optimized parameter set with applied weights is obtained.

[0052] Next, the parameter set to be verified is input into these fitting models to generate multiple verification results. The weight accuracy is verified by determining the amount of variation in the parameters of each verification result and the error values ​​for these parameters relative to the X-ray measurement signal. As can be seen from the thickness, density, and roughness space distribution plots shown in the center of Figure 12, the structural parameter results obtained are relatively variable. The right side of Figure 12 shows two fitting results and two density curve graphs. The resulting weight accuracy is shown in MSE (denoted as chisq), which is approximately 9339 and 8474 (decimals omitted), respectively, suggesting that there is still room for optimization of the weight values. Furthermore, two new parameter sets to be verified may be generated in the first iteration and used in the second iteration.

[0053] Step S27: Adjust these weight values ​​according to the weight precision until the weight precision satisfies the weight optimization condition, and apply these weight values ​​that satisfy the weight optimization condition to the optimization parameter set to generate a weight optimization parameter set.

[0054] 13 is a schematic diagram of the second iteration of step S26 according to the second embodiment of the present invention. In the second iteration, the other two parameter sets to be verified generated from the first iteration are multiplied by weights of 0.7 and 0.3, respectively, to obtain weighted optimized parameter sets.

[0055] Similarly, two new sets of parameters to be verified were input into these fitting models to generate multiple verification results. The weight accuracy was verified by determining the amount of variation in the multiple parameters in each verification result and the error values ​​for these parameters relative to the X-ray measurement signals. As can be seen from the thickness, density, and roughness space distribution plots shown in the center of Figure 13, the obtained structural parameter results converged more than in the first iteration, indicating that weightings such as 0.7 and 0.3 may be better. The right side of Figure 13 shows two fitting results and two density curve graphs. The resulting weight accuracy is shown in MSE (denoted as chisq), which is approximately 8453 and 5505 (decimals omitted), respectively, and appears to be higher accuracy than in the first iteration.

[0056] Therefore, when the weight accuracy reaches a predetermined range (for example, when the MSE is smaller than a certain value), it means that the weight optimization condition is met, and these weight values ​​that satisfy the weight optimization condition may be applied to an optimization parameter set to generate a weight optimization parameter set, which defines multiple structural parameters obtained by fitting analysis. [Beneficial Effects of Examples]

[0057] One of the beneficial effects of the present invention is that in the X-ray fitting analysis method and X-ray fitting analysis system based on a multi-physical variable model according to the present invention, a rough first joint fitting process is first performed to classify the fitting results and limit the range of structural parameters, and then a precise second joint fitting process is performed. This makes it possible to reduce the amount of calculation while maintaining a certain level of fitting accuracy when the detected object has multiple material layers, thereby maintaining the stability of the fitting model.

[0058] Furthermore, in the X-ray fitting analysis method and X-ray fitting analysis system based on a multi-physics variable model according to the present invention, a weight mechanism is also applied to perform optimization on the weight, thereby accelerating the optimization speed and reducing the degree of variation in the fitting results.

[0059] The above disclosure is merely a preferred embodiment of the present invention and does not limit the scope of the claims of the present invention. Therefore, all equivalent technical modifications made using the specification and drawings of the present invention are included in the scope of the claims of the present invention. [Explanation of symbols]

[0060] 1: X-ray fitting analysis system 10: X-ray measurement device 12: Processing device SP: Object to be detected Sx: X-ray measurement signal S10, S11, S12, S13, S14, S15, S16, S17, S20, S21, S22, S23, S24, S25, S26, S27: Steps rho: real part irho: imaginary part chisq:MSE

Claims

1. 1. An X-ray fitting analysis method based on a multi-physics variable model, comprising: The X-ray fitting analysis method includes: obtaining a plurality of X-ray measurement signals of the object to be detected by measuring the object using at least one X-ray measurement device; Configuring a processing device to perform the steps of: Including, The steps include: A plurality of fitting models are constructed based on a target frame of the detection object, and a plurality of structural parameters are defined in the target frame; performing an initial fitting process on the plurality of X-ray measurement signals using the plurality of fitting models, respectively, to generate a plurality of initial fitting results for the plurality of structural parameters; statistically analyzing the initial fitting results to generate a plurality of initial parameter ranges; performing a first co-fitting process on the X-ray measurement signals using the plurality of fitting models based on the plurality of initial parameter ranges, respectively, to obtain a plurality of first fitting results that satisfy a first fitting condition, and the first co-fitting process fits to a predetermined range of the target frame; performing a classification process on the plurality of first fitting results based on a data format of the plurality of first fitting results to generate a plurality of classified fitting results; statistically analyzing the plurality of classification fitting results to obtain a plurality of classification parameter ranges; performing a second joint fitting process on the plurality of X-ray measurement signals respectively using the plurality of fitting models according to the plurality of classification parameter ranges, to obtain a plurality of second fitting results that satisfy a second fitting condition and respectively correspond to the plurality of structural parameters, and the second joint fitting process performs a fitting analysis on the entire range of the target frame; Including, An X-ray fitting analysis method comprising:

2. The step of acquiring a plurality of X-ray measurement signals of the detection object by measuring using the at least one X-ray measurement device includes: One of the X-ray measurement devices is configured to measure the detection object under a plurality of different measurement conditions, or a plurality of different X-ray measurement devices are configured to measure the detection object. The X-ray fitting analysis method according to claim 1 .

3. each of the X-ray measurement signals includes one or more of a transmission pattern, a reflection pattern, a diffraction pattern, and a scattering pattern, and each of the structural parameters includes one or more of a thickness, a density, and a roughness; The X-ray fitting analysis method according to claim 1 .

4. the target frame comprises a plurality of material layers, and the initial fitting process includes using the processing device to execute an electromagnetic wave calculation engine corresponding to each of the fitting models to perform a spectrum fitting analysis on the corresponding X-ray measurement signal based on the target frame to obtain the corresponding initial fitting result. The X-ray fitting analysis method according to claim 1 .

5. The first joint fitting process comprises: generating a first group of parameters to be verified corresponding to the structural parameters within the predetermined ranges based on the plurality of initial parameter ranges; inputting the first group of parameters to be verified into the plurality of fitting models to verify a first accuracy; adjusting the first group of parameters to be verified based on the first accuracy and the plurality of initial parameter ranges until the first fitting condition is satisfied; and determining the first group of parameters to be verified that satisfy the first fitting condition as the first fitting result. The X-ray fitting analysis method according to claim 1 .

6. The classification process comprises: inputting the plurality of first fitting results into a trained classification model to generate the plurality of classification fitting results, the trained classification model classifying the plurality of first fitting results using a classification tree analysis process or a regression tree analysis process, and the number of the first fitting results is greater than the number of the classification results; The X-ray fitting analysis method according to claim 5 .

7. the second joint fitting process: generating a second set of parameters to be verified corresponding to the plurality of structural parameters within the entire range based on the plurality of classification parameter ranges; inputting the second group of parameters to be verified into the plurality of fitting models to verify a second accuracy; adjusting the second group of parameters to be verified based on the second accuracy and the plurality of classification parameter ranges until the second fitting condition is satisfied; and determining the second group of parameters to be verified that satisfy the second fitting condition as the second fitting result. The X-ray fitting analysis method according to claim 1 .

8. 1. An X-ray fitting analysis method based on a multi-physics variable model, comprising: The X-ray fitting analysis method includes: obtaining a plurality of X-ray measurement signals of the object to be detected by measuring the object using at least one X-ray measurement device; Configuring a processing device to perform the steps of: Including, The steps include: A plurality of fitting models are constructed based on a target frame of the detection object, and a plurality of structural parameters are defined in the target frame; performing an initial fitting process on the plurality of X-ray measurement signals using the plurality of fitting models, respectively, to generate a plurality of initial fitting results for the plurality of structural parameters; statistically analyzing the initial fitting results to generate a plurality of initial parameter ranges; generating a first set of parameters to be verified based on the plurality of initial parameter ranges; inputting the first group of parameters to be verified into the plurality of fitting models to verify accuracy, and adjusting the group of parameters to be verified based on the accuracy and the plurality of initial parameter ranges until an optimized group of parameters is obtained when an optimization condition is satisfied; setting a plurality of weight values ​​for the set of optimization parameters, applying the plurality of weight values ​​to the set of optimization parameters and inputting them into the plurality of fitting models, and verifying weight accuracy; adjusting the weight values ​​based on the weight accuracy until the weight accuracy satisfies a weight optimization condition, and applying the weight values ​​for which the weight optimization condition is satisfied to the optimization parameter set to generate a weight optimization parameter set; Including, An X-ray fitting analysis method comprising:

9. The step of inputting the first group of parameters to be verified into the plurality of fitting models to verify the accuracy includes: inputting the group of parameters to be verified into the plurality of fitting models to generate a plurality of verification results, and verifying the weight accuracy by determining the amount of variation of the plurality of parameters in each of the verification results and an error value for the plurality of X-ray measurement signals; The X-ray fitting analysis method according to claim 8.

10. each of the X-ray measurement signals includes one or more of a transmission pattern, a reflection pattern, a diffraction pattern, and a scattering pattern, and each of the structural parameters includes one or more of a thickness, a density, and a roughness; The X-ray fitting analysis method according to claim 9 .

11. 1. A multi-physics variable model based X-ray fitting analysis system, comprising: The X-ray fitting analysis system includes: at least one X-ray measurement device that measures a detection object and acquires a plurality of X-ray measurement signals; a processing unit configured to perform the steps of: Equipped with The steps include: A plurality of fitting models are constructed based on a target frame of the detection object, and a plurality of structural parameters are defined in the target frame; performing an initial fitting process on the plurality of X-ray measurement signals using the plurality of fitting models, respectively, to generate a plurality of initial fitting results for the plurality of structural parameters; statistically analyzing the initial fitting results to generate a plurality of initial parameter ranges; performing a first co-fitting process on the X-ray measurement signals using the plurality of fitting models based on the plurality of initial parameter ranges, respectively, to obtain a plurality of first fitting results that satisfy a first fitting condition, and the first co-fitting process fits to a predetermined range of the target frame; performing a classification process on the plurality of first fitting results based on a data format of the plurality of first fitting results to generate a plurality of classified fitting results; statistically analyzing the plurality of classification fitting results to obtain a plurality of classification parameter ranges; performing a second joint fitting process on the plurality of X-ray measurement signals respectively using the plurality of fitting models according to the plurality of classification parameter ranges, to obtain a plurality of second fitting results that satisfy a second fitting condition and respectively correspond to the plurality of structural parameters, and the second joint fitting process performs a fitting analysis on the entire range of the target frame; Including, An X-ray fitting analysis system comprising:

12. The step of acquiring a plurality of X-ray measurement signals of the detection object by measuring using the at least one X-ray measurement device includes: One of the X-ray measurement devices is configured to measure the detection object under a plurality of different measurement conditions, or a plurality of different X-ray measurement devices are configured to measure the detection object. The X-ray fitting analysis system according to claim 11.

13. each of the X-ray measurement signals includes one or more of a transmission pattern, a reflection pattern, a diffraction pattern, and a scattering pattern, and each of the structural parameters includes one or more of a thickness, a density, and a roughness; The X-ray fitting analysis system according to claim 11.

14. the target frame comprises a plurality of material layers, and the initial fitting process includes using the processing device to execute an electromagnetic wave calculation engine corresponding to each of the fitting models to perform a spectrum fitting analysis on the corresponding X-ray measurement signal based on the target frame to obtain the corresponding initial fitting result. The X-ray fitting analysis system according to claim 11.

15. The first joint fitting process comprises: generating a first group of parameters to be verified corresponding to the structural parameters within the predetermined ranges based on the plurality of initial parameter ranges; inputting the first group of parameters to be verified into the plurality of fitting models to verify a first accuracy; adjusting the first group of parameters to be verified based on the first accuracy and the plurality of initial parameter ranges until the first fitting condition is satisfied; and determining the first group of parameters to be verified that satisfy the first fitting condition as the first fitting result. The X-ray fitting analysis system according to claim 11.

16. The classification process comprises: inputting the plurality of first fitting results into a trained classification model to generate the plurality of classification fitting results, the trained classification model classifying the plurality of first fitting results using a classification tree analysis process or a regression tree analysis process, and the number of the first fitting results is greater than the number of the classification results; The X-ray fitting analysis system according to claim 15.

17. the second joint fitting process: generating a second set of parameters to be verified corresponding to the plurality of structural parameters within the entire range based on the plurality of classification parameter ranges; inputting the second group of parameters to be verified into the plurality of fitting models to verify a second accuracy; adjusting the second group of parameters to be verified based on the second accuracy and the plurality of classification parameter ranges until the second fitting condition is satisfied; and determining the second group of parameters to be verified that satisfy the second fitting condition as the second fitting result. The X-ray fitting analysis system according to claim 11.

18. 1. A multi-physics variable model based X-ray fitting analysis system, comprising: The X-ray fitting analysis system includes: at least one X-ray measurement device that measures a detection object and acquires a plurality of X-ray measurement signals; a processing unit configured to perform the steps of: Equipped with The steps include: A plurality of fitting models are constructed based on a target frame of the detection object, and a plurality of structural parameters are defined in the target frame; performing an initial fitting process on the plurality of X-ray measurement signals using the plurality of fitting models, respectively, to generate a plurality of initial fitting results for the plurality of structural parameters; statistically analyzing the initial fitting results to generate a plurality of initial parameter ranges; generating a first set of parameters to be verified based on the plurality of initial parameter ranges; inputting the first group of parameters to be verified into the plurality of fitting models to verify accuracy, and adjusting the group of parameters to be verified based on the accuracy and the plurality of initial parameter ranges until an optimized group of parameters is obtained when an optimization condition is satisfied; setting a plurality of weight values ​​for the set of optimization parameters, applying the plurality of weight values ​​to the set of optimization parameters and inputting them into the plurality of fitting models, and verifying weight accuracy; adjusting the weight values ​​based on the weight accuracy until the weight accuracy satisfies a weight optimization condition, and applying the weight values ​​for which the weight optimization condition is satisfied to the optimization parameter set to generate a weight optimization parameter set; Including, An X-ray fitting analysis system comprising:

19. The step of inputting the first group of parameters to be verified into the plurality of fitting models to verify the accuracy includes: inputting the group of parameters to be verified into the plurality of fitting models to generate a plurality of verification results, and verifying the weight accuracy by determining the amount of variation of the plurality of parameters in each of the verification results and an error value for the plurality of X-ray measurement signals; 19. The X-ray fitting analysis system according to claim 18.

20. each of the X-ray measurement signals includes one or more of a transmission pattern, a reflection pattern, a diffraction pattern, and a scattering pattern, and each of the structural parameters includes one or more of a thickness, a density, and a roughness; 20. The X-ray fitting analysis system according to claim 19.

Images