A method for obtaining theoretical spectrum and a method for measuring morphological parameters

Abstract

The invention discloses a method for obtaining a theoretical spectrum and a method for measuring morphological parameters, which are used to improve the calculation efficiency of the theoretical spectrum and the measurement efficiency of the morphological parameters. The method for obtaining a theoretical spectrum includes: obtaining a morphological model of a sample to be measured, dividing the morphological model into M blocks, obtaining the initial morphological parameters of each block, floating the initial morphological parameters to obtain multiple groups of reference morphological parameters; determining the initial configuration and multiple reference configurations of each block, and the scattering characteristic matrix group of each block includes scattering characteristic matrices corresponding to the initial configuration and multiple reference configurations; from the bottom to the top of the morphological model, any scattering matrix selected from the scattering characteristic matrix group of each block is merged step by step to obtain the scattering characteristic matrix of the incident area of ​​the morphological model; obtaining multiple scattering characteristic matrices of the incident area; obtaining multiple reflected electric fields of the incident area, and obtaining multiple theoretical spectra of the sample to be measured based on the multiple reflected electric fields.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor packaging and testing technology, and in particular to a method for obtaining a theoretical spectrum and a method for measuring morphological parameters. Background Art

[0002] As semiconductor manufacturing develops towards precision and speed, chip foundries and device manufacturers are facing challenges in measurement technology. Optical Critical Dimension (OCD) technology estimates the specific morphological parameters of the structure by acquiring the scattering signal of the periodic structure of the specific measured area and the model of the structure. It can meet the needs of fast and accurate measurement of fine structures in new processes and new technologies. It is non-contact and non-destructive, can measure the morphological parameters of multiple processes at the same time, and realize online measurement. It has many advantages and is widely used in the semiconductor manufacturing industry and optical measurement.

[0003] During the OCD measurement process, it is necessary to establish a morphological model of the sample to be measured, and find a specific theoretical spectrum to achieve the best match with the measured spectrum to determine its morphological parameters. The current matching method based on the regression process is a method that uses optimal numerical technology to complete the matching problem. The basic idea of ​​the regression process is to give the nominal value of the morphological parameter as the initial value of the morphological parameter of the regression process based on the process information of the sample to be measured, and then search for a set of theoretical spectra corresponding to the morphological parameters to achieve the best match with the measured spectrum. During the search process, each iterative update requires multiple theoretical spectrum calculations, which is time-consuming, seriously affecting the regression efficiency, thereby reducing the measurement efficiency. Summary of the invention

[0004] The embodiment of the present invention provides a method for obtaining a theoretical spectrum and a method for measuring morphological parameters, which are used to improve the efficiency of calculating the theoretical spectrum and measuring the morphological parameters.

[0005] In a first aspect, the present invention provides a method for obtaining a theoretical spectrum, the method comprising: obtaining a morphology model of a sample to be measured, the morphology model comprising N initial morphology parameters; dividing the morphology model into M blocks, and obtaining the initial morphology parameters of each block; floating at least one initial morphology parameter of each block for multiple times to obtain multiple groups of reference morphology parameters of each block; determining an initial configuration of each block according to the initial morphology parameters of each block, and determining multiple reference configurations of each block according to the multiple groups of reference morphology parameters of each block, wherein the sum of the number of initial morphology parameters of the M blocks is equal to N; obtaining a scattering characteristic matrix group of each block, the scattering characteristic matrix group of each block comprising a scattering characteristic matrix corresponding to the initial configuration of each block and multiple reference configurations of each block, respectively. Corresponding scattering characteristic matrix; from the bottom to the top of the morphology model, any scattering matrix selected from the scattering characteristic matrix group of each block is merged step by step to obtain the scattering characteristic matrix of the top layer of the morphology model, and the scattering characteristic matrix of the incident area above the surface of the morphology model is iteratively calculated upward from the scattering characteristic matrix of the top layer of the morphology model to obtain the scattering characteristic matrix of the incident area above the surface of the morphology model; traverse each scattering characteristic matrix in the scattering characteristic matrix group of each block, and perform the step-by-step merging respectively to obtain multiple scattering characteristic matrices of the incident area above the surface of the morphology model; according to the multiple scattering characteristic matrices of the incident area, multiple reflected electric fields of the incident area above the surface of the morphology model are respectively obtained, and multiple theoretical spectra of the sample to be measured are obtained based on the multiple reflected electric fields; wherein M and N are positive integers greater than 1.

[0006] The beneficial effect of the method for obtaining theoretical spectrum provided by the present invention is that: different from the prior art, the prior art usually calculates the theoretical spectrum each time by iteratively calculating from the transmission zone layer by layer to obtain the scattering characteristic matrix propagating from the transmission layer to the incident zone, and then obtains the reflected electric field, and then obtains the theoretical spectrum, and each iterative update needs to perform multiple theoretical spectrum calculations, so it is very time-consuming; and the present invention calculates and saves the scattering characteristic matrix of each configuration of each block separately, and in the process of each iterative update of the morphological parameters of the regression process, the scattering characteristic matrix of each configuration of each block is merged in sequence from bottom to top of each block using the scattering characteristic matrix merging formula to obtain the scattering characteristic matrix of the top layer of the model, and then iterates upward to calculate the scattering characteristic matrix of the incident zone, and then obtains the reflected electric field, and then obtains multiple theoretical spectra. It can be seen that the present invention calculates the scattering characteristic matrix of each block and then merges it, because the scattering characteristic matrix merger is not time-consuming, and the total number of scattering characteristic matrices that need to be calculated for M blocks is less than that of the prior art, so the calculation time is saved, the regression efficiency is improved, and the spectrum calculation efficiency can be improved.

[0007] In a possible embodiment, each floating changes the value of an initial morphological parameter of each block.

[0008] In a possible embodiment, the step-by-step merging includes: selecting a scattering characteristic matrix corresponding to any one reference configuration from a scattering characteristic matrix group of at most one block.

[0009] In a possible embodiment, the method divides the topography model into M blocks, including:

[0010] S1, the N initial morphology parameters are used as a set of partitioned morphology parameters;

[0011] S2, changing the value of any one morphology parameter selected from the divided morphology parameter set, after changing the value of any one morphology parameter, taking the changed structure in the morphology model as a block, and obtaining the initial morphology parameter corresponding to the block;

[0012] S3, remove the initial morphology parameters corresponding to the block from the divided morphology parameter set. If the number of remaining morphology parameters in the divided morphology parameter set is not zero, update the divided morphology parameter set with the remaining morphology parameters, and repeat S2 until the number of remaining morphology parameters in the divided morphology parameter set is zero, and the morphology model division is completed.

[0013] In a possible embodiment, obtaining the scattering characteristic matrix group of each block includes:

[0014] For the i-th block, the i-th block is any one of the M blocks, and i is any integer in [1, M], and the following processing is performed:

[0015] Dividing the i-th block into a plurality of slices;

[0016] Starting from the bottom layer of the initial configuration of the i-th block, using the S matrix algorithm, calculate the scattering characteristic matrix of each layer and iterate the calculation layer by layer upward to the top layer of the initial configuration of the i-th block, to obtain the scattering characteristic matrix corresponding to the initial configuration of the i-th block;

[0017] For each reference configuration of the i-th block, starting from the bottom layer of each reference configuration, the S-matrix algorithm is used to calculate the scattering characteristic matrix of each layer and iteratively calculate upward layer by layer to the top layer of each reference configuration of the i-th block to obtain the scattering characteristic matrices corresponding to each reference configuration of the i-th block.

[0018] In a possible embodiment, the step-by-step merging satisfies the following formula:

[0019]

[0020]

[0021] in, is the scattering characteristic matrix corresponding to the jth block in the direction from the bottom to the top of the topography model, is the scattering characteristic matrix corresponding to the j+1th block in the direction from the bottom to the top of the topography model, The submatrix S is the matrix obtained by merging the scattering characteristic matrices corresponding to the 1st to j+1th blocks in the direction from the bottom to the top of the morphology model step by step. 12 is the reflection characteristic matrix, used to describe the reflection characteristics; submatrix S 11 and S 21 is a sub-block of the scattering characteristic matrix, used to describe the scattering characteristics; the sub-matrix S 22 is the transmission characteristic matrix, used to describe the transmission characteristics, j is an integer and j∈[1,M-1].

[0022] In a second aspect, the present invention provides a method for measuring morphological parameters, which comprises: S1, obtaining a measurement spectrum of a sample to be measured; S2, obtaining an initial morphological model of the sample to be measured, wherein the initial morphological model comprises N initial morphological parameters; S3, adopting the method for obtaining a theoretical spectrum as described in any one of the embodiments of the first aspect above, obtaining a plurality of theoretical spectra of the sample to be measured, wherein the plurality of theoretical spectra comprises an initial theoretical spectrum corresponding to the initial morphological model; S4, respectively obtaining a first fitting error of the initial theoretical spectrum and other theoretical spectra in the plurality of theoretical spectra excluding the initial theoretical spectrum, and obtaining a first fitting error based on the first fitting error. The step size of the floating morphology parameter and the gradient of the difference are used to update at least one of the initial morphology parameters to obtain the reference morphology model of the sample to be measured; S5, calculating the reference theoretical spectrum corresponding to the reference morphology model of the sample to be measured, and obtaining the second fitting error between the measured spectrum and the reference theoretical spectrum; setting a threshold to judge the second fitting error, if the second fitting error is not less than the threshold, based on the reference morphology model, repeating S3-S4 until the second fitting error is less than the threshold; S6, taking the value of the morphology parameter corresponding to the reference morphology model when the second fitting error is less than the threshold as the measured value of the morphology parameter of the sample to be measured.

[0023] The beneficial effect of the method for measuring the morphological parameters provided by the present invention is that: different from the prior art, the prior art usually calculates the theoretical spectrum each time by iteratively calculating from the transmission zone layer by layer to obtain the scattering characteristic matrix propagating from the transmission layer to the incident zone, and then obtains the reflected electric field, and then obtains the theoretical spectrum, and each iterative update requires multiple theoretical spectrum calculations to be performed, so it is very time-consuming; and the present invention calculates and saves the scattering characteristic matrices of each configuration of each block separately, and in the process of each iterative update of the morphological parameters of the regression process, the scattering characteristic matrices of each configuration of each block are merged in sequence from bottom to top in the Z direction of each block using the scattering characteristic matrix merging formula to obtain the scattering characteristic matrix of the top layer of the model, and then iteratively calculates the scattering characteristic matrix of the incident zone upward, and then obtains the reflected electric field, and then obtains multiple theoretical spectra. It can be seen that the present invention calculates the scattering characteristic matrix of each block and then merges it, because the scattering characteristic matrix merger is not time-consuming, and the total number of scattering characteristic matrices that need to be calculated for M blocks is less than that of the prior art, so the calculation time is saved, while improving the regression efficiency, the measurement efficiency of the morphological parameters is further improved.

[0024] In a possible embodiment, the first fitting error is at least one of a mean square error, a root mean square error, or a goodness of fit; and the second fitting error is at least one of a mean square error, a root mean square error, or a goodness of fit.

[0025] In another possible embodiment, the value of the updated initial morphology parameter is the sum of the product of the gradient of the first fitting error and the step length of the floating morphology parameter and the value of the initial morphology parameter before updating.

[0026] In other possible embodiments, the rigorous coupled wave analysis method is used in S5 to calculate the reference theoretical spectrum corresponding to the reference morphology model of the sample to be measured. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 and Figure 2 Schematic diagram of different morphological configurations;

[0029] Figure 3 and Figure 4 A layered schematic diagram of a morphology model provided in an embodiment of the present application;

[0030] Figure 5A schematic diagram of a method for obtaining a theoretical spectrum provided in an embodiment of the present application;

[0031] Figure 6 A schematic diagram of a block division method of a morphology model provided in an embodiment of the present application;

[0032] Figure 7 A schematic diagram of a configuration after morphological parameters are floated provided in an embodiment of the present application;

[0033] Figure 8 A schematic diagram of a layered approach for a morphology model provided in an embodiment of the present application;

[0034] Fig. 9 A schematic diagram of a method for measuring a morphology parameter provided in an embodiment of the present application;

[0035] Fig.10 A schematic diagram of a device for obtaining a theoretical spectrum provided in an embodiment of the present application;

[0036] Fig.11 A schematic diagram of a device for measuring morphological parameters provided in an embodiment of the present application;

[0037] Fig.12 A schematic diagram of an electronic device configuration provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] At present, optical critical dimension measurement technology is an important part of advanced process control in current semiconductor manufacturing process. It has the advantages of fast speed, low cost and non-destructiveness, and has important applications in advanced process control of semiconductor manufacturing. The optical critical dimension measurement technology can be generally summarized into two steps: the spectrum acquisition process, that is, obtaining the scattered signal of the sample and processing it into a measured spectrum; the spectrum matching process, that is, establishing the morphology model of the sample and finding a specific theoretical spectrum to achieve the best match with the measured spectrum to determine its morphology parameters. Among them, the spectrum matching process first establishes the morphology model of the periodic structure of the sample according to the process information of the sample. The morphology model includes a set of morphology parameters. The morphology model is an arbitrary two-dimensional or three-dimensional morphology structure. According to the light scattering principle of the periodic structure, the theoretical spectrum of the sample scattering corresponding to the morphology model determined by the specific morphology parameters can be calculated. Changing the morphology parameters will result in different theoretical spectra of the sample. The spectrum matching process is to find a set of morphology parameters, and the theoretical spectrum of the sample with the morphology parameters is closest to the measured spectrum. In this way, the actual morphology of the measured sample can be estimated by this set of best matching morphology parameters, thereby realizing the measurement of the morphology parameters of the sample.

[0039] At present, the corresponding theoretical spectrum has been calculated according to the morphology model, which can be realized by numerical calculation methods. The representative numerical method is: Rigorous Coupled-Wave Analysis (RCWA). In theory, the side wall angle of the sample morphology designed in the semiconductor manufacturing process is a right angle, such as Figure 1 As shown in the figure, the theoretical spectrum can be obtained based on RCWA without dividing the morphology model. However, due to the limitations of semiconductor processing technologies such as photolithography and etching, the actual morphology of the sample will have complex features such as domes, bottom feet, undercuts, or sidewall angles that do not change to 90 degrees, such as Figure 2 In this case, the topography model needs to be divided according to the requirements of the strict coupled wave analysis algorithm. The usual processing method is to divide the two-dimensional model into a series of rectangular slices or divide the three-dimensional model into a series of rectangular slices. The division results are shown in Figure 3 As shown. For RCWA modeling of complex morphology models, there is a problem of numerical instability when solving the reflection or transmission coefficient matrix. To ensure numerical stability, the scattering characteristic matrix algorithm is used. The scattering characteristic matrix is ​​used to describe the scattering characteristics of the material, represented by the symbol S, and for the convenience of description, it is also recorded as Submatrix S 12 is the reflection characteristic matrix, which can be used to describe the reflection characteristics; submatrix S 11 and S 21 is a sub-block of the scattering characteristic matrix, which can be used to describe the scattering characteristics; the sub-matrix S 22 is the transmission characteristic matrix, which can be used to describe the transmission characteristics. In optical measurement, the incident light illuminates the sample, generating reflected light and transmitted light. The area above the sample surface is called the incident area, and the area below the sample surface is called the transmission area, such as Figure 4 The steps of calculating the theoretical spectrum of the morphology model by the scattering characteristic matrix algorithm are as follows: starting from the transmission area, calculating the scattering characteristic matrix of each layer and iterating the calculation upward step by step, and finally obtaining the scattering characteristic matrix propagating from the transmission area to the incident area, and then using the scattering characteristic matrix of the incident area to calculate the reflection coefficient matrix and the theoretical spectrum of the morphology model.

[0040] The matching method based on the regression process in the prior art is a method that uses the optimization numerical technology to complete the matching problem. The basic idea of ​​the regression process is to give the nominal value of the morphology parameter as the initial value of the morphology parameter of the regression process according to the sample process information, and then search for a set of theoretical spectra corresponding to the morphology parameters and the measured spectrum to achieve the best match. In the search process, each iterative update needs to perform multiple theoretical spectrum calculations, so it is very time-consuming. The present invention proposes a method for improving the efficiency of theoretical spectrum calculation.

[0041] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention and to make the above-mentioned purposes, features and advantages of the embodiments of the present invention more obvious and easy to understand, the prior art solutions and the technical solutions in the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0042] like Figure 5 As shown, the present invention provides a method for obtaining a theoretical spectrum, the method comprising the following steps:

[0043] S501, obtaining a morphology model of a sample to be measured, dividing the morphology model into M blocks, and obtaining initial morphology parameters of each block.

[0044] The morphology model includes N initial morphology parameters, wherein the sum of the initial morphology parameters of M blocks is equal to N, and M and N are positive integers greater than 1.

[0045] In a possible implementation manner, in the above S501, dividing the morphology model into M blocks includes the following steps:

[0046] S1, taking the N initial morphology parameters as a partitioned morphology parameter set; S2, changing the value of any one morphology parameter selected from the partitioned morphology parameter set, after changing the value of any one morphology parameter, taking the changed structure in the morphology model as a block, and obtaining the initial morphology parameters corresponding to the block; S3, removing the initial morphology parameters corresponding to the block from the partitioned morphology parameter set, if the number of remaining morphology parameters in the partitioned morphology parameter set is not zero, updating the partitioned morphology parameter set with the remaining morphology parameters, and repeating S2 until the number of remaining morphology parameters in the partitioned morphology parameter set is zero, and the morphology model partitioning is completed.

[0047] For example, the topography model of the sample to be measured is as follows: Figure 6 As shown in FIG. 1 , the initial morphology parameters of the morphology model include (TCD1, BCD1, HT1, TCD2, BCD2, HT2). The morphology model is divided into two blocks, and (TCD1, BCD1, HT1, TCD2, BCD2, HT2) is used as a set of partitioned morphology parameters. The value of any selected morphology parameter in the partitioned morphology parameter set is changed. For example, see the attached Figure 7, float TCD1 to TCD1+ΔTCD1, and take the structure below that changes with the floating of TCD1 in the morphology model as a block A, and the initial morphology parameters of the block A below include (TCD1, BCD1, HT1); remove the initial morphology parameters of block A from the divided morphology parameter set (TCD1, BCD1, HT1, TCD2, BCD2, HT2) to obtain an updated divided morphology parameter set (TCD2, BCD2, HT2), and repeat the above dividing and blocking process to obtain the block B above, and the initial morphology parameters of block B include (TCD2, BCD2, HT2).

[0048] S502, floating at least one initial morphology parameter of each block for multiple times to obtain multiple groups of reference morphology parameters of each block, and determining the initial configuration of each block based on the initial morphology parameters of each block, and determining multiple reference configurations of each block based on the multiple groups of reference morphology parameters of each block.

[0049] Exemplarily, each floating changes the value of an initial morphological parameter of each block, for Figure 6 The initial morphological parameters of block A in include (TCD1, BCD1, HT1), TCD1 is floated to TCD1+ΔTCD1, and a set of reference morphological parameters (TCD1+ΔTCD1, BCD1, HT1) is obtained. The reference configuration corresponding to the set of reference morphological parameters (TCD1+ΔTCD1, BCD1, HT1) is as follows Figure 7 In addition, BCD1 and HT1 can be floated separately to obtain another two sets of reference morphological parameters (TCD1, BCD1+ΔBCD1, HT1) and (TCD1, BCD1, HT1+ΔHT1), as well as the reference configuration corresponding to the reference morphological parameters (TCD1, BCD1+ΔBCD1, HT1) and the reference configuration corresponding to the reference morphological parameters (TCD1, BCD1, HT1+ΔHT1).

[0050] Similarly, for Figure 6 The initial morphology parameters of block B at the top include (TCD2, BCD2, HT2). By floating TCD2, BCD2 and HT2 in turn, we can obtain three sets of reference morphology parameters (TCD2+ΔTCD2, BCD2, HT2), (TCD2, BCD2+ΔBCD2, HT2) and (TCD2, BCD2, HT2+ΔHT2), as well as the reference configurations corresponding to the three sets of reference morphology parameters.

[0051] S503, obtaining a scattering characteristic matrix group of each block, wherein the scattering characteristic matrix group of each block includes a scattering characteristic matrix corresponding to an initial configuration of each block and scattering characteristic matrices corresponding to a plurality of reference configurations of each block.

[0052] Combined with the above Figure 6 For example, the initial configuration of the block A located below corresponds to the morphology parameters (TCD1, BCD1, HT1). First, the scattering characteristic matrix of the initial configuration of block A is calculated. The reference configurations of block A after the morphology parameters are floated are (TCD1+△TCD1, BCD1, HT1), (TCD1, BCD1+△BCD1, HT1) and (TCD1, BCD1, HT1+△HT1). The scattering characteristic matrix of the reference configuration corresponding to the morphology parameters (TCD1+△TCD1, BCD1, HT1) is calculated, the scattering characteristic matrix of the reference configuration corresponding to the morphology parameters (TCD1, BCD1+△BCD1, HT1) is calculated, and the scattering characteristic matrix of the reference configuration corresponding to the morphology parameters (TCD1, BCD1, HT1+△HT1) is calculated. Finally, the scattering characteristic matrix group of block A is obtained. The scattering characteristic matrix group of block A includes 4 scattering characteristic matrices in total.

[0053] Similarly, the initial configuration of block B located below corresponds to the morphological parameters (TCD2, BCD2, HT2). First, the scattering characteristic matrix of the initial configuration of block B is calculated. The reference configurations of block B after the morphological parameters are floated are (TCD2+△TCD2, BCD2, HT2), (TCD2, BCD2+△BCD2, HT2) and (TCD2, BCD2, HT2+△HT2). The scattering characteristic matrix of the reference configuration corresponding to the morphological parameters (TCD2+△TCD2, BCD2, HT2) is calculated, the scattering characteristic matrix of the reference configuration corresponding to the morphological parameters (TCD2, BCD2+△BCD2, HT2) is calculated, and the scattering characteristic matrix of the reference configuration corresponding to the morphological parameters (TCD2, BCD2, HT2+△HT2) is calculated. Finally, the scattering characteristic matrix group of block B is obtained. The scattering characteristic matrix group of block B includes 4 scattering characteristic matrices in total.

[0054] In this embodiment, each initial morphology parameter corresponding to each block is floated respectively, so that the scattering characteristic matrix groups of blocks A and B each contain 4 scattering characteristic matrices. It can be understood that, according to actual needs, some of the initial morphology parameters corresponding to each block can also be floated. For example, the reference configurations of the morphology parameters of block A after floating are (TCD1+△TCD1, BCD1, HT1) and (TCD1, BCD1+△BCD1, HT1). Accordingly, the scattering characteristic matrix group of block A contains 3 scattering characteristic matrices.

[0055] S504, any scattering matrix selected from the scattering characteristic matrix group of each block is merged step by step from the bottom to the top of the morphology model to obtain the scattering characteristic matrix of the top layer of the morphology model, and the scattering characteristic matrix of the incident area above the surface of the morphology model is iteratively calculated upward from the scattering characteristic matrix of the top layer of the morphology model.

[0056] S505, traversing each scattering characteristic matrix in the scattering characteristic matrix group of each block, and performing the step-by-step merging respectively to obtain a plurality of scattering characteristic matrices of the incident area above the surface layer of the topography model.

[0057] S506, respectively acquiring a plurality of reflected electric fields of the incident area above the surface layer of the topography model according to a plurality of scattering characteristic matrices of the incident area, and obtaining a plurality of theoretical spectra of the sample to be measured based on the plurality of reflected electric fields.

[0058] In a possible embodiment, the step of calculating the scattering characteristic matrices corresponding to the initial configuration of each block and the multiple reference configurations in S503 includes:

[0059] For the i-th block, the i-th block is any one of the M blocks, and i is any integer in [1, M], and the following processing is performed:

[0060] The i-th block is divided into a plurality of slices; starting from the bottom slice of the initial configuration of the i-th block, the S matrix algorithm is used to calculate the scattering characteristic matrix of each slice and iteratively calculate upward layer by layer to the top slice of the initial configuration of the i-th block, so as to obtain the scattering characteristic matrix corresponding to the initial configuration of the i-th block;

[0061] For each reference configuration of the i-th block, starting from the bottom layer of each reference configuration, the S-matrix algorithm is used to calculate the scattering characteristic matrix of each layer and iteratively calculate upward layer by layer to the top layer of each reference configuration of the i-th block to obtain the scattering characteristic matrices corresponding to each reference configuration of the i-th block.

[0062] For example, it is possible to Figure 6 The block A and block B in the topography model shown are divided into 5 slices in the Z direction, such as Figure 8As shown, the calculation steps of the scattering characteristic matrix of each block are: in the Z direction, starting from the bottom layer of the initial configuration of block A, using the S matrix algorithm, calculate the scattering characteristic matrix of each layer and iterate the calculation upward layer by layer to the top layer of the initial configuration of block A, and finally obtain the scattering characteristic matrix corresponding to the initial configuration of block A. For any reference configuration of block A, starting from the bottom layer of the reference configuration, using the S matrix algorithm, calculate the scattering characteristic matrix of each layer and iterate the calculation upward layer by layer to the top layer of each reference configuration of block A, and obtain the scattering characteristic matrix corresponding to the reference configuration of block A. Similarly, this embodiment can also calculate the scattering characteristic matrix corresponding to the initial configuration of block B, and the scattering characteristic matrices corresponding to each reference configuration of block B.

[0063] Furthermore, the scattering characteristic matrix merging formula is used to merge the scattering characteristic matrices corresponding to all configurations of each block in the order from the bottom to the upper layer of each block in the Z direction step by step, so as to obtain multiple scattering characteristic matrices of the incident area above the surface layer of the morphology model, and multiple reflected electric fields of the incident area above the surface layer of the morphology model are respectively obtained according to the multiple scattering characteristic matrices of the incident area, and multiple theoretical spectra of the sample to be measured are obtained based on the multiple reflected electric fields. The step-by-step merging formula of the scattering characteristic matrix satisfies the following formula:

[0064]

[0065]

[0066] in, is the scattering characteristic matrix corresponding to the jth block in the direction from the bottom to the top of the topography model, is the scattering characteristic matrix corresponding to the j+1th block in the direction from the bottom to the top of the topography model, The submatrix S is the matrix obtained by merging the scattering characteristic matrices corresponding to the 1st to j+1th blocks in the direction from the bottom to the top of the morphology model step by step. 12 is the reflection characteristic matrix, used to describe the reflection characteristics; submatrix S 11 and S 21 is a sub-block of the scattering characteristic matrix, used to describe the scattering characteristics; the sub-matrix S 22 is the transmission characteristic matrix, used to describe the transmission characteristics, j is an integer and j∈[1,M-1].

[0067] The beneficial effect of the method for obtaining theoretical spectrum provided by the present invention is that: different from the prior art, the prior art usually calculates the theoretical spectrum each time by iteratively calculating from the transmission zone layer by layer to obtain the scattering characteristic matrix propagating from the transmission layer to the incident zone, and then obtains the reflected electric field, and then obtains the theoretical spectrum, and each iterative update needs to perform multiple theoretical spectrum calculations, so it is very time-consuming; and the present invention calculates and saves the scattering characteristic matrix of each configuration of each block separately, and in the process of each iterative update of the morphological parameters of the regression process, the scattering characteristic matrix of each configuration of each block is merged in sequence from bottom to top of each block using the scattering characteristic matrix merging formula to obtain the scattering characteristic matrix of the top layer of the model, and then iterates upward to calculate the scattering characteristic matrix of the incident zone, and then obtains the reflected electric field, and then obtains multiple theoretical spectra. It can be seen that the present invention calculates the scattering characteristic matrix of each block and then merges it, because the scattering characteristic matrix merger is not time-consuming, and the total number of scattering characteristic matrices that need to be calculated for M blocks is less than that of the prior art, so the calculation time is saved, the regression efficiency is improved, and the spectrum calculation efficiency can be improved.

[0068] Based on the above method for obtaining theoretical spectrum, the present invention also provides a method for measuring morphological parameters, such as Fig. 9 As shown, the method includes:

[0069] S901, obtaining a measurement spectrum of a sample to be measured.

[0070] S902, obtaining an initial morphology model of the sample to be measured, wherein the initial morphology model includes N initial morphology parameters.

[0071] S903, adopting the method for obtaining theoretical spectra in any of the above embodiments to obtain multiple theoretical spectra of the sample to be measured, wherein the multiple theoretical spectra include an initial theoretical spectrum corresponding to the initial morphology model.

[0072] S904, respectively obtaining first fitting errors of the initial theoretical spectrum and other theoretical spectra among the multiple theoretical spectra excluding the initial theoretical spectrum, and updating at least one of the initial morphological parameters based on the gradient of the first fitting error and the step size of the floating morphological parameter to obtain a reference morphological model of the sample to be measured.

[0073] S905, calculate the reference theoretical spectrum corresponding to the reference morphology model of the sample to be measured, and obtain a second fitting error between the measured spectrum and the reference theoretical spectrum; use the set threshold to judge the second fitting error, if the second fitting error is not less than the set threshold, based on the reference morphology model, repeat S903-S904 until the second fitting error is less than the threshold.

[0074] In a possible implementation manner, in S905 , a rigorous coupled wave analysis method is used to calculate a reference theoretical spectrum corresponding to a reference morphology model of the sample to be measured.

[0075] S906, taking the value of the morphology parameter corresponding to the reference morphology model when the second fitting error is less than the set threshold as the measured value of the morphology parameter of the sample to be measured.

[0076] It should be understood that in the above embodiment, the first fitting error is at least one of mean square error, root mean square error or goodness of fit; the second fitting error is at least one of mean square error, root mean square error or goodness of fit.

[0077] In a possible implementation manner, the value of the updated initial morphology parameter is the sum of the product of the gradient of the first fitting error and the step length of the floating morphology parameter and the value of the initial morphology parameter before updating.

[0078] Still Figure 6 Taking the morphology model as an example, the scattering characteristic matrix of the morphology parameters (TCD1+△TCD1, BCD1, HT1) corresponding to block A and the scattering characteristic matrix of the morphology parameters (TCD2, BCD2, HT2) corresponding to block B are merged using the scattering characteristic matrix merging formula to obtain the scattering characteristic matrix of the top layer of the model, and then the scattering characteristic matrix of the incident area above the surface of the morphology model is iteratively calculated upward to obtain the scattering characteristic matrix of the incident area, and then the reflected electric field of the incident area is obtained to obtain the theoretical spectrum of the first merger of the two blocks. The morphology parameters corresponding to the theoretical spectrum are (TCD1+△TCD1, BCD1, HT1, TCD2, BCD2, HT2).

[0079] Similarly, the scattering characteristic matrix of the morphological parameters (TCD1, BCD1+△BCD1, HT1) corresponding to block A and the scattering characteristic matrix of the morphological parameters (TCD2, BCD2, HT2) corresponding to block B are merged using the scattering characteristic matrix merging formula to obtain the scattering characteristic matrix of the top layer of the model, and then the scattering characteristic matrix of the incident area above the surface of the morphological model is iterated upward to obtain the scattering characteristic matrix of the incident area, and then the reflected electric field of the incident area is obtained to obtain the theoretical spectrum of the second merger of the two blocks. The morphological parameters corresponding to the theoretical spectrum are (TCD1, BCD1+△BCD1, HT1, TCD2, BCD2, HT2).

[0080] Similarly, the scattering characteristic matrix of the morphological parameters (TCD1, BCD1, HT1+△HT1) corresponding to block A and the scattering characteristic matrix of the morphological parameters (TCD2, BCD2, HT2) corresponding to block B are merged using the scattering characteristic matrix merging formula to obtain the scattering characteristic matrix of the top layer of the model, and then the scattering characteristic matrix of the incident area above the surface of the morphological model is iteratively calculated upward to obtain the scattering characteristic matrix of the incident area, and then the reflected electric field of the incident area is obtained to obtain the theoretical spectrum of the third merger of the two blocks. The morphological parameters corresponding to the theoretical spectrum are (TCD1, BCD1, HT1+△HT1, TCD2, BCD2, HT2).

[0081] Similarly, the scattering characteristic matrix of the morphological parameters (TCD1, BCD1, HT1) corresponding to block A and the scattering characteristic matrix of the morphological parameters (TCD2+△TCD2, BCD2, HT2) corresponding to block B are merged using the scattering characteristic matrix merging formula to obtain the scattering characteristic matrix of the top layer of the model, and then the scattering characteristic matrix of the incident area above the surface of the morphological model is iteratively calculated upward to obtain the scattering characteristic matrix of the incident area, and then the reflected electric field of the incident area is obtained to obtain the theoretical spectrum of the fourth merger of the two blocks. The morphological parameters corresponding to the theoretical spectrum are (TCD1, BCD1, HT1, TCD2+△TCD2, BCD2, HT2).

[0082] Similarly, the scattering characteristic matrix of the morphological parameters (TCD1, BCD1, HT1) corresponding to block A and the scattering characteristic matrix of the morphological parameters (TCD2, BCD2+△BCD2, HT2) corresponding to block B are merged using the scattering characteristic matrix merging formula to obtain the scattering characteristic matrix of the top layer of the model, and then the scattering characteristic matrix of the incident area above the surface of the morphological model is iteratively calculated upward to obtain the scattering characteristic matrix of the incident area, and then the reflected electric field of the incident area is obtained to obtain the theoretical spectrum of the fifth merger of the two blocks. The morphological parameters corresponding to the theoretical spectrum are (TCD1, BCD1, HT1, TCD2, BCD2+△BCD2, HT2).

[0083] Similarly, the scattering characteristic matrix of the morphological parameters (TCD1, BCD1, HT1) corresponding to block A and the scattering characteristic matrix of the morphological parameters (TCD2, BCD2, HT2+△HT2) corresponding to block B are merged using the scattering characteristic matrix merging formula to obtain the scattering characteristic matrix of the top layer of the model, and then the scattering characteristic matrix of the incident area above the surface of the morphological model is iteratively calculated upward to obtain the scattering characteristic matrix of the incident area, and then the reflected electric field of the incident area is obtained to obtain the theoretical spectrum of the sixth merger of the two blocks. The morphological parameters corresponding to the theoretical spectrum are (TCD1, BCD1, HT1, TCD2, BCD2, HT2+△HT2).

[0084] Similarly, the scattering characteristic matrix of the morphological parameters (TCD1, BCD1, HT1) corresponding to block A and the scattering characteristic matrix of the morphological parameters (TCD2, BCD2, HT2) corresponding to block B are merged using the scattering characteristic matrix merging formula to obtain the scattering characteristic matrix of the top layer of the model, and then the scattering characteristic matrix of the incident area above the surface of the morphological model is iteratively calculated upward to obtain the scattering characteristic matrix of the incident area, and then the reflected electric field of the incident area is obtained to obtain the theoretical spectrum of the seventh merger of the two blocks. The morphological parameters corresponding to the theoretical spectrum are (TCD1, BCD1, HT1, TCD2, BCD2, HT2).

[0085] In summary, for the morphology model with initial morphology parameters (TCD1, BCD1, HT1, TCD2, BCD2, HT2), because there are 6 morphology parameters, the theoretical spectrum corresponding to each morphology parameter is 6 theoretical spectra. Therefore, a total of 7 theoretical spectra need to be calculated. One theoretical spectrum corresponds to one set of morphology parameters, and 7 theoretical spectra correspond to 7 groups of morphology parameters. The corresponding relationship between 7 theoretical spectra and 7 groups of morphology parameters is as follows:

[0086] Morphological parameters Theoretical spectrum (TCD1, BCD1, HT1, TCD2, BCD2, HT2) The first theoretical spectrum (TCD1+△TCD1,BCD1,HT1,TCD2,BCD2,HT2) The second theoretical spectrum (TCD1,BCD1+△BCD1,HT1,TCD2,BCD2,HT2) The third theoretical spectrum (TCD1,BCD1,HT1+△HT1,TCD2,BCD2,HT2) The fourth theoretical spectrum (TCD1,BCD1,HT1,TCD2+△TCD2,BCD2,HT2) The fifth theoretical spectrum (TCD1,BCD1,HT1,TCD2,BCD2+△BCD2,HT2) The sixth theoretical spectrum (TCD1,BCD1,HT1,TCD2,BCD2,HT2+△HT2) The seventh theoretical spectrum

[0087] Still combining the above example for comparison, the prior art calculates the theoretical spectrum starting from the transmission area, calculates the scattering characteristic matrix of each layer and iterates upward layer by layer, and finally obtains the scattering characteristic matrix propagating from the transmission area to the incident area, and then uses the scattering characteristic matrix of the incident area to calculate the reflectance matrix and the theoretical spectrum of the morphology model. For 10 slices, the 7 theoretical spectra require a total of 7×10=70 iterations of scattering characteristic matrices.

[0088] The calculation method provided in this embodiment is used to calculate the scattering characteristic matrix of each block. One configuration of a block includes 5 slices and 3 morphology parameters. The initial configuration of a block is the initial morphology parameter updated in each iteration. Three configurations are obtained by floating each morphology parameter of each block. That is to say, one block needs to calculate 4 configurations. Therefore, a total of 5×4=20 scattering characteristic matrices are calculated for one block, and a total of 20+20=40 scattering characteristic matrices are calculated for two blocks. Moreover, the scattering characteristic matrices are merged 7 times to obtain 7 theoretical spectra. Considering that the merging is not time-consuming, and the scattering characteristic matrices of 40 times are much smaller than the scattering characteristic matrices of 70 times, the number of calculations is reduced, so the calculation time is shortened, and the efficiency of obtaining the theoretical spectrum is higher, thereby improving the regression efficiency and the morphology parameter measurement efficiency.

[0089] Based on the above method for obtaining theoretical spectrum, in some embodiments of the present application, the present application discloses a device for obtaining theoretical spectrum, such as Fig.10 As shown, the device 1000 is used to implement the methods recorded in the above method embodiments, which includes:

[0090] The acquisition unit 1001 is used to acquire a morphology model of a sample to be measured, wherein the morphology model includes N initial morphology parameters.

[0091] The division unit 1002 is used to divide the topography model into M blocks and obtain initial topography parameters of each block.

[0092] The reference configuration determination unit 1003 is used to float at least one initial morphology parameter of each block for multiple times to obtain multiple groups of reference morphology parameters of each block, determine the initial configuration of each block according to the initial morphology parameters of each block, and determine multiple reference configurations of each block according to the multiple groups of reference morphology parameters of each block, wherein the sum of the number of initial morphology parameters of the M blocks is equal to N.

[0093] The scattering characteristic matrix calculation unit 1004 is used to merge any scattering matrices selected from the scattering characteristic matrix group of each block in order from the bottom to the top of the morphology model step by step to obtain the scattering characteristic matrix of the top layer of the morphology model, and iteratively calculate the scattering characteristic matrix of the incident area above the surface of the morphology model upward.

[0094] The merging calculation unit 1005 is used to traverse each scattering characteristic matrix in the scattering characteristic matrix group of each block, and perform the step-by-step merging to obtain multiple scattering characteristic matrices of the incident area above the surface of the morphology model, and obtain multiple reflected electric fields of the incident area above the surface of the morphology model according to the multiple scattering characteristic matrices of the incident area, and obtain multiple theoretical spectra of the sample to be measured based on the multiple reflected electric fields.

[0095] All relevant contents of each step involved in the above method embodiment can be referred to the functional description of the corresponding functional module, and will not be repeated here.

[0096] Based on the above-mentioned morphology parameter measurement method, in some embodiments of the present application, the present application embodiment discloses a morphology parameter measurement device, such as Fig.11 As shown, the device 1100 is used to implement the methods recorded in the above method embodiments, which includes:

[0097] The acquisition unit 1101 is used to acquire a measurement spectrum of the sample to be measured; and to acquire an initial morphology model of the sample to be measured, wherein the initial morphology model includes N initial morphology parameters; and to acquire multiple theoretical spectra of the sample to be measured using the above-mentioned method for acquiring theoretical spectra, wherein the multiple theoretical spectra include an initial theoretical spectrum corresponding to the initial morphology model.

[0098] The calculation unit 1102 is used to respectively obtain the first fitting errors of the initial theoretical spectrum and other theoretical spectra in the multiple theoretical spectra except the initial theoretical spectrum, and update at least one of the initial morphology parameters based on the gradient of the first fitting error and the step size of the floating morphology parameter to obtain the reference morphology model of the sample to be measured; calculate the reference theoretical spectrum corresponding to the reference morphology model of the sample to be measured, and obtain the second fitting error between the measured spectrum and the reference theoretical spectrum; use a set threshold to judge the second fitting error, if the second fitting error is not less than the set threshold, based on the reference morphology model, repeat the above steps until the second fitting error is less than the set threshold.

[0099] The morphology parameter determining unit 1103 takes the value of the morphology parameter corresponding to the reference morphology model when the second fitting error is less than the set threshold as the measured value of the morphology parameter of the sample to be measured.

[0100] All relevant contents of each step involved in the above method embodiment can be referred to the functional description of the corresponding functional module, and will not be repeated here.

[0101] In other embodiments of the present application, the present application discloses an electronic device, such as Fig.12 As shown, the electronic device 1200 may include: one or more processors 1201; a memory 1202; a display 1203; one or more applications (not shown); and one or more computer programs 1204, and the above-mentioned components may be connected via one or more communication buses 1205. The one or more computer programs 1204 are stored in the above-mentioned memory 1202 and are configured to be executed by the one or more processors 1201, and the one or more computer programs 1204 include instructions. The one or more computer programs 1204 are stored in the above-mentioned memory 1202 and are configured to be executed by the one or more processors 1201, and the one or more computer programs 1204 include instructions, which can be used to execute as follows Figure 5 or Fig. 9 And each step in the corresponding embodiment.

[0102] The present application also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a computer, the method described in the above method embodiment is implemented. The specific effects can refer to the above embodiment.

[0103] The present application also provides a computer program product, which implements the method described in the above method embodiment when executed by a computer. The specific effects can refer to the above embodiment.

[0104] Through the description of the above implementation methods, technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal configuration of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0105] Each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0106] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as flash memory, mobile hard disk, read-only memory, random access memory, disk or optical disk.

[0107] The above is only a specific implementation of the embodiment of the present application, but the protection scope of the embodiment of the present application is not limited thereto, and any changes or replacements within the technical scope disclosed in the embodiment of the present application should be included in the protection scope of the embodiment of the present application. Therefore, the protection scope of the embodiment of the present application should be based on the protection scope of the claims.

Claims

1. A method for obtaining a theoretical spectrum, It is characterized in that The method includes: Acquire a morphology model of a sample to be measured, wherein the morphology model includes N initial morphology parameters; Dividing the topography model into M blocks, and obtaining initial topography parameters of each block; Floating at least one initial morphological parameter of each block for multiple times to obtain multiple groups of reference morphological parameters of each block; Determining an initial configuration of each block according to the initial morphological parameters of each block, and determining multiple reference configurations of each block according to the multiple groups of reference morphological parameters of each block, wherein the sum of the number of initial morphological parameters of the M blocks is equal to N; Acquire a scattering characteristic matrix group of each block, wherein the scattering characteristic matrix group of each block includes a scattering characteristic matrix corresponding to an initial configuration of each block and scattering characteristic matrices corresponding to a plurality of reference configurations of each block; From the bottom to the top of the topography model, any scattering matrix selected from the scattering characteristic matrix group of each block is merged step by step to obtain the scattering characteristic matrix of the top layer of the topography model, and the scattering characteristic matrix of the incident area above the surface layer of the topography model is iteratively calculated upward from the scattering characteristic matrix of the top layer of the topography model; Traversing each scattering characteristic matrix in the scattering characteristic matrix group of each block, respectively performing the step-by-step merging to obtain a plurality of scattering characteristic matrices of the incident area above the surface layer of the topography model; Acquire multiple reflected electric fields of the incident area above the surface layer of the topography model according to multiple scattering characteristic matrices of the incident area, and obtain multiple theoretical spectra of the sample to be measured based on the multiple reflected electric fields; Wherein, M and N are positive integers greater than 1.

2. The method according to claim 1, It is characterized in that Each floating changes the value of an initial morphological parameter of each block.

3. The method according to claim 1, It is characterized in that The step-by-step merging includes: selecting a scattering characteristic matrix corresponding to any one reference configuration from a scattering characteristic matrix group of at most one block.

4. The method according to claim 1, It is characterized in that The morphology model is divided into M blocks, including: S1, taking the N initial morphology parameters as a set of partitioned morphology parameters; S2, changing the value of any one morphology parameter selected from the divided morphology parameter set, after changing the value of any one morphology parameter, taking the changed structure in the morphology model as a block, and obtaining the initial morphology parameter corresponding to the block; S3, remove the initial morphology parameters corresponding to the block from the divided morphology parameter set. If the number of remaining morphology parameters in the divided morphology parameter set is not zero, update the divided morphology parameter set with the remaining morphology parameters, and repeat S2 until the number of remaining morphology parameters in the divided morphology parameter set is zero, and the morphology model division is completed.

5. The method according to claim 1, It is characterized in that The step of obtaining a scattering characteristic matrix group for each block includes: For the i-th block, the i-th block is any one of the M blocks, and i is any integer in [1, M], and the following processing is performed: Dividing the i-th block into a plurality of slices; Starting from the bottom layer of the initial configuration of the i-th block, using the S matrix algorithm, calculate the scattering characteristic matrix of each layer and iterate the calculation layer by layer upward to the top layer of the initial configuration of the i-th block, to obtain the scattering characteristic matrix corresponding to the initial configuration of the i-th block; For each reference configuration of the i-th block, starting from the bottom layer of each reference configuration, the S-matrix algorithm is used to calculate the scattering characteristic matrix of each layer and iteratively calculate upward layer by layer to the top layer of each reference configuration of the i-th block to obtain the scattering characteristic matrices corresponding to each reference configuration of the i-th block.

6. The method according to any one of claims 1 to 5, It is characterized in that The step-by-step merging satisfies the following formula: in, is the scattering characteristic matrix corresponding to the jth block in the direction from the bottom to the top of the topography model, is the scattering characteristic matrix corresponding to the j+1th block in the direction from the bottom to the top of the topography model, The submatrix S is the matrix obtained by merging the scattering characteristic matrices corresponding to the 1st to j+1th blocks in the direction from the bottom to the top of the morphology model step by step. 12 is the reflection characteristic matrix, used to describe the reflection characteristics; submatrix S 11 and S 21 is a sub-block of the scattering characteristic matrix, used to describe the scattering characteristics; the sub-matrix S 22 is the transmission characteristic matrix, used to describe the transmission characteristics, j is an integer and j∈[1,M-1].

7. A method for measuring morphological parameters, It is characterized in that The measuring method comprises: S1, obtaining a measurement spectrum of a sample to be measured; S2, obtaining an initial morphology model of the sample to be measured, wherein the initial morphology model includes N initial morphology parameters; S3, using the method for obtaining theoretical spectra according to any one of claims 1 to 6 to obtain a plurality of theoretical spectra of the sample to be measured, wherein the plurality of theoretical spectra include an initial theoretical spectrum corresponding to the initial morphology model; S4, respectively obtaining first fitting errors of the initial theoretical spectrum and other theoretical spectra in the plurality of theoretical spectra except the initial theoretical spectrum, and updating at least one of the initial morphological parameters based on the gradient of the first fitting error and the step size of the floating morphological parameter to obtain a reference morphological model of the sample to be measured; S5, calculating a reference theoretical spectrum corresponding to the reference morphology model of the sample to be measured, and obtaining a second fitting error between the measured spectrum and the reference theoretical spectrum; The second fitting error is determined by using a set threshold, and if the second fitting error is not less than the set threshold, S3-S4 are repeated based on the reference morphology model until the second fitting error is less than the set threshold; S6, taking the value of the morphology parameter corresponding to the reference morphology model when the second fitting error is less than the set threshold as the measured value of the morphology parameter of the sample to be measured.

8. The method according to claim 7, It is characterized in that The first fitting error is at least one of a mean square error, a root mean square error or a goodness of fit; the second fitting error is at least one of a mean square error, a root mean square error or a goodness of fit.

9. The method according to claim 7, It is characterized in that The value of the updated initial morphology parameter is the sum of the product of the gradient of the first fitting error and the step length of the floating morphology parameter and the value of the initial morphology parameter before updating.

10. The method according to claim 7, It is characterized in that In S5, a rigorous coupled wave analysis method is used to calculate a reference theoretical spectrum corresponding to the reference morphology model of the sample to be measured.

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