Measurement equipment adjusting method and system based on RCWA algorithm optimization

Abstract

The invention provides a measurement equipment adjustment method and system based on RCWA algorithm optimization, and the method comprises the steps: obtaining a reference spectrum and a plurality of orders of electric fields based on a preset to-be-measured structure; obtaining an order importance sequence based on a plurality of orders of electric fields; obtaining an approximate spectrum based on the order importance ranking; based on the reference spectrum, adjusting an approximate spectrum order proportion to obtain an order selection result; and based on the order selection result and a preset RCWA algorithm, obtaining an optimized RCWA algorithm so as to adjust parameters of the measurement equipment. According to the method, order importance is considered, and Fourier expansion order selection in the RCWA algorithm is flexibly adjusted, so that equipment measurement parameters are adjusted, and the reliability of equipment measurement results is improved.

Description

Technical Field

[0001] The present invention relates to the field of electromagnetic simulation, and in particular to a measurement equipment adjustment method and system based on RCWA algorithm optimization. Background Art

[0002] The rigorous coupled wave analysis (RCWA) method is a numerical method in computational electromagnetics suitable for solving light wave scattering problems in periodic dielectric structures. Its main process is to represent the dielectric constant distribution and electromagnetic field as a superposition of spatial harmonics through Fourier expansion. The order of the Fourier expansion is used to capture the characteristics of the dielectric constant distribution. After integrating the RCWA algorithm into the measurement equipment, the measurement equipment can be adjusted.

[0003] Currently, existing technologies typically use fixed order selection or experience-based order selection. When faced with complex periodic dielectric structures, this method has a relatively simple order selection method and relatively limited order selection. In addition, its computational complexity is large and its adaptability is poor. Existing technologies also use methods that perform operations in compressed Fourier space, which increase or decrease the order through iterations. Each iteration requires a complete RCWA calculation. When measuring equipment faces complex periodic dielectric structures, the order selection is relatively limited, resulting in unreliable device measurement results. Summary of the Invention

[0004] To address the above issues, the present invention proposes a measurement equipment adjustment method and system based on RCWA algorithm optimization, which takes into account the importance of the order and flexibly adjusts the selection of the Fourier expansion order in the RCWA algorithm to adjust the device measurement parameters and improve the reliability of the device measurement results.

[0005] To achieve the above objectives, an embodiment of the present invention provides a measurement equipment adjustment method based on RCWA algorithm optimization, including: obtaining a reference spectrum and several orders of electric fields based on a preset structure to be measured; obtaining an order importance ranking based on the several orders of the electric fields; obtaining an approximate spectrum based on the order importance ranking; adjusting the approximate spectrum order ratio based on the reference spectrum to obtain an order selection result; and obtaining an optimized RCWA algorithm based on the order selection result and a preset RCWA algorithm to adjust the measurement equipment parameters.

[0006] An embodiment of the present invention proposes a measurement equipment adjustment method based on RCWA algorithm optimization. A preset structure to be measured is analyzed to obtain a reference spectrum and several order electric fields. The several order electric fields are ranked by importance, and an approximate spectrum of the preset structure to be measured is calculated based on the order importance ranking. An order selection result is then obtained by analyzing the reference spectrum and the approximate spectrum, thereby optimizing the RCWA algorithm to adjust the measurement equipment parameters. By considering the order importance of several order electric fields of the preset structure to be measured, an approximate spectrum of the preset structure to be measured is obtained. Combined with the reference spectrum of the preset structure to be measured, the order ratio of the approximate spectrum is adjusted to obtain an order selection result. The order selection of the RCWA algorithm is flexibly adjusted based on the order selection result, thereby optimizing the RCWA algorithm, adjusting the measurement equipment parameters, and improving the reliability of the equipment measurement results.

[0007] Furthermore, the method of obtaining a reference spectrum and several orders of electric fields based on a preset structure to be measured includes: calculating the overall spectrum of the preset structure to be measured based on the preset structure to be measured and a preset order ratio until the overall spectrum meets the preset accuracy requirements to obtain a reference spectrum; performing Fourier expansion on the dielectric constant of each layer of the preset structure to be measured to obtain several orders of electric displacement vectors; and obtaining several orders of electric fields based on the several orders of electric displacement vectors and a preset approximate relationship algorithm.

[0008] Through the above scheme, the overall spectrum of the preset structure to be measured is calculated at a preset order ratio so that the overall spectrum meets the preset accuracy requirements, and a reference spectrum is obtained. Then, the dielectric constant of each layer of the preset structure to be measured is Fourier expanded and analyzed. The Fourier expansion matrix can characterize the relationship between several orders of electric displacement vectors and several orders of electric fields. Then, according to a preset approximate relationship algorithm, the approximate relationship between several orders of electric displacement vectors and several orders of electric fields is obtained to obtain several orders of electric fields. Thus, by accurately obtaining the reference spectrum with the measurement structure, a reliable reference value is provided for the subsequent optimization of the RCWA algorithm. Then, the approximate relationship between several orders of electric displacement vectors and several orders of electric fields of the dielectric constant of each layer of the structure to be measured is obtained, providing an accurate data basis for the subsequent order importance ranking, thereby improving the reliability of the RCWA algorithm optimization, thereby improving the reliability of the measurement equipment parameter adjustment, and ultimately achieving the improvement of the reliability of the equipment measurement results.

[0009] Furthermore, the order importance ranking is obtained based on the electric fields of several orders, including: obtaining order importance results based on the electric fields of several orders and preset order electric fields; obtaining order importance ranking of each direction of incident light based on the order importance results and a preset incident light direction weighting algorithm; obtaining order importance ranking of each layer of structure based on the order importance results and a preset multi-layer structure weighting algorithm; obtaining order importance ranking of each wavelength based on the order importance results and a preset multi-wavelength weighting algorithm; obtaining order importance ranking based on the order importance ranking of each direction of the incident light, the order importance ranking of each layer of structure and the order importance ranking of each wavelength.

[0010] Through the above scheme, the order importance of several orders of electric fields is analyzed to determine the contribution of electric fields of different orders. The order importance is then ranked by considering the incident light direction, multi-layer structure and multi-wavelength factors to achieve dynamic evaluation of order importance. This provides a reliable data basis for the subsequent flexible adjustment of the Fourier expansion order selection in the RCWA algorithm, improves the reliability of RCWA algorithm optimization, and thus improves the reliability of measurement equipment parameter adjustment, ultimately achieving improved reliability of equipment measurement results.

[0011] Furthermore, based on the order importance result and the preset incident light direction weighting algorithm, the order importance ranking of each direction of the incident light is obtained, including: decomposing the order importance result into each direction of the incident light to obtain the order importance result of each direction; based on the preset incident light direction weighting algorithm, weighting the order importance result of each direction to obtain the order weighted importance result of each direction; based on the order weighted importance result of each direction, obtaining the order importance ranking of each direction of the incident light.

[0012] Through the above scheme, based on the order importance results, an incident light weighting mechanism is introduced to consider the order importance of the incident light in each direction and rank them, optimize the order selection in different directions, and realize dynamic evaluation of order importance. This provides a reliable data basis for the subsequent flexible adjustment of the Fourier expansion order selection in the RCWA algorithm, improves the reliability of RCWA algorithm optimization, and thus improves the reliability of measurement equipment parameter adjustment, ultimately achieving improved reliability of equipment measurement results.

[0013] Furthermore, based on the order importance results and the preset multi-layer structure weighting algorithm, the order importance ranking of each layer of the structure is obtained, including: based on the order importance results, obtaining the order importance results of each layer of the structure; based on the preset multi-layer structure weighting algorithm, weighting the order importance results of each layer of the structure to obtain the weighted importance results of the order of each layer of the structure; based on the weighted importance results of the order of each layer of the structure, obtaining the order importance ranking of each layer of the structure.

[0014] Through the above scheme, on the basis of the order importance results, a multi-layer structure weighting mechanism is introduced to consider the order importance of each layer in the multi-layer structure and rank them, optimize the order selection of different levels, and realize dynamic evaluation of order importance. This provides a reliable data basis for the subsequent flexible adjustment of the Fourier expansion order selection in the RCWA algorithm, improves the reliability of RCWA algorithm optimization, and thus improves the reliability of measurement equipment parameter adjustment, ultimately achieving improved reliability of equipment measurement results.

[0015] Furthermore, based on the order importance results and a preset multi-wavelength weighted algorithm, an order importance ranking of each wavelength is obtained, including: based on the order importance results, obtaining the order importance results of each wavelength; based on the preset multi-wavelength weighted algorithm, weighting the order importance results of each wavelength to obtain the weighted order importance results of each wavelength; based on the weighted order importance results of each wavelength, obtaining the order importance ranking of each wavelength.

[0016] Through the above scheme, based on the order importance results, a multi-wavelength weighting mechanism is introduced to consider the order importance of each wavelength and rank them, optimize the order selection at different wavelengths, and realize dynamic evaluation of order importance. This provides a reliable data basis for the subsequent flexible adjustment of the Fourier expansion order selection in the RCWA algorithm, improves the reliability of RCWA algorithm optimization, and thus improves the reliability of measurement equipment parameter adjustment, ultimately achieving improved equipment measurement results reliability.

[0017] Furthermore, the order importance sorting is based on the order importance to obtain an approximate spectrum, including: based on the order importance sorting, eliminating the order importance results that do not meet the preset contribution requirements in a preset proportion to obtain the remaining order importance results; based on the remaining order importance results, calculating the spectral data of the remaining orders to obtain the approximate spectrum.

[0018] Through the above scheme, the orders are sorted according to their importance, only the order importance results that meet the preset contribution requirements are retained, and the spectral data of the remaining orders are calculated to obtain an approximate spectrum. Only the orders with high contribution are retained, so that the approximate spectrum represents the set of high-contribution orders, and the orders that do not meet the contribution requirements are eliminated. This can reduce unnecessary calculations and provide a reliable data basis for the subsequent flexible adjustment of the Fourier expansion order selection in the RCWA algorithm. It can also improve the reliability of the RCWA algorithm optimization, thereby improving the reliability of the measurement equipment parameter adjustment, and ultimately achieving improved reliability of the equipment measurement results.

[0019] Furthermore, based on the reference spectrum, the approximate spectrum order ratio is adjusted to obtain an order selection result, including: calculating the spectrum root mean square error based on the reference spectrum and the approximate spectrum; if the spectrum root mean square error does not meet the preset error tolerance threshold, adjusting the approximate spectrum order ratio until it meets the preset error tolerance threshold to obtain the order selection result.

[0020] Through the above scheme, the root mean square error of the spectrum is calculated to analyze the error tolerance between the approximate spectrum and the reference spectrum, and the reliability of the order selection of the approximate spectrum is judged. After meeting the preset error tolerance threshold, the order selection of the approximate spectrum provides a reliable data basis for the subsequent flexible adjustment of the Fourier expansion order in the RCWA algorithm, thereby improving the reliability of the RCWA algorithm optimization, thereby improving the reliability of the measurement equipment parameter adjustment, and ultimately achieving improved reliability of the equipment measurement results.

[0021] Furthermore, based on the order selection result and the preset RCWA algorithm, an optimized RCWA algorithm is obtained to adjust the measurement equipment parameters, including: based on the order selection result, adjusting the Fourier expansion order selection parameters of the preset RCWA algorithm to obtain the optimized RCWA algorithm; based on the optimized RCWA algorithm, adjusting the measurement equipment parameters.

[0022] Through the above scheme, the Fourier expansion order selection parameters of the RCWA algorithm are adjusted by obtaining reliable order selection results, and the data processing process of the RCWA algorithm is optimized. In addition, the Fourier expansion order selection in the RCWA algorithm can be flexibly adjusted under different structures and working conditions to improve the reliability of the measurement equipment parameter adjustment, and ultimately improve the reliability of the equipment measurement results.

[0023] An embodiment of the present invention also provides a measurement equipment adjustment system based on RCWA algorithm optimization, including: a structure analysis module to be measured, an order importance ranking module, an approximate spectrum acquisition module, an order selection result acquisition module and a measurement equipment adjustment module; the structure analysis module to be measured is used to obtain a reference spectrum and several orders of electric fields based on a preset structure to be measured; the order importance ranking module is used to obtain an order importance ranking based on several orders of the electric fields; the approximate spectrum acquisition module is used to obtain an approximate spectrum based on the order importance ranking; the order selection result acquisition module is used to adjust the approximate spectrum order ratio based on the reference spectrum to obtain an order selection result; the measurement equipment adjustment module is used to obtain an optimized RCWA algorithm based on the order selection result to adjust the measurement equipment parameters.

[0024] An embodiment of the present invention provides a measurement equipment adjustment system based on RCWA algorithm optimization. A measured structure analysis module analyzes a preset measured structure to obtain a reference spectrum and several order electric fields. An order importance ranking module ranks the several order electric fields by importance. An approximate spectrum acquisition module calculates an approximate spectrum of the preset measured structure based on the order importance ranking. An order selection result acquisition module and a measurement equipment adjustment module then analyze the reference spectrum and the approximate spectrum to obtain an order selection result, thereby optimizing the RCWA algorithm to adjust measurement equipment parameters. By considering the order importance of several order electric fields of the preset measured structure, an approximate spectrum of the preset measured structure is obtained. Combined with the reference spectrum of the preset measured structure, the order ratio of the approximate spectrum is adjusted to obtain an order selection result. The order selection of the RCWA algorithm is flexibly adjusted based on the order selection result, thereby optimizing the RCWA algorithm, adjusting measurement equipment parameters, and improving the reliability of equipment measurement results. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic flow chart of a measurement equipment adjustment method based on RCWA algorithm optimization provided in one embodiment of the present invention;

[0026] Figure 2 A schematic diagram of a classic checkerboard double-periodic grating according to a measurement equipment adjustment method based on RCWA algorithm optimization provided in one embodiment of the present invention;

[0027] Figure 3 Schematic diagram of NCS spectra of different order ratios of a measurement equipment adjustment method based on RCWA algorithm optimization provided in one embodiment of the present invention Figure 1 ;

[0028] Figure 4 Schematic diagram of NCS spectra of different order ratios of a measurement equipment adjustment method based on RCWA algorithm optimization provided in one embodiment of the present invention Figure 2 ;

[0029] Figure 5 Schematic diagram of NCS spectra of different order ratios of a measurement equipment adjustment method based on RCWA algorithm optimization provided in one embodiment of the present invention Figure 3 ;

[0030] Figure 6 Schematic diagram of retaining different proportions of residual orders in a measurement equipment adjustment method based on RCWA algorithm optimization provided in one embodiment of the present invention Figure 1 ;

[0031] Figure 7 Schematic diagram of retaining different proportions of residual orders in a measurement equipment adjustment method based on RCWA algorithm optimization provided in one embodiment of the present invention Figure 2 ;

[0032] Figure 8 Schematic diagram of retaining different proportions of residual orders in a measurement equipment adjustment method based on RCWA algorithm optimization provided in one embodiment of the present invention Figure 3 ;

[0033] Figure 9 Schematic diagram of retaining different proportions of residual orders in a measurement equipment adjustment method based on RCWA algorithm optimization provided in one embodiment of the present invention Figure 4 ;

[0034] Figure 10 A schematic diagram comparing the calculation time before and after RCWA algorithm optimization of a measurement equipment adjustment method based on RCWA algorithm optimization provided by one embodiment of the present invention;

[0035] Figure 11 A schematic diagram of the module structure of a measurement equipment adjustment system based on RCWA algorithm optimization provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] Example 1

[0038] See also Figure 1 , Figure 1 A schematic flow chart of a measurement equipment adjustment method based on RCWA algorithm optimization is provided in one embodiment of the present invention. Figure 1 As shown, the embodiment of the present invention proposes a method including steps 101 to 105, each of which is specifically as follows:

[0039] Step 101, obtaining a reference spectrum and several orders of electric fields based on a preset structure to be measured;

[0040] Step 102, obtaining an order importance ranking based on the electric fields of several orders;

[0041] Step 103, obtaining an approximate spectrum based on the order importance ranking;

[0042] Step 104, adjusting the order ratio of the approximate spectrum based on the reference spectrum to obtain an order selection result;

[0043] Step 105 : Based on the order selection result and the preset RCWA algorithm, an optimized RCWA algorithm is obtained to adjust the measurement equipment parameters.

[0044] For a specific implementation method, see Figure 2 , Figure 2 A schematic diagram of a classic checkerboard double-periodic grating according to a measurement equipment adjustment method based on RCWA algorithm optimization provided in one embodiment of the present invention; Figure 2 As shown, for the preset structure to be measured, the following Figure 2 Taking the classic checkerboard double-period grating shown as an example, a sufficiently large order is selected, such as -10 to 10 in the x-direction and -10 to 10 in the y-direction, and the spectrum of the preset structure to be measured is calculated. Then, the dielectric constant of each layer of the preset structure to be measured is analyzed to obtain several orders of electric fields. By confirming the importance of each order of electric field, the electric fields are sorted according to their importance to obtain an order importance ranking. Based on the order importance ranking, a truncated approximate spectrum can be calculated. The spectral value of the reference spectrum is compared with the spectral value of the approximate spectrum, and the order ratio of the approximate spectrum is dynamically adjusted to obtain an order selection result. Finally, the order selection result is recorded in the preset RCWA algorithm, and the preset RCWA algorithm is optimized. The optimized RCWA algorithm is integrated into the measurement equipment, and the measurement equipment parameters are adjusted.

[0045] An embodiment of the present invention proposes a measurement equipment adjustment method based on RCWA algorithm optimization. A preset structure to be measured is analyzed to obtain a reference spectrum and several order electric fields. The several order electric fields are ranked by importance, and an approximate spectrum of the preset structure to be measured is calculated based on the order importance ranking. An order selection result is then obtained by analyzing the reference spectrum and the approximate spectrum, thereby optimizing the RCWA algorithm to adjust the measurement equipment parameters. By considering the order importance of several order electric fields of the preset structure to be measured, an approximate spectrum of the preset structure to be measured is obtained. Combined with the reference spectrum of the preset structure to be measured, the order ratio of the approximate spectrum is adjusted to obtain an order selection result. The order selection of the RCWA algorithm is flexibly adjusted based on the order selection result, thereby optimizing the RCWA algorithm, adjusting the measurement equipment parameters, and improving the reliability of the equipment measurement results.

[0046] As an example of this embodiment, executing step 101 includes: based on a preset structure to be measured and a preset order ratio, calculating the overall spectrum of the preset structure to be measured until the overall spectrum meets the preset accuracy requirements, thereby obtaining a reference spectrum; performing Fourier expansion on the dielectric constant of each layer of the preset structure to be measured to obtain electric displacement vectors of several orders; and obtaining electric fields of several orders based on the electric displacement vectors of several orders and a preset approximate relationship algorithm.

[0047] For a specific implementation method, see Figure 2 , Figure 2 A schematic diagram of a classic checkerboard double-periodic grating according to a measurement equipment adjustment method based on RCWA algorithm optimization provided in one embodiment of the present invention; Figure 2 As shown, for the preset structure to be measured, the following Figure 2 The classic double-periodic checkerboard grating shown in the figure is used as an example. A sufficiently large order is selected, such as -10 to 10 in the x-direction and -10 to 10 in the y-direction. The overall spectrum of the preset structure to be measured is calculated until the overall spectrum converges. At this point, the overall spectrum can represent the spectrum that meets the accuracy requirements, that is, the reference spectrum. For details, see Figure 3 、 Figure 4 and Figure 5 , Figure 3 Schematic diagram of NCS spectra of different order ratios of a measurement equipment adjustment method based on RCWA algorithm optimization provided in one embodiment of the present invention Figure 1 ; Figure 4 Schematic diagram of NCS spectra of different order ratios of a measurement equipment adjustment method based on RCWA algorithm optimization provided in one embodiment of the present invention Figure 2 ; Figure 5 Schematic diagram of NCS spectra of different order ratios of a measurement equipment adjustment method based on RCWA algorithm optimization provided in one embodiment of the present invention Figure 3 ;like Figure 3 、 Figure 4 and Figure 5 As shown in FIG, in different dimensions (NSpectrum, C Spectrum, and SSpectrum), the NCS spectrum when the order retention ratio is 100% is the benchmark spectrum; the benchmark spectrum data obtained by calculation is stored as:

[0048] R exact ={R exact (λ1), R exact (λ2),…,R exact (λ P )};

[0049] Where λ P are different incident light wavelengths (wavelength), P is the total number of wavelength sampling points;

[0050] Then, the dielectric constant of each layer of the structure to be measured is Fourier expanded to obtain several orders of electric displacement vectors, and then the approximate relationship algorithm is preset to obtain several orders of electric fields. A specific explanation is that in the RCWA algorithm, the Fourier expansion matrix of the dielectric constant of each layer can be used to describe the relationship between the electric displacement vector and the electric field. The specific formula is as follows:

[0051]

[0052] Assume that the expansion order in the x and y directions is from -M to M and -N to N respectively, D(m,n) represents the electric displacement vector of the (m,n)th order, E(j,k) represents the electric field of the (j,k)th order, and ε m-j,n-k is an element in the dielectric constant matrix;

[0053] In order to simplify the relationship between the electric displacement vector and the electric field, the next level of approximation can be used, as follows:

[0054]

[0055] Or the second-level approximate relationship is as follows:

[0056]

[0057] Among them, f and g are functions with elements in the dielectric constant matrix as independent variables; by approximating the electric displacement vector and the Fourier coefficient of the electric field of each layer, several orders of electric fields are obtained, which are recorded as E(m,n).

[0058] Through the above scheme, the overall spectrum of the preset structure to be measured is calculated at a preset order ratio so that the overall spectrum meets the preset accuracy requirements, and a reference spectrum is obtained. Then, the dielectric constant of each layer of the preset structure to be measured is Fourier expanded and analyzed. The Fourier expansion matrix can characterize the relationship between several orders of electric displacement vectors and several orders of electric fields. Then, according to a preset approximate relationship algorithm, the approximate relationship between several orders of electric displacement vectors and several orders of electric fields is obtained to obtain several orders of electric fields. Thus, by accurately obtaining the reference spectrum with the measurement structure, a reliable reference value is provided for the subsequent optimization of the RCWA algorithm. Then, the approximate relationship between several orders of electric displacement vectors and several orders of electric fields of the dielectric constant of each layer of the structure to be measured is obtained, providing an accurate data basis for the subsequent order importance ranking, thereby improving the reliability of the RCWA algorithm optimization, thereby improving the reliability of the measurement equipment parameter adjustment, and ultimately achieving the improvement of the reliability of the equipment measurement results.

[0059] As an example of an embodiment of the present invention, executing step 102 includes: obtaining an order importance result based on the electric field of several orders and a preset order electric field; obtaining an order importance ranking of each direction of the incident light based on the order importance result and a preset incident light direction weighting algorithm; obtaining an order importance ranking of each layer of structure based on the order importance result and a preset multi-layer structure weighting algorithm; obtaining an order importance ranking of each wavelength based on the order importance result and a preset multi-wavelength weighting algorithm; obtaining an order importance ranking based on the order importance ranking of each direction of the incident light, the order importance ranking of each layer of structure and the order importance ranking of each wavelength.

[0060] In a specific implementation method, in this embodiment, in order to evaluate the importance of the order, the order importance result can be obtained by analyzing the relationship between each order and the (0,0) order electric field (equivalent to the preset order electric field) and calculating their relative sizes. The specific formula is as follows:

[0061]

[0062] Where E(m,n) is the electric field of the (m,n)th order; E(0,0) is the electric field of the (0,0)th order; Importance(m,n) reflects the importance of this order to the calculation result.

[0063] Since the structure to be detected is usually relatively complex in structure, the order importance result obtained by simple calculation alone cannot accurately analyze the true order importance. For this reason, the embodiment of the present invention proposes to analyze the order importance in different situations from the perspective of the weighted incident light direction, multi-layer structure and multi-wavelength. Specifically, for the incident light direction, the direction of the incident light (expressed by angles θ, φ) also affects the choice of order. After decomposing the electric field into three directions (Ex, Ey, Ez), the order importance of each direction is weighted in combination with the distribution of the electric field decomposed by the incident light angle to obtain the order importance ranking of each direction of the incident light; for multi-layer structures, changes in the dielectric constant and geometric parameters of each layer will affect the electric field contribution of different orders. The importance of the overall order is calculated by assigning different weights to different layers. The weight assignment rule for each layer adopts two modes. The first is the default mode, which sets the weight of each layer and The second is the custom mode. The weight of each layer can be defined by the importance of the structural parameters of the layer. The appropriate weight distribution mode is selected according to the actual application situation to obtain the importance ranking of the structural order of each layer. For multiple wavelengths, since the short-wave light wavelength corresponds to a higher order, the order importance of the short-wave calculation results is given priority, and the order calculated by the short-wave is given a higher weight, while the long wave is given a lower weight. The weight is set according to the wavelength, and the order importance within the band is simulated to obtain the order importance ranking of each wavelength. Finally, by integrating the order importance ranking of each direction of the incident light, the order importance ranking of each layer of structure and the order importance ranking of each wavelength, all orders are sorted in descending order of Importance(m,n) to obtain the order importance list {(m1,n1),(m2,n2),...}, which is the overall preset order importance ranking of the structure to be tested.

[0064] Through the above scheme, the order importance of several orders of electric fields is analyzed to determine the contribution of electric fields of different orders. The order importance is then ranked by considering the incident light direction, multi-layer structure and multi-wavelength factors to achieve dynamic evaluation of order importance. This provides a reliable data basis for the subsequent flexible adjustment of the Fourier expansion order selection in the RCWA algorithm, improves the reliability of RCWA algorithm optimization, and thus improves the reliability of measurement equipment parameter adjustment, ultimately achieving improved reliability of equipment measurement results.

[0065] As an example of an embodiment of the present invention, based on the order importance result and a preset incident light direction weighting algorithm, an order importance ranking of each direction of the incident light is obtained, including: decomposing the order importance result into each direction of the incident light to obtain the order importance result of each direction; based on the preset incident light direction weighting algorithm, weighting the order importance result of each direction to obtain the order weighted importance result of each direction; based on the order weighted importance result of each direction, obtaining the order importance ranking of each direction of the incident light.

[0066] In a specific implementation method, the direction of the incident light (expressed as angles θ and φ) also affects the selection of the order. After decomposing the electric field into three directions (Ex, Ey, and Ez), the importance of the order in each direction is weighted based on the distribution of the electric field decomposed by the incident light angle. The specific calculation formula is as follows:

[0067] Ex=E0sinθcosφ;

[0068] Ey=E0sinθsinφ;

[0069] Ez=E0cosθ;

[0070] Among them, E0 is the total amplitude of the electric field of the incident light, the preset incident light direction weighting algorithm sinθcosφ is the weighting mechanism in the x direction, sinθsinφ is the weighting mechanism in the y direction, and cosθ is the weighting mechanism in the z direction. Finally, the weighted importance results of the orders in each direction are comprehensively considered to obtain the importance ranking of the orders in each direction of the incident light.

[0071] Through the above scheme, based on the order importance results, an incident light weighting mechanism is introduced to consider the order importance of the incident light in each direction and rank them, optimize the order selection in different directions, and realize dynamic evaluation of order importance. This provides a reliable data basis for the subsequent flexible adjustment of the Fourier expansion order selection in the RCWA algorithm, improves the reliability of RCWA algorithm optimization, and thus improves the reliability of measurement equipment parameter adjustment, ultimately achieving improved reliability of equipment measurement results.

[0072] As an example of an embodiment of the present invention, based on the order importance results and a preset multi-layer structure weighting algorithm, an order importance ranking of each layer of the structure is obtained, including: based on the order importance results, obtaining the order importance results of each layer of the structure; based on the preset multi-layer structure weighting algorithm, weighting the order importance results of each layer of the structure to obtain the weighted importance results of the order of each layer of the structure; based on the weighted importance results of the order of each layer of the structure, obtaining the order importance ranking of each layer of the structure.

[0073] A specific implementation method is that for multi-layer structures, the changes in the dielectric constant and geometric parameters of each layer will affect the electric field contributions of different orders. The importance of the overall order is calculated by assigning different weights to different layers. The weight assignment rule for each layer adopts two modes. The first is the default mode, which sets the weight of each layer to be proportional to the thickness of the layer. The second is the custom mode, which sets the weight of each layer to be defined by the importance of the structural parameters of the layer. According to the actual application situation, the appropriate weight assignment mode is selected to obtain the importance ranking of the structure order of each layer. Assuming that in a certain example, the preset structure to be tested is divided into K layers, the order importance result determined for each layer is calculated by step 101, and the order importance determined for the kth layer is recorded as Importance k (m,n)(k=1,2,3,…,K), the weight distribution rules in the default mode are as follows:

[0074]

[0075] Where, molecule d k is the physical thickness of the kth layer, and the denominator is the total thickness of all layers, which is used to normalize the weights (ensuring that the weights of each layer sum to 1);

[0076] In the custom mode, the weight of each layer can be defined by the importance of the structural parameters of that layer. For example, in a certain example, the structure to be predicted is preset to be a 3-layer structure, in which there are two parameters CD1 and CD2, which are two important parameters that need to be accurately simulated in the actual simulation process. They are located in the first and second layers respectively. Therefore, the order importance of these two layers should be relatively increased. You can customize w1=0.4, w2=0.4, and w3=0.2. After the weight of each layer is defined, the order importance of the entire structure (equivalent to the weighted importance of the order of each layer) can be determined by the following formula:

[0077]

[0078] In the formula, Importance total (m,n) is the order importance determined by the entire structure; finally, the order importance ranking of each layer of the structure is obtained according to the weighted importance results of the order of each layer of the structure.

[0079] Through the above scheme, on the basis of the order importance results, a multi-layer structure weighting mechanism is introduced to consider the order importance of each layer in the multi-layer structure and rank them, optimize the order selection of different levels, and realize dynamic evaluation of order importance. This provides a reliable data basis for the subsequent flexible adjustment of the Fourier expansion order selection in the RCWA algorithm, improves the reliability of RCWA algorithm optimization, and thus improves the reliability of measurement equipment parameter adjustment, ultimately achieving improved reliability of equipment measurement results.

[0080] As an example of an embodiment of the present invention, based on the order importance result and the preset multi-wavelength weighted algorithm, the order importance ranking of each wavelength is obtained, including: based on the order importance result, obtaining the order importance result of each wavelength; based on the preset multi-wavelength weighted algorithm, weighting the order importance result of each wavelength to obtain the weighted order importance result of each wavelength; based on the weighted order importance result of each wavelength, obtaining the order importance ranking of each wavelength.

[0081] A specific implementation method is that for multiple wavelengths, since short-wavelength light wavelengths correspond to higher orders, the order importance of the short-wave calculation results is given priority, and the order calculated by the short-wave is given a higher weight, while the long-wave is given a lower weight. The weight is set according to the wavelength, and the order importance within the band range is simulated to obtain the order importance ranking of each wavelength; assuming that the band range used by the structure to be tested is preset to λ in a certain embodiment min ~λ max , which contains λ1, λ2, ..., λ P There are P discrete wavelengths in total. Assume that a wavelength λ P The order importance calculated in step 101 is: p (m,n)(p=1,2,3,……,P), the weight of the wavelength is calculated using the following formula:

[0082]

[0083] After the weight of each wavelength is defined, the order importance of the kth layer within the simulation band (equivalent to the weighted importance of the order of each wavelength) is calculated as follows:

[0084]

[0085] Finally, the order of each wavelength is sorted according to its weighted importance to obtain the order of importance of each wavelength.

[0086] Through the above scheme, based on the order importance results, a multi-wavelength weighting mechanism is introduced to consider the order importance of each wavelength and rank them, optimize the order selection at different wavelengths, and realize dynamic evaluation of order importance. This provides a reliable data basis for the subsequent flexible adjustment of the Fourier expansion order selection in the RCWA algorithm, improves the reliability of RCWA algorithm optimization, and thus improves the reliability of measurement equipment parameter adjustment, ultimately achieving improved equipment measurement results reliability.

[0087] As an example of an embodiment of the present invention, executing step 103 includes: based on the order importance sorting, eliminating the order importance results whose preset proportion does not meet the preset contribution requirements to obtain the remaining order importance results; based on the remaining order importance results, calculating the spectral data of the remaining orders to obtain an approximate spectrum.

[0088] In a specific embodiment, the order importance ranking is obtained through step 102, and the order is gradually reduced and the approximate spectrum is calculated, for example, see Figure 6 、 Figure 7 、 Figure 8 and Figure 9 ,like Figure 6 Figure 7 、 Figure 8 and Figure 9 As shown in the figure, a comparison of the spectra calculated after retaining different proportions and the reference spectrum is shown, where the hollow points represent the reference spectrum order, and the solid points represent the approximate spectrum order. The order of the electric field of each order is sorted according to its order importance, and the orders with larger contributions of the preset proportion (such as 5%, 10% and 30%) are retained. The orders with smaller contributions are gradually removed. For example, 10% of the orders with the lowest contribution are removed each time, and the truncated approximate spectrum is calculated based on the remaining order importance results. The calculation formula is as follows:

[0089]

[0090] Where j represents the current degree of truncation; thus, the approximate spectrum can be calculated based on the orders retaining different proportions to provide a data basis for the subsequent order selection results.

[0091] Through the above scheme, the orders are sorted according to their importance, only the order importance results that meet the preset contribution requirements are retained, and the spectral data of the remaining orders are calculated to obtain an approximate spectrum. Only the orders with high contribution are retained, so that the approximate spectrum represents the set of high-contribution orders, and the orders that do not meet the contribution requirements are eliminated. This can reduce unnecessary calculations and provide a reliable data basis for the subsequent flexible adjustment of the Fourier expansion order selection in the RCWA algorithm. It can also improve the reliability of the RCWA algorithm optimization, thereby improving the reliability of the measurement equipment parameter adjustment, and ultimately achieving improved reliability of the equipment measurement results.

[0092] As an example of an embodiment of the present invention, executing step 104 includes: calculating the spectral root mean square error based on the reference spectrum and the approximate spectrum; if the spectral root mean square error does not meet the preset error tolerance threshold, adjusting the approximate spectrum order ratio until the preset error tolerance threshold is met, and obtaining an order selection result.

[0093] In a specific embodiment, the reference spectrum calculated in step 101 is recorded as R exact ={R exact (λ1), R exact (λ2),…,R exact (λ P )}, and the approximate spectrum calculated in step 103 is recorded as Calculate the spectrum root mean square error, the calculation formula is as follows:

[0094]

[0095] Where, is the spectrum value calculated after truncation (equivalent to the approximate spectrum); R exact (λ p ) is the spectrum value calculated by the full order (equivalent to the reference spectrum); P is the total number of wavelength sampling points;

[0096] See also Figure 3 、 Figure 4 and Figure 5 , Figure 3 Schematic diagram of NCS spectra of different order ratios of a measurement equipment adjustment method based on RCWA algorithm optimization provided in one embodiment of the present invention Figure 1 ; Figure 4 Schematic diagram of NCS spectra of different order ratios of a measurement equipment adjustment method based on RCWA algorithm optimization provided in one embodiment of the present invention Figure 2 ; Figure 5 Schematic diagram of NCS spectra of different order ratios of a measurement equipment adjustment method based on RCWA algorithm optimization provided in one embodiment of the present invention Figure 3 ;like Figure 3 、 Figure 4 and Figure 5As shown in the figure, the order is sorted according to the importance of each order of electric field, and the unimportant orders of the approximate spectrum are deleted. By setting the error tolerance (equivalent to the preset error tolerance threshold, such as residual = 0.005), the order is gradually reduced or increased until the root mean square error between the approximate spectrum and the reference spectrum is less than the set error tolerance. The proportion of batch deletion or increase is dynamically adjusted according to the size of the error. When the root mean square error is greater than the error tolerance, a certain order is added, and when the root mean square error is less than the error tolerance, a certain order is deleted. Specifically, the initial deletion ratio is set to 10 % (for example, 10% of low-contribution orders are deleted at one time). If the root mean square error exceeds the threshold (residual(j)>0.005), the deleted orders are rolled back and the deletion ratio is reduced (for example, adjusted to 5%). If the root mean square error is small (residual(j)<<0.005), the deletion ratio is increased (for example, adjusted to 20%). The optimal deletion ratio is found so that residual(j)≈0.005. The final order selection result is calculated. For example, after multiple rounds of adjustment, the optimal truncation order set O is determined. optimal , the final calculated spectrum R final satisfy: Explain that this optimal order set O optimal This is the final order selection result, which can both ensure calculation accuracy and minimize the amount of calculation. In a certain example, assuming that the initial Fourier expansion order range is: -20 to 20 in the x and y directions respectively, a total of 41×41=1681 orders; after sorting by importance, the orders are gradually deleted in the ratio of 10%, 20%, and 30%, and the approximate spectrum is calculated. When 50% of the orders are deleted, residual = 0.012, and the error exceeds the limit. At this time, it is adjusted back to 40%. When 40% of the orders are deleted, residual = 0.0048, which meets the error tolerance (residual(j)<<0.005). The optimization is stopped, and finally 60% of the important orders are retained to achieve calculation acceleration while ensuring that the error meets the requirements.

[0097] Through the above scheme, the root mean square error of the spectrum is calculated to analyze the error tolerance between the approximate spectrum and the reference spectrum, and the reliability of the order selection of the approximate spectrum is judged. After meeting the preset error tolerance threshold, the order selection of the approximate spectrum provides a reliable data basis for the subsequent flexible adjustment of the Fourier expansion order in the RCWA algorithm, thereby improving the reliability of the RCWA algorithm optimization, thereby improving the reliability of the measurement equipment parameter adjustment, and ultimately achieving improved reliability of the equipment measurement results.

[0098] As an example of an embodiment of the present invention, an optimized RCWA algorithm is obtained based on the order selection result and the preset RCWA algorithm to adjust the measurement equipment parameters, including: adjusting the Fourier expansion order selection parameters of the preset RCWA algorithm based on the order selection result to obtain the optimized RCWA algorithm; and adjusting the measurement equipment parameters based on the optimized RCWA algorithm.

[0099] A specific implementation method is to combine all the execution actions from step 101 to step 104 to obtain the order selection result and optimize the order selection of the preset RCWA algorithm. Figure 10 , Figure 10 A schematic diagram comparing the calculation time before and after RCWA algorithm optimization is provided for a measurement equipment adjustment method based on RCWA algorithm optimization according to one embodiment of the present invention. The calculation time of the optimized RCWA algorithm is accelerated from 49.7 seconds (order remaining 100%) to 0.005 seconds (order remaining 5%). Thus, the optimized RCWA algorithm is integrated into the measurement equipment to adjust the measurement equipment parameters. After adjusting the measurement equipment parameters using the optimized RCWA algorithm, a large number of simulated spectral databases can be generated in a short period of time during actual measurement and analysis. Due to the significant increase in data volume, the measurement results are more accurate and reliable. Furthermore, when periodic structure markers are required for measurement equipment parameter calibration, the significantly increased calculation speed of the analysis software can accelerate the entire calibration process, significantly shortening the routine maintenance time of the measurement equipment.

[0100] Through the above scheme, the Fourier expansion order selection parameters of the RCWA algorithm are adjusted by obtaining reliable order selection results, and the data processing process of the RCWA algorithm is optimized. In addition, the Fourier expansion order selection in the RCWA algorithm can be flexibly adjusted under different structures and working conditions to improve the reliability of the measurement equipment parameter adjustment, and ultimately improve the reliability of the equipment measurement results.

[0101] Example 2

[0102] See also Figure 11 , Figure 11 A schematic diagram of the module structure of a measurement equipment adjustment system based on RCWA algorithm optimization is provided in one embodiment of the present invention. Figure 11As shown, an embodiment of the present invention proposes a measurement equipment adjustment system based on RCWA algorithm optimization, including: a structure analysis module 201 to be measured, an order importance ranking module 202, an approximate spectrum acquisition module 203, an order selection result acquisition module 204 and a measurement equipment adjustment module 205; the structure analysis module 201 to be measured is used to obtain a reference spectrum and several orders of electric fields based on a preset structure to be measured; the order importance ranking module 202 is used to obtain an order importance ranking based on several orders of the electric fields; the approximate spectrum acquisition module 203 is used to obtain an approximate spectrum based on the order importance ranking; the order selection result acquisition module 204 is used to adjust the approximate spectrum order ratio based on the reference spectrum to obtain an order selection result; the measurement equipment adjustment module 205 is used to obtain an optimized RCWA algorithm based on the order selection result to adjust the measurement equipment parameters.

[0103] For a specific implementation method, see Figure 2 , Figure 2 A schematic diagram of a classic checkerboard double-periodic grating according to a measurement equipment adjustment method based on RCWA algorithm optimization provided in one embodiment of the present invention; Figure 2 As shown, for the preset structure to be measured, the following Figure 2 Taking the classical checkerboard double-periodic grating shown as an example, a sufficiently large order is selected, for example, -10 to 10 in the x-direction and -10 to 10 in the y-direction. The measured structure analysis module 201 calculates the spectrum of the preset measured structure, then analyzes the dielectric constant of each layer of the preset measured structure to obtain several order electric fields. The order importance ranking module 202 confirms the importance of each order electric field and ranks them according to their importance to obtain an order importance ranking. The approximate spectrum acquisition module 203 calculates a truncated approximate spectrum based on the order importance ranking. The order selection result acquisition module 204 compares the spectral value of the reference spectrum with the spectral value of the approximate spectrum, dynamically adjusts the order ratio of the approximate spectrum, and obtains an order selection result. Finally, the measurement equipment adjustment module 205 records the order selection result in the preset RCWA algorithm, optimizes the preset RCWA algorithm, integrates the optimized RCWA algorithm into the measurement equipment, and adjusts the measurement equipment parameters.

[0104] An embodiment of the present invention provides a measurement equipment adjustment system based on RCWA algorithm optimization. A measured structure analysis module analyzes a preset measured structure to obtain a reference spectrum and several order electric fields. An order importance ranking module ranks the several order electric fields by importance. An approximate spectrum acquisition module calculates an approximate spectrum of the preset measured structure based on the order importance ranking. An order selection result acquisition module and a measurement equipment adjustment module then analyze the reference spectrum and the approximate spectrum to obtain an order selection result, thereby optimizing the RCWA algorithm to adjust measurement equipment parameters. By considering the order importance of several order electric fields of the preset measured structure, an approximate spectrum of the preset measured structure is obtained. Combined with the reference spectrum of the preset measured structure, the order ratio of the approximate spectrum is adjusted to obtain an order selection result. The order selection of the RCWA algorithm is flexibly adjusted based on the order selection result, thereby optimizing the RCWA algorithm, adjusting measurement equipment parameters, and improving the reliability of equipment measurement results.

[0105] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

[0106] In the description of this specification, the reference terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" mean that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials, or characteristics described may be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless they are mutually inconsistent.

[0107] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, features specified as "first" or "second" may explicitly or implicitly include at least one of such features. In the description of this application, "plurality" means two or more, unless otherwise specifically defined.

Claims

1. A measurement equipment adjustment method based on RCWA algorithm optimization, characterized in that: include: Based on the preset structure to be measured, a reference spectrum and several orders of electric fields are obtained; Based on the electric fields of several orders, an order importance ranking is obtained; Based on the order importance ranking, an approximate spectrum is obtained; Based on the reference spectrum, adjusting the order ratio of the approximate spectrum to obtain an order selection result; Based on the order selection result and the preset RCWA algorithm, an optimized RCWA algorithm is obtained to adjust the measurement equipment parameters.

2. The measurement equipment adjustment method based on RCWA algorithm optimization according to claim 1, characterized in that: The method of obtaining a reference spectrum and several orders of electric fields based on a preset structure to be measured includes: Based on a preset structure to be measured and a preset order ratio, calculating an overall spectrum of the preset structure to be measured until the overall spectrum meets a preset accuracy requirement, thereby obtaining a reference spectrum; Performing Fourier expansion on the dielectric constant of each layer of the preset structure to be measured to obtain electric displacement vectors of several orders; Based on the electric displacement vectors of several orders and a preset approximate relationship algorithm, electric fields of several orders are obtained.

3. The measurement equipment adjustment method based on RCWA algorithm optimization according to claim 1, characterized in that: The electric field based on several orders is ranked by order importance, including: Obtaining order importance results based on the electric fields of several orders and the electric field of a preset order; Based on the order importance result and a preset incident light direction weighting algorithm, obtaining the order importance ranking of each direction of the incident light; Based on the order importance result and the preset multi-layer structure weighting algorithm, the order importance ranking of each layer structure is obtained; Based on the order importance result and a preset multi-wavelength weighting algorithm, obtaining the order importance ranking of each wavelength; The order importance ranking is obtained based on the order importance ranking of each direction of the incident light, the order importance ranking of each layer structure and the order importance ranking of each wavelength.

4. The measurement equipment adjustment method based on RCWA algorithm optimization according to claim 3, characterized in that: Based on the order importance result and the preset incident light direction weighting algorithm, the order importance ranking of each direction of the incident light is obtained, including: Decomposing the order importance result into each direction of the incident light to obtain the order importance result in each direction; Based on the preset incident light direction weighting algorithm, the order importance results of each direction are weighted to obtain the weighted order importance results of each direction; Based on the weighted importance results of the orders in each direction, the importance ranking of the orders in each direction of the incident light is obtained.

5. The measurement equipment adjustment method based on RCWA algorithm optimization according to claim 3, characterized in that: Based on the order importance results and the preset multi-layer structure weighting algorithm, the order importance ranking of each layer structure is obtained, including: Based on the order importance result, obtaining the order importance result of each layer structure; Based on a preset multi-layer structure weighting algorithm, weighting the importance results of the structure orders of each layer is performed to obtain the weighted importance results of the structure orders of each layer; Based on the weighted importance results of the structural orders of each layer, the importance ranking of the structural orders of each layer is obtained.

6. The measurement equipment adjustment method based on RCWA algorithm optimization according to claim 3, characterized in that: Based on the order importance results and the preset multi-wavelength weighting algorithm, the order importance ranking of each wavelength is obtained, including: Based on the order importance result, obtaining the order importance result of each wavelength; Based on the preset multi-wavelength weighted algorithm, the importance results of each wavelength order are weighted to obtain the weighted importance results of each wavelength order; Based on the weighted importance results of the wavelength orders, an importance ranking of the wavelength orders is obtained.

7. The measurement equipment adjustment method based on RCWA algorithm optimization according to claim 1, characterized in that: The step of obtaining an approximate spectrum based on the order importance sorting includes: Based on the order importance ranking, order importance results whose preset proportion does not meet the preset contribution requirement are eliminated to obtain the remaining order importance results; Based on the residual order importance result, the spectrum data of the residual order is calculated to obtain an approximate spectrum.

8. The measurement equipment adjustment method based on RCWA algorithm optimization according to claim 1, characterized in that: Based on the reference spectrum, the approximate spectrum order ratio is adjusted to obtain an order selection result, including: Calculating a spectrum root mean square error based on the reference spectrum and the approximate spectrum; If the spectrum root mean square error does not meet the preset error tolerance threshold, the approximate spectrum order ratio is adjusted until the preset error tolerance threshold is met to obtain an order selection result.

9. The measurement equipment adjustment method based on RCWA algorithm optimization according to claim 1, characterized in that: Based on the order selection result and the preset RCWA algorithm, an optimized RCWA algorithm is obtained to adjust the measurement equipment parameters, including: Based on the order selection result, adjusting the Fourier expansion order selection parameters of the preset RCWA algorithm to obtain an optimized RCWA algorithm; Based on the optimized RCWA algorithm, measurement equipment parameters are adjusted.

10. A measurement equipment adjustment system based on RCWA algorithm optimization, characterized in that: include: Module for analyzing the structure to be measured, module for sorting order importance, module for obtaining approximate spectrum, module for obtaining order selection results and module for adjusting measurement equipment; The structure analysis module to be measured is used to obtain a reference spectrum and several orders of electric fields based on a preset structure to be measured; The order importance ranking module is used to obtain order importance ranking based on the electric fields of several orders; The approximate spectrum acquisition module is used to obtain an approximate spectrum based on the order importance sorting; The order selection result acquisition module is used to adjust the approximate spectrum order ratio based on the reference spectrum to obtain an order selection result; The measurement equipment adjustment module is used to obtain an optimized RCWA algorithm based on the order selection result to adjust the measurement equipment parameters.

Images