Topography parameter measurement method and system

Abstract

The present disclosure provides a topography parameter measurement method and system, wherein the topography parameter measurement method comprises: acquiring a measurement spectrum of a sample to be measured, the measurement spectrum comprising at least one spectral element and at least one wavelength corresponding to each spectral element, the sample to be measured having at least one topography parameter to be measured; acquiring the sensitivity of each spectral element to the topography parameter to be measured; acquiring a target weight corresponding to each spectral element according to the sensitivity; establishing a first matching function based on the target weight and the mean square error between a theoretical spectrum and the measurement spectrum; acquiring a theoretical spectrum with the highest matching degree with the measurement spectrum as a first matching spectrum based on the first matching function, and acquiring a first measurement vector based on the first matching spectrum, the first measurement vector comprising a first measurement value of each topography parameter to be measured. The technical solution provided by the present disclosure effectively improves the measurement accuracy of the topography parameter by introducing the sensitivity of each spectral element to the topography parameter to be measured.

Description

Technical Field

[0001] This disclosure relates to the field of optical measurement technology, and more specifically, to a method and system for measuring morphological parameters. Background Technology

[0002] As the semiconductor industry continues to advance towards nanotechnology nodes, the linewidth of integrated circuits is constantly shrinking, and the design of device structures is becoming increasingly complex. In order to ensure process yield, strict process control is required to obtain a fully functional circuit structure.

[0003] Optical critical-dimensional (OCD) measurement technology is a mainstream process control technology in current semiconductor manufacturing processes. It offers advantages such as high speed, low cost, and non-destructive operation, and can be used to measure the morphological parameters of samples. Specifically, Figure 1 A schematic diagram of a process for measuring the morphological parameters of a sample is shown, such as... Figure 1 As shown, the measurement method may include:

[0004] Step 101: Spectral acquisition process, including acquiring the optical scattering signal of the sample under test through an optical critical dimension measurement device and processing it into a measurement spectrum. The measurement spectrum includes, but is not limited to, reflectance spectra, polarization state change spectra, Fourier coefficient spectra from polarization state analysis, or spectra directly describing the scattering process. Spectra directly describing the scattering process include, for example, NCS spectra or Mueller spectra.

[0005] Step 102: The spectral matching process includes establishing a model of the sample to be tested and finding a specific theoretical spectrum to achieve the best match with the measured spectrum. Since the theoretical spectrum corresponds to known theoretical values ​​of morphological parameters, the theoretical values ​​of the morphological parameters corresponding to the matched theoretical spectrum can be used as the measured values ​​of the morphological parameters of the sample to be tested. The model includes the morphological model of the sample to be tested and information about the incident light, including the incident direction. The measured values ​​are represented by a morphological parameter vector, which includes the measured values ​​of each morphological parameter. Generally, the sample to be tested contains at least one morphological parameter. For example, if the morphological model of the sample to be tested is trapezoidal, its morphological parameters can be length, width, and height. The morphological parameters of interest to the user are the morphological parameters to be measured, which include one or at least two morphological parameters. For example, after measuring the morphological parameters to be measured, if the measured values ​​of the morphological parameters to be measured form a morphological parameter vector V, it can be expressed as V = (v1, ..., v1). K v1, ..., v K Here are the measured values ​​for each morphological parameter, where K > 1.

[0006] In existing technologies, matching the theoretical spectrum with the measured spectrum can be achieved by establishing a matching function. Typically, this matching function is characterized by the mean square error (MSE) of the two spectra. Since the MSE is always non-negative, usually positive but can also be zero, a smaller MSE value indicates a higher degree of matching between the theoretical and measured spectra. When the MSE value is minimized, the theoretical spectrum that best matches the measured spectrum is obtained. Specifically, the MSE of the theoretical and measured spectra can be expressed by the following formula:

[0007]

[0008] Where Ω is the mean square error between the theoretical and measured spectra, M is the number of spectral elements, N is the number of wavelengths corresponding to each spectral element, and λ j For the j-th wavelength, S i Let be the theoretical value of the i-th spectral element in the theoretical spectrum. To measure the value of the i-th spectral element in the spectrum, 1≤i≤M, 1≤j≤N.

[0009] However, with the increasing complexity of semiconductor device structure design, in certain scenarios, complex semiconductor manufacturing processes can lead to insufficient sensitivity of spectral measurements for morphological parameters, resulting in inaccurate measurements of morphological parameters obtained based on existing matching functions. Therefore, a new method for measuring morphological parameters is needed to improve the accuracy of the measurements. Summary of the Invention

[0010] To address the problems in the prior art, the purpose of this disclosure is to provide a method and system for measuring morphological parameters. By introducing the sensitivity of each spectral element to the morphological parameter to be measured, assigning a corresponding target weight to each spectral element, and then establishing a matching function, the matched theoretical spectrum becomes more accurate, and correspondingly, the obtained morphological parameters become more accurate.

[0011] Specifically, the first aspect of this disclosure provides a method for measuring morphological parameters, which may include the following steps:

[0012] The measurement spectrum of the sample to be tested is obtained. The measurement spectrum includes at least one spectral element and at least one wavelength corresponding to each spectral element. The sample to be tested has at least one morphological parameter to be measured.

[0013] Obtain the sensitivity of each spectral element to the morphology parameter to be measured, and obtain the target weight corresponding to each spectral element based on the sensitivity;

[0014] Based on the target weights, and according to the mean square error between the theoretical spectrum and the measured spectrum, a first matching function is established;

[0015] Based on the first matching function, the theoretical spectrum that matches the measured spectrum with the highest degree is obtained as the first matching spectrum, and the first measurement vector is obtained based on the first matching spectrum. The first measurement vector includes the first measurement value of each morphological parameter to be measured.

[0016] Through the above technical solution, the sensitivity of each spectral element to the topographic parameter is introduced during the establishment of the matching function, thereby reducing the measurement deviation of the topographic parameter caused by insufficient sensitivity of the measured spectrum to the topographic parameter due to complex semiconductor manufacturing processes.

[0017] In one possible implementation of the first aspect described above, obtaining the sensitivity of each spectral element to the morphological parameter to be measured includes the following steps:

[0018] Obtain a reference sample corresponding to the sample to be tested, obtain at least one reference spectrum and at least one reference morphology parameter vector corresponding to the reference sample at at least one reference point, and each reference morphology parameter vector includes a reference value for each morphology parameter to be tested;

[0019] Based on the mean square error between the theoretical spectrum and the reference spectrum, and the change in the weight of each spectral element, a second matching function is established for each spectral element.

[0020] Based on the second matching function, the theoretical spectrum that matches the reference spectrum the most is obtained as the second matching spectrum, and the second measurement vector is obtained based on the second matching spectrum, so as to obtain at least one second measurement vector with the same number as the reference spectrum, and each second measurement vector includes the second measurement value of each morphology parameter to be measured.

[0021] Based on the sum of squares of the differences between the reference value and the second measured value of each morphological parameter to be measured, and the change in weight, the sensitivity of each spectral element to the morphological parameter to be measured is obtained.

[0022] Furthermore, the expression for the second matching function can be as follows:

[0023]

[0024] Where, Δ i Let δ be the second matching function corresponding to the i-th spectral element. i Let δ be the change in weight of the i-th spectral element. i S is a positive value. i This is the second theoretical value corresponding to the i-th spectral element in the theoretical spectrum. Let λ be the second measured value of the i-th spectral element in the reference spectrum, 1≤i≤M, 1≤j≤N, where M is the number of spectral elements, N is the number of wavelengths, and λ is the second measured value of the i-th spectral element in the reference spectrum. j This represents the j-th wavelength.

[0025] Furthermore, the expression for sensitivity can be given as follows:

[0026]

[0027] Where B is the number of reference topographic parameter vectors, K is the number of topographic parameters to be measured, 1≤b≤B, 1≤k≤K, v b，k，ref v is the reference value of the k-th topographic parameter to be measured in the b-th reference topographic parameter vector. b，k，mea Let δ be the second measured value of the k-th topographic parameter in the b-th second measurement vector, where 0 < δ i ≤1.

[0028] The above technical solution provides a specific method for obtaining the sensitivity of each spectral element to the morphological parameter to be measured: obtaining a reference spectrum and a reference value of the morphological parameter to be measured based on a reference sample; performing spectral matching based on the reference spectrum to obtain a second measured value of the morphological parameter to be measured; and obtaining the sensitivity of the spectral element to the morphological parameter to be measured based on the reference value and the second measured value of the morphological parameter to be measured and the change in weight.

[0029] In one possible implementation of the first aspect above, a second matching function is established for each spectral element based on the mean square error between the theoretical spectrum and the reference spectrum, as well as the change in the weight of each spectral element, including the following steps:

[0030] Based on the mean square error between the theoretical spectrum and the reference spectrum, the change in the weight of each spectral element, and the preset weights of all spectral elements, a second matching function is established for each spectral element.

[0031] Furthermore, the expression for the second matching function can be as follows:

[0032]

[0033] Where, Δ i ' is the second matching function corresponding to the i-th spectral element, δ i Let δ be the change in weight of the i-th spectral element. i For positive values, t i The preset weights corresponding to the i-th spectral element, t i S is a non-negative number. i This is the second theoretical value corresponding to the i-th spectral element in the theoretical spectrum. Let λ be the second measured value of the i-th spectral element in the reference spectrum, 1≤i≤M, 1≤j≤N, where M is the number of spectral elements, N is the number of wavelengths, and λ is the second measured value of the i-th spectral element in the reference spectrum. j This represents the j-th wavelength.

[0034] Through the above technical solution, a preset weight is further introduced in the process of establishing the second matching function, and the value of the preset weight can be set as needed.

[0035] In one possible implementation of the first aspect described above, the expression for sensitivity can be as follows:

[0036]

[0037] Where B is the number of reference topographic parameter vectors, K is the number of topographic parameters to be measured, 1≤b≤B, 1≤k≤K, v b，k，ref v is the reference value of the k-th topographic parameter to be measured in the b-th reference topographic parameter vector. b，k，mea Let δ be the second measured value of the k-th topographic parameter in the b-th second measurement vector, where 0 < δ i ≤1.

[0038] In one possible implementation of the first aspect above, obtaining the target weight corresponding to each spectral element based on sensitivity includes the following steps:

[0039] The sensitivity of each acquired spectral element to the morphological parameter to be measured is used as the target weight for each spectral element.

[0040] Alternatively, the sensitivity of each acquired spectral element to the measured morphology parameter can be normalized, and the result of the normalization can be used as the target weight.

[0041] The normalization expression is as follows:

[0042]

[0043] Among them, w′ i Let w be the sensitivity of the i-th spectral element to the morphological parameter to be measured. i Let be the target weight corresponding to the i-th spectral element, and M be the number of spectral elements, where 1 ≤ i ≤ M.

[0044] Through the above technical solution, the target weight corresponding to each spectral element is limited to the range of 0 to 1 by normalization processing, thus avoiding the problem that the target weight values ​​of each spectral element may differ too much.

[0045] In one possible implementation of the first aspect above, the measurement spectrum of the sample to be measured and the reference spectrum of the reference sample are acquired using the same optical critical dimension measuring device;

[0046] The weight of each spectral element changes by the same amount;

[0047] Based on the second matching function and regression analysis algorithm or library matching algorithm, the theoretical spectrum that matches the reference spectrum the most is obtained as the second matching spectrum.

[0048] By setting the weight change of each spectral element to be the same through the above technical solution, the setting of the weight change can be simplified, and the sensitivity of each spectral element to the morphological parameter to be measured will not change due to the change in the weight.

[0049] In one possible implementation of the first aspect above, the theoretical spectrum that best matches the measured spectrum is obtained as the first matching spectrum based on the first matching function and the regression analysis algorithm or the library matching algorithm.

[0050] The expression for the first matching function is as follows:

[0051]

[0052] Where M is the number of spectral elements, N is the number of wavelengths, and w i Let λ be the target weight corresponding to the i-th spectral element. j For the j-th wavelength, S i This is the first theoretical value corresponding to the i-th spectral element in the theoretical spectrum. The first measurement is for the i-th spectral element in the spectrum, where 1 ≤ i ≤ M and 1 ≤ j ≤ N.

[0053] The second aspect of this disclosure provides a morphology parameter measurement system for implementing the morphology parameter measurement method provided in the first aspect, specifically including:

[0054] The measurement spectrum acquisition unit is used to acquire the measurement spectrum of the sample to be tested. The measurement spectrum includes at least one spectral element and at least one wavelength corresponding to each spectral element. The sample to be tested has at least one morphological parameter to be measured.

[0055] The sensitivity acquisition unit is used to acquire the sensitivity of each spectral element to the morphological parameter to be measured, and to acquire the target weight corresponding to each spectral element based on the sensitivity.

[0056] The matching function acquisition unit is used to establish a first matching function based on the target weight and the mean square error between the theoretical spectrum and the measured spectrum;

[0057] The parameter measurement unit is used to obtain the theoretical spectrum that matches the measured spectrum the most based on the first matching function as the first matching spectrum, and to obtain the first measurement vector based on the first matching spectrum. The first measurement vector includes the first measurement value of each morphological parameter to be measured.

[0058] Compared with the prior art, this disclosure has the following beneficial effects:

[0059] The technical solution provided in this disclosure overcomes the problem that complex semiconductor manufacturing processes can lead to insufficient sensitivity of the measured spectrum to morphological parameters, thus affecting the accuracy of morphological parameter measurements. It introduces a sensitivity assessment for each spectral element relative to the morphological parameter to be measured, assigns a target weight to each spectral element based on this sensitivity, and establishes a first matching function. Spectral matching is then performed based on this first matching function and the measured spectrum, resulting in a more accurate theoretical spectrum and consequently, more accurate morphological parameters. The technical solution provided in this disclosure improves the accuracy of morphological parameter measurements and has widespread applicability. Attached Figure Description

[0060] Other features, objects, and advantages of this disclosure will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0061] Figure 1 Based on existing technology, a schematic flowchart is provided for measuring the morphological parameters of a sample to be tested.

[0062] Figure 2 This is a flowchart illustrating a method for measuring morphological parameters according to an embodiment of the present disclosure.

[0063] Figure 3 According to embodiments of this disclosure, a flowchart is provided for obtaining the sensitivity of each spectral element to the morphological parameter to be measured.

[0064] Figure 4 According to an embodiment of this disclosure, a schematic flowchart is provided for obtaining the theoretical spectrum that best matches the measured spectrum as the first matching spectrum.

[0065] Figure 5 According to an embodiment of this disclosure, another schematic diagram of a process for obtaining the theoretical spectrum that best matches the measured spectrum as the first matching spectrum is provided.

[0066] Figure 6 According to an embodiment of this disclosure, a structural schematic diagram of a morphology parameter measurement system is provided. Detailed Implementation

[0067] The following specific examples illustrate the implementation of this disclosure. Those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed herein. This disclosure can also be implemented or applied to systems through other different specific embodiments, and various details in this disclosure can also be modified or changed according to different viewpoints and application systems without departing from the spirit of this disclosure. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this disclosure can be combined with each other.

[0068] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings, so that those skilled in the art to which this disclosure pertains can readily implement it. This disclosure may be embodied in many different forms and is not limited to the embodiments described herein.

[0069] In this disclosure, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic represented in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. Furthermore, the specific features, structures, materials, or characteristics represented may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples represented in this disclosure, as well as the features of those different embodiments or examples.

[0070] Furthermore, the terms "first" and "second" are used for illustrative purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the representation of this disclosure, "a plurality of" means two or more, unless otherwise expressly and specifically defined.

[0071] For the purpose of clarity, devices unrelated to the description are omitted, and the same or similar components throughout the specification are given the same reference numerals.

[0072] Throughout this specification, when it is said that a device is "connected" to another device, this includes not only "direct connection" but also "indirect connection" by placing other components in between. Furthermore, when it is said that a device "comprises" a certain constituent element, unless otherwise stated otherwise, this does not exclude other constituent elements, but rather implies that other constituent elements may be included.

[0073] When we say that a device is "above" another device, this can mean that it is directly above the other device, or it can mean that other devices are present in between. Conversely, when we say that a device is "directly" "above" another device, there are no other devices present in between.

[0074] Although the terms first, second, etc., are used in some instances herein to refer to various elements, these elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, first interface and second interface, etc., are used. Furthermore, as used herein, the singular forms “a,” “an,” and “the” are intended to also include the plural forms unless the context indicates otherwise. It should be further understood that the terms “comprising,” “including,” indicate the presence of features, steps, operations, elements, components, items, kinds, and / or groups, but do not exclude the presence, occurrence, or addition of one or more other features, steps, operations, elements, components, items, kinds, and / or groups. The terms “or” and “and / or” as used herein are interpreted as inclusive, or mean any one or any combination thereof. Thus, “A, B, or C” or “A, B, and / or C” means “any one of: A; B; C; A and B; A and C; B and C; A, B, and C.” Exceptions to this definition will only occur if the combination of elements, functions, steps, or operations is inherently mutually exclusive in some way.

[0075] The technical terms used herein are for reference only to specific embodiments and are not intended to limit the scope of this disclosure. The singular form used herein includes the plural form unless the statement explicitly indicates otherwise. The word "comprising" as used in this specification means to specify a particular characteristic, region, integer, step, operation, element, and / or component, and does not exclude the presence or addition of other characteristics, regions, integers, steps, operations, elements, and / or components.

[0076] Although not explicitly defined, all terms, including technical and scientific terms used herein, shall have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. Terms defined in commonly used dictionaries shall be further interpreted as having a meaning consistent with the relevant technical literature and the content of this present disclosure, and shall not be over-interpreted as having an ideal or overly formulaic meaning unless otherwise defined.

[0077] According to existing technical explanations and descriptions, in certain scenarios, complex semiconductor manufacturing processes can lead to insufficient sensitivity of the measured spectrum for morphological parameters, resulting in inaccurate measurement results (i.e., measured values) of morphological parameters obtained based on existing matching functions. It is understandable that, since the measured spectrum is obtained by acquiring and processing the optical scattering signal of the sample under test using optical critical dimension measuring equipment, it is difficult to directly determine which spectral element in the measured spectrum has insufficient sensitivity and make adaptive adjustments when inaccurate measurement results occur.

[0078] To overcome the problems encountered in the application of the existing technology, the technical solution provided in this disclosure offers a method and system for measuring morphological parameters. By introducing the sensitivity of each spectral element to the morphological parameter to be measured, assigning corresponding target weights to each spectral element, and then establishing a first matching function, the measured values ​​of the morphological parameters can be obtained more accurately. The specific implementation of this morphological parameter measurement method will be explained in detail below:

[0079] Specifically, in some embodiments provided in this disclosure, Figure 2 A flowchart illustrating a method for measuring topographic parameters is provided. Figure 2 As shown, the specific steps may include the following:

[0080] Step 201: Obtain the measurement spectrum of the sample to be tested. The measurement spectrum includes at least one spectral element and at least one wavelength corresponding to each spectral element. The sample to be tested has at least one topographic parameter to be measured. Specifically, the measurement spectrum includes, but is not limited to, reflectance spectrum, polarization state change spectrum, Fourier coefficient spectrum of polarization state analysis, or spectrum directly describing the scattering process. Spectra directly describing the scattering process are, for example, NCS spectrum or Mueller spectrum. The topographic parameter of interest to the user is the topographic parameter to be measured. The topographic parameter to be measured includes one or at least two topographic parameters. Any topographic parameter can be a critical dimension such as linewidth, or a non-critical dimension such as sidewall angle. Those skilled in the art can select the topographic parameter to be measured according to actual needs, and no limitation is made here.

[0081] In one specific implementation of the above embodiments, the spectral type of the measured spectrum is related to the selected optical critical dimension measuring device. For example, the optical critical dimension measuring device can be a single-rotation ellipsometer for measuring NCS spectra, or a double-rotation ellipsometer for measuring Mueller spectra. For NCS spectra, it contains three spectral elements N, C, and S; for Mueller spectra, it contains 16 spectral elements S1 to S2. 16 Those skilled in the art can select appropriate optical critical dimension measuring equipment and corresponding spectral types for measurement based on actual needs, without any limitations here.

[0082] Step 202: Obtain the sensitivity of each spectral element to the morphological parameter to be measured, and obtain the target weight corresponding to each spectral element based on the sensitivity. The specific methods for obtaining the sensitivity and target weight will be explained in detail later.

[0083] Step 203: Based on the target weights and the mean square error between the theoretical and measured spectra, establish a first matching function. Specifically, the expression for the first matching function can be as follows:

[0084]

[0085] Where M is the number of spectral elements, N is the number of wavelengths, and w i Let λ be the target weight corresponding to the i-th spectral element. j For the j-th wavelength, S i This is the first theoretical value corresponding to the i-th spectral element in the theoretical spectrum. Let be the first measured value of the i-th spectral element in the measured spectrum, where 1 ≤ i ≤ M and 1 ≤ j ≤ N. It can be understood that the first matching function, based on the calculation of the mean square error between the theoretical spectrum and the measured spectrum, incorporates a target weight as an influencing factor. The larger the target weight, the greater the influence of the spectral element corresponding to that target weight on the measured morphological parameter, and the higher the sensitivity. In the subsequent matching process of the theoretical spectrum based on the first matching function, the degree of matching of this spectral element will be given special consideration.

[0086] Step 204: Based on the first matching function, obtain the theoretical spectrum that best matches the measured spectrum as the first matching spectrum, and obtain the first measurement vector based on the first matching spectrum. The first measurement vector includes the first measured value of each morphological parameter to be measured. In obtaining the theoretical spectrum that best matches the measured spectrum, either a regression analysis algorithm or a library matching algorithm can be selected. The acquisition of the first matching spectrum will be explained in detail later.

[0087] It is understandable that, through steps 201 to 204 above, a target weight representing the sensitivity of spectral elements to the morphological parameters to be measured is introduced during the establishment of the first matching function. Based on this, the first matching spectrum obtained using the first matching function is more accurate, and correspondingly, the obtained morphological parameters are more accurate. The specific implementation of steps 201 to 204 above will be further explained below:

[0088] In some embodiments of this disclosure, in the specific implementation of the aforementioned step 202, Figure 3 A schematic flowchart illustrating the process of obtaining the sensitivity of each spectral element to the morphological parameter to be measured is shown. For example... Figure 3 As shown, the specific steps may include the following:

[0089] Step 301: Obtain a reference sample corresponding to the sample to be tested, and obtain at least one reference spectrum and at least one reference morphology parameter vector corresponding to at least one reference point on the reference sample. Each reference morphology parameter vector includes a reference value for each morphology parameter to be tested. It is understood that the reference sample and the sample to be tested need to be of the same type. For example, if the sample to be tested is a wafer, and the reference sample is a wafer of the same type, during the acquisition of the reference spectrum, if B reference points (B being an integer greater than or equal to 1) are selected in the reference sample, the same optical critical dimension measurement device used to acquire the reference spectra corresponding to these B reference points can be used. During the acquisition of the reference morphology parameter vector, a scanning electron microscope can be used to acquire the reference values ​​for each morphology parameter to be tested corresponding to the B reference points, or the same optical critical dimension measurement device can be used to acquire the reference values ​​for each morphology parameter to be tested corresponding to the aforementioned B reference points; no limitation is made here.

[0090] In some embodiments, a reference spectrum and a reference topography parameter vector are obtained at any reference point. Thus, B reference spectra and B reference topography parameter vectors are obtained at B reference points.

[0091] Step 302: Based on the mean square error between the theoretical spectrum and the reference spectrum, and the change in the weight of each spectral element, establish a second matching function for each spectral element. Specifically, the expression for the second matching function can be as follows:

[0092]

[0093] Where, Δ i Let δ be the second matching function corresponding to the i-th spectral element. i Let δ be the change in weight of the i-th spectral element. i S is a positive value. i This is the second theoretical value corresponding to the i-th spectral element in the theoretical spectrum. Let λ be the second measured value of the i-th spectral element in the reference spectrum, 1≤i≤M, 1≤j≤N, where M is the number of spectral elements, N is the number of wavelengths, and λ is the second measured value of the i-th spectral element in the reference spectrum. j This represents the j-th wavelength.

[0094] In the specific implementation of step 302 above, a loop operation can be performed starting from i=1 until i=M+1, at which point the loop ends and the second matching function corresponding to the i-th spectral element is obtained in the i-th loop.

[0095] Step 303: Based on the second matching function, obtain the theoretical spectrum with the highest degree of matching to the reference spectrum as the second matching spectrum, and obtain the second measurement vector based on the second matching spectrum, resulting in at least one second measurement vector with the same number as the reference spectrum, and each second measurement vector includes the second measurement value of each morphological parameter to be measured. It is understood that in the process of obtaining the second matching spectrum with the highest degree of matching according to the second matching function, the same regression analysis algorithm or library matching algorithm as in step 204 above can be used, which will not be elaborated here.

[0096] Step 304: Based on the sum of squares of the differences between the reference values ​​and the second measured values ​​of each morphological parameter to be measured, and the change in weight, obtain the sensitivity of each spectral element to the morphological parameter to be measured.

[0097] In the above embodiments, further, in the specific implementation of step 302, a second matching function corresponding to each spectral element can be established based on the mean square error between the theoretical spectrum and the reference spectrum, the change in the weight of each spectral element, and the preset weights of all spectral elements. Specifically, the expression of the second matching function can also be as follows:

[0098]

[0099] Where, Δ i ' is the second matching function corresponding to the i-th spectral element, δ i Let δ be the change in weight of the i-th spectral element. i For positive values, t i The preset weights corresponding to the i-th spectral element, t i The non-negative number t i ≥0,S i This is the second theoretical value corresponding to the i-th spectral element in the theoretical spectrum. Let λ be the second measured value of the i-th spectral element in the reference spectrum, 1≤i≤M, 1≤j≤N, where M is the number of spectral elements, N is the number of wavelengths, and λ is the second measured value of the i-th spectral element in the reference spectrum. j This represents the j-th wavelength.

[0100] It can be seen that a preset weight t is further introduced in the process of establishing the second matching function. i Regarding the original technical solution (i.e., Δ mentioned above) i The scope has been expanded, and those skilled in the art can set appropriate preset weights t according to actual needs. i No specific limitations are specified here. It is understandable that when the preset weight t... i When both are set to 1, the updated second matching function remains consistent with the original second matching function provided in the aforementioned embodiment; preset weight t iThe presence of will not affect the calculation result of the sensitivity of the i-th spectral element to the morphological parameter to be measured.

[0101] In the above embodiments, further, the change in weight δ of each spectral element i It can be set to be the same for all. Those skilled in the art will understand that, generally speaking, the change in weight of each spectral element is different. However, in the above specific implementation, in order to simplify the setting of the change in weight, the change in weight of each spectral element can be set to be the same. In this case, the sensitivity of the i-th spectral element to the morphological parameter to be measured will not change due to the change in the magnitude of the change in weight.

[0102] In the above embodiments, in the specific implementation of step 304, the expression for sensitivity can be as follows:

[0103]

[0104] Where B is the number of reference topographic parameter vectors, K is the number of topographic parameters to be measured, 1≤b≤B, 1≤k≤K, v b，k，ref v is the reference value of the k-th topographic parameter to be measured in the b-th reference topographic parameter vector. b，k，mea Let δ be the second measured value of the k-th topographic parameter in the b-th second measurement vector, where 0 < δ i ≤1.

[0105] Understandably, in the expression for sensitivity, the numerator represents the sum of squares of the differences between the reference value and the second measured value of each morphological parameter to be measured, and the denominator represents the change in the weight of the i-th spectral element. The overall meaning of the expression is the change in the weight δ of the i-th spectral element. i The error between the measured value and the reference value of the morphological parameter to be measured is caused by the spectral element, which is the sensitivity of the i-th spectral element to the morphological parameter to be measured.

[0106] In some embodiments of this disclosure, in the specific implementation of step 202 described above, after obtaining the sensitivity of each spectral element to the morphological parameter to be measured, the obtained sensitivity of each spectral element to the morphological parameter to be measured can be directly used as the target weight corresponding to the spectral element. Alternatively, the sensitivity of each spectral element to the morphological parameter to be measured can be normalized, and the result of the normalization process can be used as the target weight corresponding to the spectral element. Specifically, the expression for the normalization process can be as follows:

[0107]

[0108] Among them, w′ i Let w be the sensitivity of the i-th spectral element to the morphological parameter to be measured. iLet be the target weight corresponding to the i-th spectral element, and M be the number of spectral elements, where 1 ≤ i ≤ M. It can be understood that after the above normalization process, the target weight corresponding to each spectral element is limited to between 0 and 1, which can avoid the situation where the target weight values ​​of different spectral elements differ too much.

[0109] In some embodiments of this disclosure, in one specific implementation of step 204 described above, Figure 4 A schematic diagram of a process for obtaining the theoretical spectrum that best matches the measured spectrum as the first matching spectrum is shown, as follows: Figure 4 As shown, it may include the following steps:

[0110] Step 401: According to the first matching function, the measured spectrum is matched with a preset theoretical spectrum database to obtain the mean square error value Ω′ of each preset theoretical spectrum and the measured spectrum. It can be understood that the first matching function provided in the aforementioned embodiment essentially calculates the mean square error value between the theoretical spectrum and the measured spectrum. The smaller the mean square error value, the closer the measured spectrum is to the preset theoretical spectrum. The preset theoretical spectrum with the smallest mean square error value can be used as the first matching spectrum with the highest degree of matching with the measured spectrum.

[0111] Step 402: Select the preset theoretical spectrum with the smallest mean square error value as the first matching spectrum.

[0112] It is understandable that, in addition to using a preset theoretical spectral database for library matching, the first matching spectrum can also be obtained by iterative regression of the measured spectrum based on the first matching function. The iterative regression involves continuously iterating the preset initial value of the morphological parameter to be measured. When the current value of the first matching function Ω′ is less than the preset threshold or cannot be further reduced through iteration, it indicates that the current theoretical spectrum obtained at this time is the first matching spectrum with the highest degree of matching with the measured spectrum.

[0113] In other embodiments of this disclosure, in another specific implementation of step 204 described above, Figure 5 This diagram illustrates another process for obtaining the theoretical spectrum that best matches the measured spectrum as the first matching spectrum, which differs from steps 401 to 402 described above. Figure 5 As shown, it may include the following steps:

[0114] Step 501: Obtain the preset initial values ​​of the morphological parameters to be tested and the initial theoretical spectrum of the sample to be tested.

[0115] Step 502: Calculate the mean square error of the initial theoretical spectrum and the measured spectrum according to the first matching function, that is, obtain the current value of the first matching function Ω′, and perform iterative processing on the preset initial value of the morphology parameter to be measured based on the optimization algorithm to obtain the current value of the morphology parameter to be measured.

[0116] Step 503: Repeatedly iterate over the current value of the morphology parameter to be tested to update the current value of the morphology parameter to be tested, and update the current value of the first matching function Ω′ until the current value of the first matching function Ω′ is less than the preset threshold or cannot be further reduced by iteration, then stop the iterative process and take the obtained current theoretical spectrum as the first matching spectrum.

[0117] In some embodiments of this disclosure, Figure 6 A schematic diagram of a morphology parameter measurement system is shown, used to implement the morphology parameter measurement method provided in the aforementioned embodiments. Figure 6 As shown, the morphological parameter measurement system 600 may specifically include:

[0118] The measurement spectrum acquisition unit 601 is used to acquire the measurement spectrum of the sample to be tested. The measurement spectrum includes at least one spectral element and at least one wavelength corresponding to each spectral element. The sample to be tested has at least one morphological parameter to be measured.

[0119] The sensitivity acquisition unit 602 is used to acquire the sensitivity of each spectral element to the morphology parameter to be measured, and to acquire the target weight corresponding to each spectral element based on the sensitivity.

[0120] The matching function acquisition unit 603 is used to establish a first matching function based on the target weight and the mean square error between the theoretical spectrum and the measured spectrum;

[0121] The parameter measurement unit 604 is used to obtain the theoretical spectrum that matches the measured spectrum the most based on the first matching function as the first matching spectrum, and to obtain the first measurement vector based on the first matching spectrum. The first measurement vector includes the first measurement value of each morphological parameter to be measured.

[0122] It is understood that the actions performed by the above-mentioned measurement spectrum acquisition unit 601 to parameter measurement unit 604 are consistent with the aforementioned steps 201 to 204, and will not be described in detail here.

[0123] It should be noted that in the embodiments provided in this disclosure, terms including the word vector are used, such as first measurement vector and reference topography parameter vector. A vector refers to listing one or more values ​​together. When a vector includes multiple values, the order in which the values ​​are placed is not limited. Taking the first measurement vector as an example, which includes two first measurement values, the line width measurement value Lm and the sidewall angle measurement value Am, the first measurement vector can be represented as (Lm, Am) or (Am, Lm).

[0124] In summary, the technical solution provided in this disclosure overcomes the problem that complex semiconductor manufacturing processes can lead to insufficient sensitivity of the measured spectrum to morphological parameters, thus affecting the accuracy of morphological parameter measurements. It introduces a sensitivity assessment for each spectral element relative to the morphological parameter to be measured, assigns a target weight to each spectral element based on this sensitivity, establishes a first matching function, and performs spectral matching based on the first matching function and the measured spectrum. This results in a more accurate theoretical spectrum and, consequently, more accurate morphological parameters. The technical solution provided in this disclosure improves the accuracy of morphological parameter measurements and has widespread applicability.

[0125] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of this disclosure and should not be construed as limiting the specific implementation of this disclosure to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of this disclosure, and all such modifications and substitutions should be considered within the scope of protection of this disclosure.

Claims

1. A method for measuring morphological parameters, characterized in that, Includes the following steps: The measurement spectrum of the sample to be tested is obtained, the measurement spectrum includes at least one spectral element and at least one wavelength corresponding to each spectral element, and the sample to be tested has at least one morphological parameter to be measured; Obtain a reference sample corresponding to the sample to be tested, and obtain at least one reference spectrum and at least one reference morphology parameter vector corresponding to the reference sample at at least one reference point, wherein each reference morphology parameter vector includes a reference value for each of the morphology parameters to be tested. Based on the mean square error between the theoretical spectrum and the reference spectrum, and the change in the weight of each spectral element, a second matching function is established for each spectral element. Based on the second matching function, the theoretical spectrum with the highest matching degree to the reference spectrum is obtained as the second matching spectrum, and a second measurement vector is obtained based on the second matching spectrum, resulting in at least one second measurement vector with the same number as the reference spectrum, and each second measurement vector includes a second measurement value for each of the topographic parameters to be measured; based on the sum of squares of the differences between the reference value and the second measurement value of each topographic parameter to be measured and the change in the weight, the sensitivity of each spectral element to the topographic parameter to be measured is obtained, and the target weight corresponding to each spectral element is obtained according to the sensitivity; Based on the target weights, and according to the mean square error between the theoretical spectrum and the measured spectrum, a first matching function is established; Based on the first matching function, the theoretical spectrum that matches the measured spectrum to the highest degree is obtained as the first matching spectrum, and a first measurement vector is obtained based on the first matching spectrum. The first measurement vector includes the first measurement value of each of the topographic parameters to be measured.

2. The method for measuring morphological parameters according to claim 1, characterized in that, Based on the mean square error between the theoretical spectrum and the reference spectrum, and the change in the weight of each spectral element, a second matching function is established for each spectral element, including the following steps: Based on the mean square error between the theoretical spectrum and the reference spectrum, the change in the weight of each spectral element, and the preset weights of all spectral elements, a second matching function is established for each spectral element.

3. The method for measuring morphological parameters according to claim 1, characterized in that, The expression for the second matching function is as follows: in, For the first The second matching function corresponding to each of the spectral elements. For the first The change in the weight of each of the spectral elements, If positive, For the theoretical spectrum and the first The second theoretical value corresponding to each of the aforementioned spectral elements. The first in the reference spectrum The second measurement value of each of the spectral elements , M is the number of spectral elements, and N is the number of wavelengths. Representing the The wavelengths mentioned above.

4. The method for measuring morphological parameters according to claim 2, characterized in that, The expression for the second matching function is as follows: in, 'For the first The second matching function corresponding to each of the spectral elements. For the first The change in the weight of each of the spectral elements, It is a positive value. For the first The preset weights corresponding to each of the spectral elements. It is a non-negative number. For the theoretical spectrum and the first The second theoretical value corresponding to each spectral element The first in the reference spectrum The second measurement value of each of the spectral elements , M is the number of spectral elements, and N is the number of wavelengths. Representing the The wavelengths mentioned above.

5. The method for measuring morphological parameters according to claim 3 or claim 4, characterized in that, The expression for the sensitivity is as follows: in, The number of reference topography parameter vectors. The number of the topographic parameters to be measured. , , For the first The first of the reference topography parameter vectors The reference values ​​for the morphological parameters to be measured, For the first The second measurement vector in the first The second measured value of the morphological parameter to be measured, 0 ≤1.

6. The method for measuring morphological parameters according to claim 1 or claim 2, characterized in that, The target weight corresponding to each spectral element is obtained based on the sensitivity, including the following steps: The sensitivity of each spectral element to the morphology parameter to be measured is used as the target weight for each spectral element. Alternatively, the sensitivity of each of the acquired spectral elements to the morphology parameter to be measured can be normalized, and the result of the normalization can be used as the target weight. The expression for the normalization process is as follows: in, For the first The sensitivity of each spectral element to the morphological parameter to be measured For the first The target weights corresponding to each of the spectral elements. The number of the spectral elements. .

7. The method for measuring morphological parameters according to claim 1 or claim 2, characterized in that, The measurement spectrum of the sample under test and the reference spectrum of the reference sample were obtained using the same optical critical dimension measurement device. The weight of each spectral element changes by the same amount; Based on the second matching function and regression analysis algorithm or library matching algorithm, the theoretical spectrum that matches the reference spectrum the most is obtained as the second matching spectrum.

8. The method for measuring morphological parameters according to claim 1, characterized in that, Based on the first matching function and regression analysis algorithm or library matching algorithm, the theoretical spectrum that matches the measured spectrum best is obtained as the first matching spectrum; The expression for the first matching function is as follows: in, The number of the spectral elements. The number of wavelengths. For the first The target weights corresponding to the spectral elements. For the first The wavelengths mentioned above For the theoretical spectrum and the first The first theoretical value corresponding to each of the aforementioned spectral elements. The first in the measured spectrum The first measured value of each of the spectral elements , .

9. A morphology parameter measurement system, used to implement the morphology parameter measurement method according to any one of claims 1 to 8, characterized in that, include: A measurement spectrum acquisition unit is used to acquire the measurement spectrum of the sample to be tested. The measurement spectrum includes at least one spectral element and at least one wavelength corresponding to each spectral element. The sample to be tested has at least one morphological parameter to be measured. A sensitivity acquisition unit is used to acquire a reference sample corresponding to the test sample, acquire at least one reference spectrum and at least one reference morphology parameter vector corresponding to at least one reference point of the reference sample, each of the reference morphology parameter vectors including a reference value of each of the test morphology parameters; and establish a second matching function corresponding to each of the spectral elements based on the mean square error between the theoretical spectrum and the reference spectrum and the change in the weight of each of the spectral elements. Based on the second matching function, the theoretical spectrum with the highest matching degree to the reference spectrum is obtained as the second matching spectrum, and a second measurement vector is obtained based on the second matching spectrum, resulting in at least one second measurement vector with the same number as the reference spectrum, and each second measurement vector includes a second measurement value for each of the topographic parameters to be measured; based on the sum of squares of the differences between the reference value and the second measurement value of each topographic parameter to be measured and the change in the weight, the sensitivity of each spectral element to the topographic parameter to be measured is obtained, and the target weight corresponding to each spectral element is obtained according to the sensitivity; The matching function acquisition unit is used to establish a first matching function based on the target weight and the mean square error between the theoretical spectrum and the measured spectrum; The parameter measurement unit is used to obtain the theoretical spectrum that has the highest degree of matching with the measured spectrum as the first matching spectrum based on the first matching function, and to obtain a first measurement vector based on the first matching spectrum. The first measurement vector includes the first measurement value of each of the topographic parameters to be measured.

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