X-ray measurement system and x-ray measurement method

Abstract

To provide an X-ray measurement system and an X-ray measurement method.SOLUTION: A plurality of measurement signals generated by a detection object DT reflecting a plurality of X-ray beams with different energies are respectively collected by using a photodetector 2, and a plurality of fitting models are constructed based on a target structure of the detection object. Spectrum fitting analysis is performed on a plurality of metrology signals by using the fitting model to generate a plurality of fitting results to be optimized, perform statistics, and generate a plurality of parameter fitting ranges. A to-be-verified parameter group is generated based on the plurality of parameter fitting ranges, the to-be-verified parameter group is input into the plurality of fitting models, an accuracy of the to-be-verified parameter group is verified, and the to-be-verified parameter group is adjusted based on the accuracy and the plurality of parameter fitting ranges until an optimization condition is met, to obtain an optimized fitting result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an X-ray measurement system and an X-ray measurement method, and more particularly to a wide energy band range measurement of X-rays and a measurement method using a multi-model algorithm. [Background technology]

[0002] As the amount of calculations performed by processors gradually increases, the demands on semiconductor circuits also become more precise. To manufacture circuits with an accuracy of 7 nanometers or less, exposure equipment that uses extreme ultraviolet (EUV) light with a wavelength of less than 13.5 nm is selected. Because EUV has a short wavelength, a reflecting mirror must be provided on the photomask to reflect the circuit pattern onto the wafer. The reflecting mirror is made of an alternating combination of Mo and Si, and at least 30 to 40 layers are used to achieve an EUV reflectivity of 80% or more.

[0003] Currently, the only way to monitor important parameters such as multilayer film thickness within EUV photomasks is to use conventional measurement equipment such as a transmission electron microscope (TEM) or secondary ion mass spectrometer (SIMS). These methods have drawbacks, such as being time-consuming, requiring sample destruction, and only being able to analyze elements in the surface layer. While there are non-destructive testing techniques using a single-wavelength light source, these techniques are unable to inspect deeper layers due to limitations in the attenuation length of the incident light that penetrates the sample. Summary of the Invention [Problem to be solved by the invention]

[0004] Therefore, in order to overcome the above drawbacks, it has become one of the important problems to be solved in the technical field by using a non-destructive measurement method to obtain more accurate measurement results. [Means for solving the problem]

[0005] The technical problem to be solved by the present invention is to provide an X-ray measurement system that addresses the shortcomings of the prior art, the X-ray measurement system including an X-ray source that generates multiple X-ray beams with different energies and irradiates the beams onto a detection target, a photodetector that collects multiple measurement signals generated by the detection target reflecting the multiple X-ray beams, and a processing device configured to perform the following steps: constructing multiple fitting models based on a target structure of the detection target, performing a spectral fitting analysis on the multiple measurement signals using the multiple fitting models to generate multiple optimization-target fitting results, compiling the multiple optimization-target fitting results to generate multiple parameter fitting ranges, generating a validation-target parameter group based on the multiple parameter fitting ranges, inputting the validation-target parameter group into the multiple fitting models, verifying accuracy of the validation-target parameter group, adjusting the validation-target parameter group based on the accuracy and the multiple parameter fitting ranges until an optimization condition is met, and determining the validation-target parameter group that satisfies the optimization condition as the optimization fitting result.

[0006] To solve the above technical problems, one technical solution adopted in the present invention is to provide an X-ray measurement method, which includes using at least one X-ray source to generate multiple X-ray beams having different energies and irradiating the beams onto a detection object, using a photodetector to collect multiple measurement signals generated by the detection object reflecting the multiple X-ray beams, and using a processing device to perform the following steps, including constructing multiple fitting models based on a target structure of the detection object, performing a spectral fitting analysis on the multiple measurement signals using the multiple fitting models to generate multiple optimization-target fitting results, compiling the multiple optimization-target fitting results and generating multiple parameter fitting ranges, generating a validation-target parameter group based on the multiple parameter fitting ranges, inputting the validation-target parameter group into the multiple fitting models, verifying accuracy of the validation-target parameter group, adjusting the validation-target parameter group based on the accuracy and the multiple parameter fitting ranges until an optimization condition is met, and setting the validation-target parameter group that satisfies the optimization condition as the optimization fitting result. [Effects of the Invention]

[0007] One of the beneficial effects of the present invention is that the X-ray measurement system and method provided by the present invention can reduce the standard deviation of parameters in the optimized fitting result, i.e., reduce the error value, by using the following technical means: "irradiating a detection object with multiple X-ray beams having different energies, and using a photodetector to collect multiple measurement signals generated by the detection object reflecting the X-ray beams, respectively," "constructing multiple fitting models based on the target structure of the detection object," and "performing spectral fitting analysis on the multiple measurement signals using the fitting models to generate multiple optimization target fitting results, statistically analyzing the multiple optimization target fitting results to generate multiple parameter fitting ranges, generating a parameter group to be verified based on the multiple parameter fitting ranges, inputting the parameter group to be verified into the multiple fitting models, verifying the accuracy of the parameter group to be verified, adjusting the parameter group to be verified based on the accuracy and the multiple parameter fitting ranges until an optimization condition is met, and determining the parameter group to be verified that satisfies the optimization condition as the optimized fitting result."

[0008] 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]

[0009] [Figure 1] 1 is a schematic diagram of an X-ray measurement system according to an embodiment of the present invention. [Figure 2] FIG. 1 is a schematic diagram of an X-ray metrology system having multiple target materials in accordance with an embodiment of the present invention. [Figure 3A] FIG. 1 is a functional block diagram of an X-ray measurement method according to an embodiment of the present invention. [Figure 3B] FIG. 1 is a functional block diagram of an X-ray measurement method according to an embodiment of the present invention. [Figure 4] X-ray energy-depth diagram corresponding to an element or compound. [Figure 5] FIG. 2 is a spectrum diagram of a first X-ray beam of the X-ray metrology system according to the present invention. [Figure 6] FIG. 10 is a spectrum diagram of a second X-ray beam of the X-ray metrology system according to the present invention. [Figure 7] FIG. 10 is a spectrum diagram of a third X-ray beam of the X-ray metrology system according to the present invention. [Figure 8] FIG. 2 is a schematic diagram of a first fitting model of the X-ray measurement system according to the present invention. [Figure 9] FIG. 10 is a schematic diagram of a second fitting model of the X-ray measurement system according to the present invention. [Figure 10] FIG. 10 is a schematic diagram of a third fitting model of the X-ray measurement system according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0010] The following describes the embodiments of the "X-ray measurement system and X-ray measurement method" 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.

[0011] Fig. 1 is a schematic diagram of an X-ray metrology system according to an embodiment of the present invention. Fig. 2 is a schematic diagram of an X-ray metrology system having multiple target materials according to an embodiment of the present invention. As shown in Fig. 1, a first embodiment of the present invention provides an X-ray metrology system including an X-ray source 1, a photodetector 2, and a processing device 3.

[0012] The X-ray source 1 generates multiple X-ray beams with different energies and irradiates the detection object DT with the beams. The X-ray source 1 may include an X-ray generator 10 and an X-ray lens group 20. In the X-ray source 1, an electron beam passes through different target materials to generate X-ray beams with different energies. As shown in FIG. 2, the X-ray generator 10 may include an electron beam generator 11 and a target material 12. The electron beam generator 11 irradiates different target materials with the electron beam to generate multiple X-ray beams with different wavelengths. The target materials are different materials such as copper, molybdenum, silver, and aluminum. The multiple X-ray beams with different wavelengths may include, for example, a first X-ray beam in an energy range of 90 eV to 94 eV, a second X-ray beam in an energy range of 1480 eV to 1490 eV, and a third X-ray beam in an energy range of 8040 eV to 8900 eV. The X-ray source 1 generates X-ray beams having different wavelengths and irradiates them onto the detection object DT. The detection object DT may have a multi-layer structure. For example, the detection object DT is an extreme ultraviolet (EUV) photomask having a multi-layer structure.

[0013] Furthermore, since X-ray beams with different energies have different attenuation lengths for different materials, the X-ray measurement system can detect the target object DT by adopting X-ray beams with different energy ranges for the materials used in the multi-layer structure of the target object DT. It should be noted that the attenuation length indicates the penetration depth that can be achieved when an element or compound is irradiated with X-rays having a specific energy and the energy is reduced to 1 / e (approximately 1 / 2.72 of the original intensity), and can be used to analyze the analytical sensitivity of X-rays at this specific energy.

[0014] In this embodiment, when the detection target DT is an EUV photomask, it may include, for example, one or more of an inter-diffusion layer, a reflective composite layer, a protective layer (capping layer, CL), and a low thermal expansion material layer (LTEM). For example, an X-ray beam in the energy range of 90 eV to 94 eV may be used to measure the inter-diffusion layer, an X-ray beam in the energy range of 1480 eV to 1490 eV may be used to measure the reflective composite layer and protective layer (capping layer, CL) inside the photomask, and an X-ray beam in the energy range of 8040 eV to 8900 eV may be used to measure the low thermal expansion material layer (LTEM). In this embodiment, the mutual expansion layer is an oxide layer generated during the formation of the photomask, which contains a large amount of light elements such as molybdenum disilicide (MoSi2), molybdenum trisilicide (Mo5Si3), and ruthenium oxide (RuO), and is suitable for measurement using X-rays in the low energy range of 90 eV to 94 eV.

[0015] On the other hand, the protective layer contains ruthenium and a ruthenium compound. The reflective composite layer has about 40 pairs of repeating layers and is formed by alternating material layers made of two or more types of metal elements. For example, the reflective composite layer of this embodiment contains about 40 pairs of molybdenum-silicon thin film pairs (Mo / Si) and is applicable to measurement using X-rays in the energy range of 1480 eV to 1490 eV.

[0016] The low thermal expansion material layer is typically located at the bottom of the EUV photomask and is a thick multilayer film containing molten silicon, fused silica, calcium fluoride, silicon carbide, silicon oxide-titanium oxide alloys and / or other suitable materials known in the art for measurement with X-rays in the energy range of 8040 eV to 8900 eV.

[0017] After irradiating the detection object DT with X-rays of different energies, the photodetector 2 may collect multiple measurement signals reflected from the detection object DT. The processing device 3 includes multiple measurement tools for analyzing the multiple measurement signals. Here, the multiple measurement signals include reflected light, scattered light, diffracted light, or fluorescence emitted by the detection object DT when excited by the X-rays. The measurement tools include an X-ray reflectivity (XRR) analyzer, an X-ray fluorescence spectrometer (XRF), a small-angle X-ray scattering (SAXS) analyzer, an X-ray diffractometer (XRD), or other devices capable of measurement using X-rays as a light source. The X-ray measurement system of the present invention may analyze the multiple measurement signals collected by the detection object DT. The measurement signals and measurement tools are not limited to the types described above. Specifically, when the measurement signal is reflected light, a reflected light receiver may be used as the receiver 2 of the X-ray measurement system, and an XRR analyzer may be used as the measurement tool to analyze the reflected light of the X-ray beam reflected by the detection object DT. Alternatively, when the measurement signal is fluorescence, when the detection object DT is irradiated with an X-ray beam, electrons in the inner orbits are excited by the X-rays, and the electrons in the higher energy levels transition to lower energy levels, filling the vacancies created by the excited electrons and releasing corresponding energy, i.e., corresponding fluorescence. A fluorescence receiver is used as the receiver 2, and an X-ray fluorescence spectrometer is used as the measurement tool. The collected fluorescence is analyzed to obtain a fluorescence spectrum. Alternatively, when the measurement signal is scattered light, a scattered light receiver is used as the receiver 2, and a SAXS analyzer is used as the measurement tool to perform small-angle X-ray scattering analysis. Alternatively, if the measurement signal is diffracted light, a diffracted light receiver is used as the light receiver 2, and an X-ray diffractometer is used as the measurement tool to collect and analyze the diffracted light generated by the detection target DT.

[0018] Figure 4 is a diagram of X-ray energy versus depth corresponding to an element or compound. As shown in Figure 4, each measurement signal is a reflection spectrum generated by irradiating the target object with each X-ray beam at multiple different incident angles and collected by the photodetector 2. The X-axis represents the X-ray energy, and the Y-axis represents the X-ray penetration depth. When the X-ray beam passes through the EUV mask and irradiates each layer structure, the X-ray energy is attenuated as it passes through different elements or compounds, i.e., it is reduced to approximately 0.367 times its original intensity. Since X-ray beams of different energy ranges have different optical sensitivities to material layers, the analytical sensitivity of the source energy can be analyzed.

[0019] The processing device 3 of the present invention is a general processor, a computer, a specific hardware device having a specific logic circuit, or a device having a specific function. The processing device 3 of this embodiment further includes an electromagnetic wave calculation engine. The electromagnetic wave calculation engine is an algorithm implemented in a computer-related hardware device. The electromagnetic wave calculation engine includes 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).

[0020] 3A and 3B are functional block diagrams of an X-ray measurement method according to an embodiment of the present invention. As shown in FIGS. 3A and 3B, the measurement method includes acquiring multiple structural parameters of a target structure TS using a multi-model calculation algorithm for data measured from X-ray beams of different energy bands. When the detection target DT is an EUV photomask having a multi-layer structure as described above, the target structure TS may include multiple material layers designed according to the structure of the detection target DT, and the multiple structural parameters may include, for example, the thickness, density, or roughness of each material layer.

[0021] The computational process performed by the processing unit 3 includes the following steps:

[0022] Step S1: Acquire a plurality of measurement signals using a plurality of measurement tools. Step S1 further includes steps S11 and S12.

[0023] Step S11: A plurality of X-ray beams having different energies are generated using a plurality of X-ray sources, and are irradiated onto the detection target DT.

[0024] Step S12: Using the photodetector 2, a plurality of measurement signals generated by the detection object DT reflecting the plurality of X-ray beams are collected.

[0025] Steps S11 and S12 will be described in more detail. The measurement signals in this embodiment include a first measurement signal SP1, a second measurement signal SP2, and a third measurement signal SP3. For example, the first measurement signal SP1 is obtained by measuring and calculating the photomask sample with a first measurement tool using a first X-ray beam in the energy range of 90 eV to 94 eV. The second measurement signal SP2 is obtained by measuring and calculating the photomask sample with a second measurement tool using a second X-ray beam in the energy range of 1480 eV to 1490 eV. The third measurement signal SP3 is obtained by measuring and calculating the photomask sample with a third measurement tool using a third X-ray beam in the energy range of 8040 eV to 8900 eV. In this embodiment, the number of measurement tools is not limited. It should be noted that the first, second, and third measurement tools used in this step each include the configuration of the X-ray source 1 and the photodetector 2 described above, but differ only in the X-ray energy used. The numbers of the first measurement tool, second measurement tool, and third measurement tool are merely examples, and each may be an X-ray reflectivity analyzer, an X-ray fluorescence spectrometer, a small-angle X-ray scattering analyzer, an X-ray diffractometer, or other devices capable of measurement using X-rays as a light source.

[0026] Figure 5 is a spectrum diagram of a first X-ray beam of an X-ray metrology system according to the present invention, Figure 6 is a spectrum diagram of a second X-ray beam of an X-ray metrology system according to the present invention, and Figure 7 is a spectrum diagram of a third X-ray beam of an X-ray metrology system according to the present invention.

[0027] As shown in Figures 5 to 7, measurement signals are obtained by measuring a photomask sample using X-ray beams of three different energies. In Figures 5 to 7, the X-axis represents the measurement angle in degrees. The Y-axis represents the intensity of the measurement signal collected from the X-rays in counts per second (CPS), which is the integrated value of the photons received by the photodetector per second. Figures 5 to 7 show measurement signals collected by measuring a photomask sample using X-ray beams of different energies. The measurement signals include first data SP1 collected by irradiating the first X-ray beam, second data SP2 collected by irradiating the second X-ray beam, and third data SP3 collected by irradiating the third X-ray beam. The processing device 3 performs calculations based on the characteristics of the measured spectral curves to obtain structural parameters of each material layer. These structural parameters include thickness, density, and roughness. The characteristics of the spectral curves include the slope of the curve or the spacing between specific peaks. The spectral curves shown in Figures 5 to 7 are different, indicating that the thickness, density and roughness of each material layer obtained by the X-ray beams in different energy ranges are also different.

[0028] Step S2: A plurality of fitting models are constructed based on the target structure of the object to be detected, and are calculated using an electromagnetic wave calculation engine corresponding to the measurement tool.

[0029] The processing device 3 constructs multiple fitting models FM based on the target structure TS of the detection object DT, and executes the electromagnetic wave calculation engine corresponding to each fitting model FM to perform a spectrum fitting analysis of the corresponding measurement signal based on the target structure TS and obtain a corresponding optimization target fitting result. For example, in this embodiment, the first parameter fitting model FM1 and the first electromagnetic wave calculation engine EM1 both correspond to the first measurement tool. The second parameter fitting model FM2 and the second electromagnetic wave calculation engine EM2 both correspond to the second measurement tool. The third parameter fitting model FM3 and the third electromagnetic wave calculation engine EM3 both correspond to the third measurement tool.

[0030] When constructing the model, a different calculation mode may be applied to each material layer depending on factors such as the importance of each material layer or changes in process conditions, to construct a fitting model. The processing device 3 executes an electromagnetic wave calculation engine corresponding to each fitting model to perform a spectral fitting analysis of the corresponding measurement signal based on the target structure TS and obtains the corresponding optimization target fitting result. The present invention includes multiple binding calculation modes. In the binding calculation mode, multiple material layers of the target structure TS are divided into one or more calculation groups, each calculation group being an independent calculation group or a binding calculation group, and the electromagnetic wave calculation engine performs a spectral fitting analysis of the measurement signal based on the one or more calculation groups.

[0031] Hereinafter, three aspects of the fitting model will be further described. FIG. 8 is a schematic diagram of the first fitting model of the X-ray measurement system according to the present invention. FIG. 9 is a schematic diagram of the second fitting model of the X-ray measurement system according to the present invention. FIG. 10 is a schematic diagram of the third fitting model of the X-ray measurement system according to the present invention. As shown in FIGS. 8 to 10, the X-ray measurement system of this embodiment includes a first fitting model, a second fitting model, and a third fitting model. As shown in FIG. 8, in the first fitting model, each layer of a plurality of material layers is divided into independent calculation groups. Each material layer in the first fitting model is calculated by an electromagnetic wave calculation engine, and it requires the most calculation time and calculation capacity among the three fitting model samples. As shown in FIG. 9, in the second fitting model, these material layers are divided into n independent calculation groups and N binding calculation groups, where n > N. As shown in FIG. 10, in the third fitting model, these material layers are divided into m independent calculation groups and M binding calculation groups, where m < M. Here, the n independent calculation groups and the m independent calculation groups each include at least four material layers M1, M2, M3, and M4. The first parameter fitting model FM1, the second parameter fitting model FM2, and the third parameter fitting model FM3 of the present invention can select fitting models in different forms according to whether the parameters of the material layer are important for the manufacturing process. Thereby, unnecessary calculations can be reduced, the calculation time can be shortened, and the measurement accuracy can be improved.

[0032] Step S3: Using a plurality of fitting models, perform spectral fitting analysis on each measurement signal to generate a plurality of optimization target fitting results.

[0033] The optimization target fitting result includes a plurality of first structural parameters for describing a plurality of material layers, and a plurality of first error values ​​(Calibration Optimization with Standard Technique, COST) COSTX1 to COSTX corresponding to the plurality of first structural parameters, respectively. n and multiple first variances Δp1 to Δp n Includes:

[0034] Specifically, the electromagnetic wave calculation engine stored in the processing device 3 performs a spectral fitting analysis on the corresponding measurement signal to generate an optimization target fitting result. As shown in Figures 3A and 3B, the processing device 3 constructs a first parameter fitting model FM1, and a first electromagnetic wave calculation engine EM1 corresponding to the first parameter fitting model FM1 performs a spectral fitting analysis on the first measurement signal SP1 to generate a first optimization target fitting result. A second electromagnetic wave calculation engine EM2 corresponding to the second parameter fitting model FM2 performs a spectral fitting analysis on the second measurement signal SP2 to generate a second optimization target fitting result. A third electromagnetic wave calculation engine EM3 corresponding to the third parameter fitting model FM3 performs a spectral fitting analysis on the third measurement signal SP3 to generate a third optimization target fitting result.

[0035] As described above, the present invention obtains measurement data from N measurement devices, constructs corresponding fitting models for each of the measurement data, and then performs fitting analysis on each of the measurement signals using N parameter fitting models. Then, through the corresponding N electromagnetic wave calculations, a plurality of first error values ​​COSTX1 to COSTX2, each corresponding to a plurality of first structural parameters, are obtained. n and a plurality of first distribution numbers Δp1 to Δp n The first error values ​​and the first variance numbers are the first optimization object fitting results, where the error values ​​may be optimization calibration values ​​according to standard techniques, and the present invention is not limited to the above example.

[0036] For example, the target structure TS has six material layers, including a first material layer ML1, a second material layer ML2, a third material layer ML3, a fourth material layer ML4, a fifth material layer ML5, and a sixth material layer ML6. When the first electromagnetic wave calculation engine EM1 corresponding to the first parameter fitting model FM1 performs a spectral fitting analysis on the first measurement signal SP1, the thickness values ​​of the material layers of the target structure TS are obtained as follows: ML1, ML2, ML3, ML4, ML5, ML6: 20, 20, 20, 20. When the second electromagnetic wave calculation engine EM2 corresponding to the second parameter fitting model FM2 performs a spectral fitting analysis on the second measurement signal SP2, the thickness values ​​of the material layers of the target structure TS are obtained as follows: ML1, ML2, ML3, ML4, ML5, ML6: 60, 40, 60, 40, 60, 40. Because the above data is subjected to statistical analysis as a numerical sequence, the thickness units are not specified. The two sequences of thicknesses of each material layer generated by the above-mentioned fitting analysis are the two fitting results of the optimization target. In this embodiment, the number of material layers is not limited to six, and the values ​​generated after performing the spectral fitting analysis are not limited to the thickness of each material layer.

[0037] Step S4: Collect statistics on a plurality of optimization target fitting results, generate a plurality of parameter fitting ranges, and generate a validation target parameter group based on the plurality of parameter fitting ranges.

[0038] Here, the verification target parameter group includes a plurality of second structure parameters describing each material layer, and the X-ray measurement method further includes generating the verification target parameter group using a random number scheme based on the parameter fitting ranges, inputting the verification target parameter group into a fitting model in an alternating combination to verify accuracy, and adjusting the verification target parameter group by alternating combination again based on the accuracy and the parameter fitting ranges.

[0039] Continuing with the example of step S3 above, the two thickness series of each material layer are statistically analyzed to generate a fitting range of thickness for each material layer, i.e., a parameter fitting range. For example, the first material layer ML1 has a thickness range of 20 to 60, the second material layer ML2 has a thickness range of 20 to 40, the third material layer ML3 has a thickness range of 20 to 60, the fourth material layer ML4 has a thickness range of 20 to 40, the fifth material layer ML5 has a thickness range of 20 to 60, and the sixth material layer ML6 has a thickness range of 20 to 40.

[0040] Next, a parameter group to be verified is generated based on these parameter fitting ranges. Different thickness arrangement combinations are generated using random numbers within the thickness fitting range of each material layer. For example, one parameter group to be verified includes two number sequences, and each value of each sequence is selected from the parameter fitting range of each material layer. Specifically, the first value of the first number sequence is generated randomly from the thickness range of the first material layer ML1. That is, the first value is generated randomly from the range of 20 to 60. The second value of the first number sequence is generated randomly from the thickness range of the second material layer ML2 (numeric range 20 to 40). In this way, the sixth value of the first number sequence is generated randomly from the thickness range of the sixth material layer ML6 (numeric range 20 to 40). Similarly, each value of the second number sequence is generated randomly from the thickness range of the first material layer ML1 to the sixth material layer ML6. The first number sequence and the second number sequence constitute one parameter group to be verified. In other words, one parameter group to be verified includes at least two sequences to be alternately combined, however, the number of sequences in the present invention is not limited to two.

[0041] Step S5: This parameter group to be verified is input to a plurality of fitting models, and their accuracy is verified.

[0042] The parameter groups to be verified are alternately combined and then input into these fitting models to verify their accuracy. For example, the parameter group to be verified includes a first number sequence and a second number sequence. A third number sequence and a fourth number sequence are obtained by randomly selecting N numbers from the first number sequence and replacing them with the corresponding numbers in the second number sequence. For example, the third number sequence and the fourth number sequence are obtained by randomly selecting numbers from the first material layer ML1 to the third material layer ML3 from the first number sequence and replacing them with the numbers from the first material layer ML1 to the third material layer ML3 in the second number sequence. That is, the third number sequence and the fourth number sequence are different from the first number sequence and the second number sequence. Next, the third number sequence and the fourth number sequence are input into these fitting models to verify their accuracy.

[0043] The step of verifying the accuracy of the parameter group to be verified includes inputting the parameter group to be verified into these fitting models, and generating a plurality of fitting results to be verified and a corresponding plurality of second error values ​​COSTX′1 to COSTX′. n and a plurality of second distribution numbers Δp'1 to Δp' n and generating these second error values ​​COSTX'1 to COSTX' n These first error values ​​COSTX1 to COSTX n and compare these second dispersion numbers Δp'1 to Δp' n These first distribution numbers Δp1 to Δp n and comparing the two, respectively.

[0044] Step S6: Based on the accuracy and the parameter fitting range, the parameter group to be verified is adjusted.

[0045] Based on the comparison result, it is determined whether the optimization condition is met. If the optimization condition is met, the parameter group to be verified can be the optimized fitting result. If the optimization condition is not met, the parameter group to be verified is adjusted after being alternately combined again based on the accuracy and the parameter fitting range.

[0046] If the optimization condition is satisfied, a plurality of second error values ​​COSTX'1 to COSTX' n are multiple first error values ​​COSTX1 to COSTX n These second dispersion numbers Δp'1 to Δp' are smaller than n These first dispersion numbers Δp1 to Δp n and , respectively, it is determined that the optimization conditions are satisfied.

[0047] If the optimization condition is not satisfied, a plurality of second error values ​​COSTX'1 to COSTX' n are multiple first error values ​​COSTX1 to COSTX n These second dispersion numbers Δp'1 to Δp' are larger than n These first dispersion numbers Δp1 to Δp n If it is determined that the optimization conditions are not met, the parameter group to be verified is adjusted based on these parameter fitting ranges, and the accuracy of the parameter group to be verified is determined after the adjustment. After obtaining a structural solution with a reduced number of variances, the optimized calibration values ​​(COSTX'1, COSTX'2, ... COSTX' n ) and fitting dispersion numbers (Δp'1, Δp'2...Δp' n ) was confirmed to be smaller than when no loop processing was performed.

[0048] [Beneficial Effects of Examples] One of the beneficial effects of the present invention is that the X-ray measurement system and X-ray measurement method provided by the present invention can reduce the standard deviation of the parameters of the optimized fitting results, i.e., reduce the error value, by using the following technical means: "irradiate the object to be detected with multiple X-ray beams having different energies, and use a photodetector to collect multiple measurement signals generated by the object to be detected when the object to be detected reflects the X-ray beams," "construct multiple fitting models based on the target structure of the object to be detected," and "use the multiple fitting models to perform spectral fitting analysis on the multiple measurement signals, respectively, generate parameters to be verified based on the fitting analysis results, verify the accuracy, make adjustments according to the accuracy, repeat the process until the optimization conditions are met, and obtain the optimized fitting results."

[0049] Furthermore, the X-ray measurement system and method provided by the present invention can select different fitting models in the model construction stage depending on whether the parameters of the material layer are important to the manufacturing process, thereby reducing unnecessary calculations, shortening calculation time, and improving measurement accuracy.

[0050] 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]

[0051] 1:X-ray source 10: X-ray generator 20: X-ray lens group 11: Electron beam generator 12: Target material 2:Receiver 3: Processing equipment S1~S6: Step M1, M2, M3, M4: Material layer

Claims

1. generating a plurality of X-ray beams having different energies using at least one X-ray source and irradiating the object to be detected; using a photodetector to collect a plurality of measurement signals generated by the object reflecting the plurality of X-ray beams; performing the steps of: Including, The steps include: constructing a plurality of fitting models based on the target structure of the object to be detected; performing a spectral fitting analysis on the measurement signals using the plurality of fitting models to generate a plurality of optimization target fitting results; collating a plurality of said optimization target fitting results to generate a plurality of parameter fitting ranges; generating a parameter group to be validated based on the plurality of parameter fitting ranges, inputting the parameter group to be validated into the plurality of fitting models, verifying the accuracy of the parameter group to be validated, adjusting the parameter group to be validated based on the accuracy and the plurality of parameter fitting ranges until an optimization condition is satisfied, and determining the parameter group to be validated that satisfies the optimization condition as an optimized fitting result; Including, An X-ray measurement method comprising:

2. Each of the measurement signals is a reflection spectrum generated by irradiating the detection object with each of the X-ray beams at a plurality of different incident angles and collected by the photodetector. The X-ray measurement method according to claim 1 .

3. the X-ray beams include a first X-ray beam in an energy range of 90 eV to 94 eV, a second X-ray beam in an energy range of 1480 eV to 1490 eV, and a third X-ray beam in an energy range of 8040 eV to 8900 eV; The X-ray measurement method according to claim 2 .

4. the target structure includes a plurality of material layers, and the processing device executes an electromagnetic wave calculation engine corresponding to each of the fitting models to perform a spectrum fitting analysis of the corresponding measurement signal based on the target structure, thereby obtaining the corresponding optimization target fitting result; The X-ray measurement method according to claim 2 .

5. The step of executing the electromagnetic wave calculation engine to perform a spectrum fitting analysis further includes: dividing the material layers of the target structure into one or more operation groups, each operation group being an independent operation group or a binding operation group; and the electromagnetic wave calculation engine performing a spectrum fitting analysis on the measurement signal based on the one or more operation groups. The X-ray measurement method according to claim 4.

6. In a first fitting model among the plurality of fitting models, all of the plurality of material layers are divided into independent operation groups; In a second fitting model among the plurality of fitting models, the plurality of material layers are divided into n independent operation groups and N binding operation groups, where n>N; In a third fitting model among the plurality of fitting models, the plurality of material layers are divided into m independent operation groups and M binding operation groups, where m<M; The X-ray measurement method according to claim 5 .

7. The n independent operation groups and the m independent operation groups each include at least four of the material layers; The X-ray measurement method according to claim 6.

8. the plurality of optimization target fitting results include a plurality of first structure parameters for describing the plurality of material layers, a plurality of first error values ​​and a plurality of first dispersion numbers respectively corresponding to the plurality of first structure parameters; The X-ray measurement method according to claim 6.

9. the verification target parameter group includes a plurality of second structural parameters for describing each of the material layers; The X-ray measurement method includes: generating the parameter group to be verified using a random number method based on the plurality of parameter fitting ranges; alternately combining the parameter groups to be verified and inputting them into the plurality of fitting models to verify the accuracy; and alternately combining the parameter groups to be verified again based on the accuracy and the plurality of parameter fitting ranges to adjust the parameter group to be verified. The X-ray measurement method according to claim 8.

10. The step of verifying the accuracy of the parameter group to be verified includes: inputting the parameter group to be verified into a plurality of the fitting models to generate a plurality of fitting results to be verified, a corresponding plurality of second error values, and a corresponding plurality of second variance numbers; comparing the second error values ​​to the first error values, respectively; comparing the second share numbers with the first share numbers, respectively; The X-ray measurement method according to claim 9.

11. determining that the optimization condition is satisfied when the second error values ​​are each smaller than the first error values ​​and the second distribution numbers are each smaller than the first distribution numbers; adjusting the parameter group to be validated based on the plurality of parameter fitting ranges in response to determining that the optimization condition is not satisfied, and determining the accuracy of the parameter group to be validated after the adjustment; The X-ray measurement method according to claim 10.

12. an X-ray source that generates a plurality of X-ray beams having different energies and irradiates the X-ray beams onto a detection target; a photoreceiver that collects a plurality of measurement signals generated by the object to be detected reflecting the plurality of X-ray beams; a processing unit configured to perform the steps of: Equipped with The steps include: constructing a plurality of fitting models based on the target structure of the object to be detected; performing a spectral fitting analysis on the measurement signals using the plurality of fitting models to generate a plurality of optimization target fitting results; collating a plurality of said optimization target fitting results to generate a plurality of parameter fitting ranges; generating a parameter group to be validated based on the plurality of parameter fitting ranges, inputting the parameter group to be validated into the plurality of fitting models, verifying the accuracy of the parameter group to be validated, adjusting the parameter group to be validated based on the accuracy and the plurality of parameter fitting ranges until an optimization condition is satisfied, and determining the parameter group to be validated that satisfies the optimization condition as an optimization fitting result; Including, An X-ray measurement system characterized by:

13. Each of the measurement signals is a reflection spectrum generated by irradiating the detection object with each of the X-ray beams at a plurality of different incident angles and collected by the photodetector. The X-ray measurement system according to claim 12.

14. the X-ray beams include a first X-ray beam in an energy range of 90 eV to 94 eV, a second X-ray beam in an energy range of 1480 eV to 1490 eV, and a third X-ray beam in an energy range of 8040 eV to 8900 eV; The X-ray measurement system according to claim 13.

15. the target structure includes a plurality of material layers, and the processing device executes an electromagnetic wave calculation engine corresponding to each of the fitting models to perform a spectrum fitting analysis of the corresponding measurement signal based on the target structure, thereby obtaining the corresponding optimization target fitting result; The X-ray measurement system according to claim 13.

16. The step of executing the electromagnetic wave calculation engine to perform a spectrum fitting analysis further includes: dividing the material layers of the target structure into one or more operation groups, each operation group being an independent operation group or a binding operation group; and the electromagnetic wave calculation engine performing a spectrum fitting analysis on the measurement signal based on the one or more operation groups. The X-ray measurement system according to claim 15.

17. In a first fitting model among the plurality of fitting models, all of the plurality of material layers are divided into independent operation groups; In a second fitting model among the plurality of fitting models, the plurality of material layers are divided into n independent operation groups and N binding operation groups, where n>N; In a third fitting model among the plurality of fitting models, the plurality of material layers are divided into m independent operation groups and M binding operation groups, where m<M; The X-ray measurement system according to claim 16.

18. The n independent operation groups and the m independent operation groups each include at least four of the material layers; 18. The X-ray measurement system according to claim 17.

19. the plurality of optimization target fitting results include a plurality of first structure parameters for describing the plurality of material layers, a plurality of first error values ​​and a plurality of first dispersion numbers respectively corresponding to the plurality of first structure parameters; 18. The X-ray measurement system according to claim 17.

20. the verification target parameter group includes a plurality of second structural parameters for describing each of the material layers; The processing device includes: The method is further configured to generate the parameter group to be verified in a random number manner based on the plurality of parameter fitting ranges, alternately combine the parameter groups to be verified and input them into the plurality of fitting models to verify the accuracy, and alternately combine them again based on the accuracy and the plurality of parameter fitting ranges to adjust the parameter group to be verified.

20. The X-ray measurement system according to claim 19.

21. The step of verifying the accuracy of the parameter group to be verified includes: inputting the parameter group to be verified into a plurality of the fitting models to generate a plurality of fitting results to be verified, a corresponding plurality of second error values, and a corresponding plurality of second variance numbers; comparing the second error values ​​to the first error values, respectively; comparing the second share numbers with the first share numbers, respectively; The X-ray measurement system according to claim 20.

22. determining that the optimization condition is satisfied when the second error values ​​are each smaller than the first error values ​​and the second distribution numbers are each smaller than the first distribution numbers; adjusting the parameter group to be validated based on the plurality of parameter fitting ranges in response to determining that the optimization condition is not satisfied, and determining the accuracy of the parameter group to be validated after the adjustment; 22. The X-ray metrology system according to claim 21.

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