Assay processing apparatus, assay method, and program product

Abstract

The present application provides a kind of determination processing device, determination method and program product.The determination processing device executes the following processing: obtaining the spectrum of observation light generated in the sample by irradiating determination light;Extract one or more data sets containing a spectrum similar to the spectrum of observation light as a candidate group;For each data set of one or more data sets contained in the candidate group, fit the parameters of the model formula in such a way that the spectrum calculated based on the model formula determined according to the film model contained in each data set is consistent with the spectrum of observation light according to the analysis conditions contained in each data set, thereby calculating the determination result containing the film thickness of each layer included in the sample;And determine the film thickness of each layer of the sample based on one or more of the calculated determination results corresponding to each data set contained in the candidate group.

Description

Background of the Invention Technical Field

[0001] This invention relates to a measuring and processing apparatus, measuring method, and program product for measuring the film thickness and other properties of a sample. Background Technology

[0002] It is known that there is an optical interferometry measuring device for measuring the film thickness and optical constants of thin films (for example, see Japanese Patent Application Publication No. 2000-065536, etc.).

[0003] In optical interferometry measuring apparatus, in order to determine the film thickness of a sample, it is necessary to fit the reflectance spectrum or transmittance spectrum obtained from the sample with the spectrum theoretically calculated according to a pre-prepared model formula (for example, see Japanese Patent Application Publication No. 2013-040813, etc.). Summary of the Invention

[0004] A model formula corresponding to the sample needs to be prepared, but users with little professional knowledge may find it difficult to prepare the model formula.

[0005] According to one embodiment, the measurement processing apparatus performs the following processing: acquiring the spectrum of the observation light generated on the sample by irradiating the measurement light; referring to a database including one or more datasets containing at least a film model, analytical conditions, and spectra, extracting one or more datasets containing spectra similar to the spectrum of the observation light as candidate groups; for each dataset in the candidate group, fitting the parameters of the model formula according to the analytical conditions contained in each dataset in such a way that the spectrum calculated based on the model formula determined according to the film model contained in each dataset is consistent with the spectrum of the observation light, thereby calculating the measurement result of the film thickness of each layer included in the sample; and determining the film thickness of each layer of the sample based on one or more measurement results calculated corresponding to each dataset in the candidate group.

[0006] The measurement processing apparatus can also be configured to perform the following processing: calculating one or more index values ​​based on a comparison between the spectrum of the observed light and the theoretical spectrum calculated by applying the measurement results to a model. The film thickness of each layer of the sample can be determined based on one or more index values ​​from each measurement result.

[0007] Fitting the parameters of the model can include changing the film thickness of each layer and changing at least a portion of the optical constants of each layer.

[0008] The measurement processing device may also be configured to perform the following processing: based on a first measurement result calculated by fitting the parameters of a model under the condition of changing the film thickness of each layer while maintaining the optical constant of each layer, if the change in a second measurement result calculated by fitting the parameters of a model under the condition of changing at least a portion of the film thickness and the optical constant of each layer exceeds a predetermined threshold, the second measurement result is discarded.

[0009] At least a portion of the database may also reside on a server independent of the measurement and processing device.

[0010] The measuring and processing apparatus may also be configured to further perform the following processing: generate a dataset based on the film thickness of each layer of the sample obtained from making the decision, and append the dataset to a database.

[0011] The membrane models included in the dataset can include default values ​​for the optical constants of each layer.

[0012] Analysis conditions may include specifying the analysis method used to fit the parameters of the model.

[0013] According to one embodiment, the measurement method includes the following steps: acquiring the spectrum of the observation light generated on the sample by irradiating the sample with measurement light; referring to a database including one or more datasets containing at least a film model, analytical conditions, and spectra, extracting one or more datasets containing spectra similar to the spectrum of the observation light as candidate groups; for each dataset in the candidate group, fitting the parameters of the model formula according to the analytical conditions contained in each dataset in such a way that the spectrum calculated based on the model formula determined according to the film model contained in each dataset is consistent with the spectrum of the observation light, thereby calculating the measurement result of the film thickness of each layer included in the sample; and determining the film thickness of each layer of the sample based on one or more measurement results calculated corresponding to each dataset in the candidate group.

[0014] According to one embodiment, a program product containing a measurement procedure is provided. The measurement procedure causes a computer to perform the following steps: acquiring the spectrum of observation light generated on a sample by irradiating a measurement light; referring to a database including one or more datasets containing at least a film model, analytical conditions, and spectra, extracting one or more datasets containing spectra similar to the spectrum of the observation light as candidate groups; for each dataset in the candidate group, fitting the parameters of the model formula according to the analytical conditions contained in each dataset in such a way that the spectrum calculated based on the model formula determined according to the film model contained in each dataset is consistent with the spectrum of the observation light, thereby calculating the measurement result of the film thickness of each layer included in the sample; and determining the film thickness of each layer of the sample based on one or more measurement results calculated corresponding to each dataset in the candidate group.

[0015] The above and other objects, features, aspects, and advantages of the invention will become clear from the following detailed description of the invention as understood in conjunction with the accompanying drawings. Attached Figure Description

[0016] Figure 1 This is a schematic diagram showing an example of the structure of the optical measurement system of this embodiment.

[0017] Figure 2 This is a schematic diagram showing an example of the cross-sectional structure of the spectrophotometer included in the optical measurement system of this embodiment.

[0018] Figure 3 This is a schematic diagram showing an example of the structure of the measurement processing device included in the optical measurement system of this embodiment.

[0019] Figure 4 This is a diagram illustrating an example of a measurement knowledge database included in the database of the optical measurement system of this embodiment.

[0020] Figures 5A-5C It is shown Figure 4 The diagram shown is a schematic representation of an example of a sample structure represented by the membrane model.

[0021] Figure 6 This figure shows an example of a material information database included in the database of the optical measurement system of this embodiment.

[0022] Figure 7 This is a flowchart illustrating an example of the measurement process of the optical measurement system of this embodiment.

[0023] Figure 8 This is a diagram illustrating an example of the measurement process of the optical measurement system of this embodiment.

[0024] Figure 9 is a schematic diagram showing an application example of the measurement processing apparatus according to the present embodiment. Detailed Description of Embodiments

[0025] Embodiments of the present invention are described in detail below with reference to the accompanying drawings. In addition, the same or corresponding parts in the drawings are denoted by the same reference numerals, and the description thereof will not be repeated.

[0026] <A. Optical Measurement System>

[0027] First, a structural example of the optical measurement system 1 of the present embodiment will be described.

[0028] with reference to Figure 1 , the optical measurement system 1 comprises: a light source 10 configured to generate measurement light for irradiating a sample 2; a spectroscopic detector 20 configured to receive observation light (e.g., reflected light or transmitted light) generated by the measurement light on the sample 2; and a measurement processing apparatus 100 to which the detection result of the spectroscopic detector 20 is input. The detection result represents the light intensity for each wavelength.

[0029] The optical measurement system 1 irradiates the sample 2 with measurement light from the light source 10, observes light interference generated inside the sample 2, and measures the thickness of one or more films contained in the sample 2, etc. In the sample 2, for example, one or more films are formed on a substrate.

[0030] in Figure 1 , as an example, a reflection optical system that irradiates the sample 2 with measurement light and observes reflected light generated from the sample 2 is shown, but a transmission optical system that irradiates the sample 2 with measurement light and observes transmitted light passing through the sample 2 can also be used.

[0031] The measurement processing apparatus 100 measures the film thickness or optical constants (refractive index or extinction coefficient) of the sample 2 based on the detection result of the spectroscopic detector 20. The measurement processing apparatus 100 executes the measurement processing described later by referring to the database 200. At least a part of the database 200 may also exist in a server independent of the measurement processing apparatus 100. In this case, the measurement processing apparatus 100 can access the database 200 via the network 30. The database 200 may also be a part of the measurement processing apparatus 100.

[0032] The light source 10 and the spectroscopic detector 20 are optically connected via a Y-shaped optical fiber 4 having an irradiation port facing the sample 2.

[0033] The light source 10 generates measurement light having a predetermined wavelength range. The wavelength range of the measurement light is determined according to the measurement range of the film thickness and the like. For the light source 10, for example, a halogen lamp, a white LED, or the like is used.

[0034] with reference to Figure 2 The spectrophotometer 20 includes: a diffraction grating 22 that diffracts light incident via the Y-shaped optical fiber 4; a light-receiving unit 24 disposed corresponding to the diffraction grating 22; and a communication interface 26 electrically connected to the light-receiving unit 24 for outputting detection results to the measurement and processing apparatus 100. The light-receiving unit 24 is composed of a line sensor or a two-dimensional sensor, etc., and is capable of outputting the intensity of each wavelength component as a detection result.

[0035] Reference Figure 3 The measurement and processing device 100 includes a processor 102, a memory 104, an input unit 106, a display unit 108, a storage device 110, a communication interface 120, a network interface 122, and a media driver 124.

[0036] Typically, the processor 102 is a processing unit such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit), which reads one or more programs stored in the storage device 110 into the memory 104 for execution. The memory 104 is a volatile memory such as DRAM (Dynamic Random Access Memory) or SRAM (Static Random Access Memory), which functions as the working memory for the processor 102 to execute programs.

[0037] The input unit 106 includes a keyboard, mouse, etc., and accepts user input. The display unit 108 outputs the execution results of the program executed by the processor 102 to the user.

[0038] Storage device 110 is composed of non-volatile memory such as hard disk or flash memory, and stores various programs and data. More specifically, storage device 110 maintains operating system 112 (OS), measurement program 114, test result 116, and measurement result 118.

[0039] Operating system 112 provides an environment for processor 102 to execute programs. Measurement program 114 is executed by processor 102, thereby realizing the optical measurement method of this embodiment. Detection result 116 includes data output from spectrophotometer 20. Measurement result 118 includes calculated values ​​of optical properties such as film thickness obtained by executing measurement program 114.

[0040] Communication interface 120 is used to relay data transmission between the measurement processing device 100 and the spectrophotometer 20. Network interface 122 is used to relay data transmission between the measurement processing device 100 and an external server.

[0041] The media driver 124 reads required data from a recording medium 126 (e.g., an optical disc, etc.) storing programs and the like executed by the processor 102, and stores the data in the storage device 110. In addition, the measurement program 114 and the like executed in the measurement processing apparatus 100 may be installed via the recording medium 126 or the like, or may be downloaded from a server via the network interface 122 or the like.

[0042] The measurement program 114 may also call required modules from program modules provided as part of the operating system 112 at predetermined timings in a predetermined arrangement to cause the modules to execute processing. Even if the modules are not included therein, the measurement program 114 is still within the technical scope of the present invention. The measurement program 114 may also be provided as a part incorporated into another program.

[0043] In addition, all or part of the functions provided by executing the measurement program 114 by the processor 102 of the measurement processing apparatus 100 can also be implemented by dedicated hardware.

[0044] <B. Outline of Measurement Processing>

[0045] Next, the measurement processing for film thickness measurement executed by the measurement processing apparatus 100 of the present embodiment will be described.

[0046] The measurement processing apparatus 100 measures the film thickness of one or more films included in the sample 2 by observing optical interference generated inside the sample 2. More specifically, the measurement processing apparatus 100 acquires the reflectance spectrum or transmittance spectrum (hereinafter, also simply referred to as "spectrum") of the sample 2 based on the detection result of the spectroscopic detector 20. In addition, the measurement processing apparatus 100 acquires a reference for calculating the spectrum in advance.

[0047] For the film thickness of a sample, the film thickness is determined by fitting parameters of a model formula such that the spectrum calculated based on the model formula of the sample matches the acquired spectrum. Optical constants can also be determined together with the film thickness. In addition, the spectrum calculated based on the model formula of the sample may be directly compared with the acquired spectrum, or power spectra obtained by performing frequency transformation on each spectrum may be compared with each other. For calculation of a spectrum based on a model formula, simulation based on a model formula may also be used.

[0048] For fitting of parameters of the model formula, the least square method aiming to minimize the least square error representing an error between spectra or between power spectra can also be used. Alternatively, an optimization method such as the Maximum Entropy Method (MEM) may be used to determine parameters of the model formula in a manner that minimizes the error.

[0049] <C.Database 200>

[0050] Next, the database 200 of the optical measurement system 1 according to the present embodiment will be described.

[0051] The measurement processing apparatus 100 refers to the database 200 to determine a model formula or the like, and executes measurement processing.

[0052] With reference to Figure 4 , the measurement knowledge database 210 includes one or more knowledge data sets. Each knowledge data set includes, for example, an ID 211, a film model 212, analysis conditions 213, a spectrum 214, additional information 215, and retrieval information 216.

[0053] The ID 211 is information for identifying a knowledge data set.

[0054] The film model 212 contains information representing the structure of a sample. The film model 212 includes, for example, information such as the number of layers contained in the sample, the material of each layer, the layer category of each layer, the search film thickness range of each layer, and the search film thickness step of each layer. As layer categories, for example, information such as a single layer which is a layer composed of a single material, and a non-interference layer which is a layer in which no optical interference can be observed, are set. In addition, the film thickness of each layer may not be set in the film model 212.

[0055] The search film thickness range of each layer and the search film thickness step of each layer respectively represent the variation range and the variation amount of the film thickness of each layer in the fitting of parameters of the model formula.

[0056] With reference to Figure 5A , in the sample structure 218A, a first layer 2182 is formed on a substrate 2181. For example, the substrate 2181 is set as a non-interference layer, and the first layer 2182 is set as a single layer. When measurement light is incident on the sample structure 218A from the upper side of the drawing plane, reflection occurs respectively on the surface of the first layer 2182 and at the interface between the first layer 2182 and the substrate 2181.

[0057] With reference to Figure 5B , in the sample structure 218B, a first layer 2184 and a second layer 2185 are formed on a substrate 2183. For example, the substrate 2183 is set as a non-interference layer, and the first layer 2184 and the second layer 2185 are set as single layers. When measurement light is incident on the sample structure 218B from the upper side of the drawing plane, reflection occurs respectively on the surface of the second layer 2185, at the interface between the second layer 2185 and the first layer 2184, and at the interface between the first layer 2184 and the substrate 2183.

[0058] With reference to Figure 5CIn the sample structure 218C, a first layer 2187 is formed on a substrate 2186. A second layer 2189 is formed on the first layer 2187, separated by an air layer 2188. For example, both the substrate 2186 and the air layer 2188 are configured as non-interference layers, and both the first layer 2187 and the second layer 2189 are configured as single layers. When measurement light is incident on the sample structure 218C from the side of the paper, reflections occur at the surface of the second layer 2189, the interface between the second layer 2189 and the air layer 2188, the interface between the air layer 2188 and the first layer 2187, and the interface between the first layer 2187 and the substrate 2186, respectively. However, the refractive index of the air layer 2188 is smaller than that of the first layer 2187 and the second layer 2189.

[0059] Refer again Figure 4 Analysis condition 213 includes, for example, information such as the analysis method used to calculate the film thickness and the analysis wavelength range. Analysis condition 213 specifies the analysis method used to fit the parameters of the model. For example, the least squares method or optimization method may be specified as the analysis method.

[0060] Spectrum 214 is the spectrum measured from the sample 2, which is the object. Spectrum 214 represents the reflectance or transmittance of sample 2 at each wavelength.

[0061] Additional information 215 may include, for example, the date and time of the corresponding spectral measurement, the device serial number (e.g., the manufacturing number of the spectrophotometer 20), etc. It is also possible to filter only the knowledge dataset within a specific device based on the additional information 215. The additional information 215 may also include the reliability of the measurement results for which the knowledge dataset was generated.

[0062] Retrieval information 216 is information used to retrieve a knowledge dataset as an object from the measurement knowledge database 210. For example, retrieval information 216 may also include information that enables filtering under conditions such as a sample formed on a glass substrate, a sample formed on a silicon substrate, or a sample containing an air layer.

[0063] Database 200 may also include a database of materials contained in membrane model 212.

[0064] Reference Figure 6 The material information database 220 contains one or more material information entries. For example, each material information entry in the material information database 220 includes an ID 2201, a material name 2202, a refractive index 2203, and an extinction coefficient 2204.

[0065] ID 2201 is information used to specify material information.

[0066] Material name 2202 is the name of the material represented by the material information, and it can be set arbitrarily.

[0067] Refractive index 2203 and extinction coefficient 2204 are default values of optical constants of a target material. For refractive index 2203, the refractive index n(λ) of the target material is set. For extinction coefficient 2204, the extinction coefficient k(λ) of the target material is set. Both the refractive index n(λ) and the extinction coefficient k(λ) can reflect wavelength dependence or wavelength dispersion. The refractive index n(λ) and the extinction coefficient k(λ) can be used to define values for each wavelength, and can be defined using a predetermined function.

[0068] For example, if the material can be regarded as a material with extinction coefficient k(λ)≈0, Cauchy's dispersion formula can also be used to define the refractive index n(λ). In this case, for refractive index 2203, coefficients defining Cauchy's dispersion formula can be set. Alternatively, a function including a Tauc-Lorentz oscillator can also be used.

[0069] By associating the film models 212 included in each knowledge dataset with the material information database 220, default values of optical constants for each layer are set.

[0070] <D. Measurement Processing>

[0071] Next, an example of measurement processing executed by the measurement processing apparatus 100 of the optical measurement system 1 according to the present embodiment will be described.

[0072] with reference to Figure 7 , an example of measurement processing of the optical measurement system 1 according to the present embodiment will be described. Figure 7 the processing executed by the measurement processing apparatus 100 shown in [reference] can also be implemented, for example, by the processor 102 of the measurement processing apparatus 100 executing the measurement program 114.

[0073] The measurement processing apparatus 100 acquires a spectrum of observation light generated on the sample 2 by irradiation of measurement light. As an example, the measurement processing apparatus 100 acquires the spectrum of the sample 2 detected by the spectroscopic detector 20 (step S2). The spectrum may be a reflectance spectrum or a transmittance spectrum.

[0074] The measurement processing apparatus 100 determines one or more indicators for extracting a knowledge dataset from the measurement knowledge database 210 included in the database 200 (step S4).

[0075] The measurement processing apparatus 100 selects one knowledge dataset included in the measurement knowledge database 210 (step S6), compares the spectrum acquired in step S2 with the spectrum 214 included in the selected knowledge dataset, and calculates one or more indicator values (step S8). The measurement processing apparatus 100 calculates an indicator value for each of the one or more indicators determined in step S4.

[0076] One or more index values ​​calculated in step S8 represent the similarity between the spectrum obtained in step S2 and the spectrum 214 contained in the selected knowledge dataset. For example, residuals or correlation coefficients can also be used as indicators of similarity. Alternatively, an index obtained by expanding the residuals or correlation coefficients to take into account the amount of spectral data can be used.

[0077] Repeat steps S6 and S8 until all knowledge datasets contained in the knowledge database 210 are selected ("Yes" in step S10).

[0078] The measurement processing device 100 extracts a predetermined number of knowledge datasets as first candidate groups according to the order of one or more index values ​​calculated in step S8 from best to worst (step S12). The measurement processing device 100 may also classify the first candidate groups of extracted knowledge datasets according to the order of one or more index values ​​calculated in step S8 from best to worst.

[0079] For example, 100 knowledge datasets can be extracted as the first candidate group. Alternatively, the number of knowledge datasets extracted as the first candidate group can be arbitrarily set or changed.

[0080] When multiple index values ​​are calculated in step S8, the first candidate group can be extracted in step S12 based on the combination of multiple index values.

[0081] The processing in step S8 can also be performed using neural networks or similar methods. In this case, the processing of the determination index in step S4 can be omitted.

[0082] Steps S4 to S12 are equivalent to the process of extracting the first candidate group of knowledge datasets for measurement from the measurement knowledge database 210. That is, the measurement processing device 100 refers to the measurement knowledge database 210, which includes one or more knowledge datasets that contain at least a membrane model 212, analytical conditions 213, and a spectrum 214, and extracts one or more knowledge datasets containing spectra similar to the spectrum of the observed light as candidate groups.

[0083] Next, the measurement processing device 100 selects a knowledge dataset from the first candidate group of the extracted knowledge datasets (step S14), and determines the model formula based on the membrane model 212 contained in the selected knowledge dataset (step S16). The determined model formula reflects, for example, the membrane model 212 contained in the selected knowledge dataset. Figures 5A-5C The theoretical formula for the sample structure represented by the film model 212 shown. In the determined model formula, optical constants can also be used from the material information database 220 (see reference). Figure 6 The default value is predefined in the ().

[0084] The measurement processing apparatus 100 fits the parameters of the model formula according to the analysis conditions 213 contained in the selected knowledge dataset in a manner that makes the spectrum calculated based on the model formula consistent with the spectrum obtained in step S2, thereby calculating the film thickness of each layer (step S18). The measurement results of step S18 include the calculated film thickness of each layer and the default values ​​of the optical constants of each layer.

[0085] As for the spectral analysis method, optimization methods and least squares methods defined in analysis condition 213 are employed. In step S18, the film thickness of each layer defined in film model 212 is changed sequentially to search for the optimal film thickness. That is, fitting the parameters of the model formula includes changing the film thickness of each layer.

[0086] The measurement processing device 100 calculates one or more index values ​​based on a comparison between the spectrum obtained in step S2 and the theoretical spectrum calculated by applying the searched film thickness to the film model 212 (step S20). That is, one or more index values ​​are calculated based on a comparison between the spectrum of the observed light and the theoretical spectrum calculated by applying the measurement results to the model. The calculated one or more index values ​​may be the same as or different from those in step S8.

[0087] Repeat steps S14 to S20 until all knowledge datasets contained in the first candidate group of the knowledge dataset are selected ("Yes" in step S22).

[0088] In this way, the measurement processing device 100 fits the parameters of the model formula to each of the one or more knowledge datasets included in the first candidate group in accordance with the analysis conditions 213 contained in each knowledge dataset, so that the spectrum calculated based on the model formula determined according to the film model 212 contained in each knowledge dataset is consistent with the spectrum of the observed light, thereby calculating the measurement results of the film thickness of each layer included in the sample 2.

[0089] The measurement processing device 100 determines the film thickness of each layer of the sample 2 based on one or more measurement results calculated from each knowledge dataset included in the first candidate group (e.g., 100). The measurement result with the best index value can be determined as the film thickness of each layer of the sample 2, but the knowledge dataset corresponding to one or more measurement results can also be used as a second candidate group for further detailed analysis, as shown below.

[0090] Specifically, the measurement processing device 100 extracts a predetermined number of knowledge datasets as the second candidate group based on one or more index values ​​calculated in step S20 (step S24). For example, it is also possible to extract 10 knowledge datasets and the measurement results based on those knowledge datasets from the measurement results based on 100 knowledge datasets, in descending order of index values. Alternatively, the number of knowledge datasets extracted as the second candidate group can be arbitrarily set or changed. It is also possible to extract only one knowledge dataset as the second candidate group.

[0091] When calculating index values ​​for multiple indicators separately in step S24, a knowledge dataset representing well-defined index values ​​and measurement results based on that knowledge dataset can be extracted for each category of the indicators. In this case, the second candidate group can be a set of knowledge datasets extracted for each category of the indicators.

[0092] Steps S14 to S24 are equivalent to determining the first candidate group based on the knowledge dataset and extracting promising knowledge datasets from the candidates of the knowledge dataset as the second candidate group.

[0093] Next, a search process is performed that modifies the optical constants (refractive index or extinction coefficient). That is, the optical constants are modified in addition to the film thickness during the search process. Search processes can be performed separately for cases where all parameters within the optical constants are modified, and for cases where only some parameters within the optical constants are modified. Which parameter within the optical constants is modified can be determined based on additional information contained in the corresponding knowledge dataset.

[0094] Specifically, the determination processing device 100 selects a knowledge dataset from the second candidate group of the extracted knowledge dataset (step S26), and determines the model formula based on the membrane model 212 contained in the selected knowledge dataset (step S28). Furthermore, the processing in step S28 is essentially the same as that in step S16, so the processing in step S28 can be skipped, and the model formula determined in step S16 can be used again.

[0095] The measurement processing device 100 fits the parameters of the model formula in a manner that allows modification of the analysis conditions 213 and all parameters in the optical constants contained in the selected knowledge dataset so that the spectrum calculated based on the model formula is consistent with the spectrum obtained in step S2, thereby calculating the film thickness, refractive index, and extinction coefficient of each layer (step S30). The measurement results of step S30 include the calculated film thickness, refractive index, and extinction coefficient of each layer.

[0096] The measurement processing device 100 fits the parameters of the model formula according to the analysis conditions 213 included in the selected knowledge dataset and a portion of the parameters included in the optical constants, in a way that this condition can be changed so that the spectrum calculated based on the model formula is consistent with the spectrum obtained in step S2, thereby calculating the film thickness, refractive index, and extinction coefficient of each layer (step S32). The measurement result of step S32 includes the calculated film thickness, refractive index, and extinction coefficient of each layer, as well as the default values ​​of the parameters of the optical constants that have not been changed.

[0097] Alternatively, step S32 can be performed separately for multiple cases where the parameters in the optical constants that can be changed are different.

[0098] The measurement processing device 100 discards measurement results from steps S30 and S32 where the change in measurement results does not meet the specified conditions (step S34). For example, the measurement processing device 100 compares the film thickness of each layer calculated by fixedly analyzing optical constants (the calculation result in step S18) and the default value of the optical constants (the value set in the material information database 220) with the film thickness and optical constants of each layer calculated in step S30 or S32. If the change in the film thickness of any layer or the change in the optical constant of any layer exceeds a predetermined threshold, the measurement results from step S30 or S32 are discarded.

[0099] In this way, the measurement processing device 100 uses the measurement result (the calculation result in step S18) calculated by fitting the parameters of the model formula while changing the film thickness of each layer while maintaining the optical constant of each layer as a reference. If the change in the measurement result (the calculation result in step S30 or S32) calculated by fitting the parameters of the model formula while changing at least a portion of the film thickness and optical constant of each layer exceeds a predetermined threshold, the measurement result is discarded.

[0100] The threshold used in step S34 can be arbitrarily set for film thickness, refractive index, and extinction coefficient.

[0101] The measurement processing device 100 calculates one or more index values ​​based on a comparison between the spectrum obtained in step S2 and the theoretical spectrum calculated by applying the measurement results from each step of S30 and S32 to the model formula (step S36). The measurement processing device 100 determines the measurement result representing the optimal index value from the measurement results when the optical constants are at their default values ​​(step S18), the measurement results when all parameters included in the optical constants can be changed (step S30), and the measurement results when some parameters included in the optical constants can be changed (step S32) (step S38).

[0102] Repeat steps S26 to S38 until all knowledge datasets contained in the second candidate group of the knowledge dataset are selected ("Yes" in step S40).

[0103] The measurement processing device 100 determines the best measurement result among the measurement results determined in step S38 (step S42).

[0104] The measurement results determined in step S42 can also be output as the final measurement results. In this way, the film thickness of each layer of sample 2 can be determined based on one or more index values ​​of each measurement result.

[0105] For ease of explanation, Figure 7 The required processing is described in detail, but for processing that can utilize previous calculation results, redundant calculations can be eliminated by making the most of those previous results, thereby reducing the processing load. Alternatively, some or all of the processing can be performed in parallel.

[0106] Reference Figure 8 For example, suppose we have prepared a measurement knowledge database 210 containing N knowledge datasets.

[0107] When the measurement processing device 100 acquires a spectrum (step S2), it calculates one or more indicators for each knowledge dataset based on the comparison results between the acquired spectrum and the spectra contained in each knowledge dataset contained in the measurement knowledge database 210 (step S8).

[0108] The measurement and processing device 100 extracts a predetermined number of knowledge datasets (the first candidate group of knowledge datasets) in descending order of one or more calculated index values ​​(step S12). Figure 8 The example shown is of extracting M (M≤N) knowledge data from N knowledge datasets.

[0109] Next, the measurement processing device 100 determines the model formula for each of the M knowledge datasets based on the membrane model 212 contained in each knowledge dataset (step S16). Then, the measurement processing device 100 fits the parameters of the model formula according to the analysis conditions 213 contained in each knowledge dataset in a way that makes the spectrum calculated based on the model formula consistent with the acquired spectrum, thereby calculating the membrane thickness of each layer (step S18).

[0110] Furthermore, the measurement processing device 100 calculates one or more index values ​​based on the comparison between the acquired spectrum and the theoretical spectrum calculated by applying the searched film thickness to the model (step S20).

[0111] The measurement processing apparatus 100 extracts a prescribed number of knowledge data sets as a second candidate group based on the calculated one or more index values (step S24). In Figure 8 an example of calculating three indicators is shown. For indicator 1, knowledge data set 3 represents the optimum index value. For indicator 2, knowledge data set 5 represents the optimum index value. For indicator 3, knowledge data set 8 represents the optimum index value.

[0112] Next, for each of the three knowledge data sets, the measurement processing apparatus 100 determines a model formula based on the film model 212 included in each knowledge data set (step S28). Then, the measurement processing apparatus 100 fits the parameters of the model formula such that the spectrum calculated based on the model formula matches the acquired spectrum under the condition that all parameters included in the analysis conditions 213 and optical constants contained in each knowledge data set can be changed, thereby calculating the film thickness, refractive index and extinction coefficient of each layer (step S30). Similarly, the measurement processing apparatus 100 fits the spectrum calculated based on the model formula with the acquired spectrum under the condition that part of the parameters included in the analysis conditions 213 and optical constants contained in each knowledge data set can be changed, thereby calculating the film thickness, refractive index and extinction coefficient of each layer (step S32).

[0113] In steps S30 and S32, fitting the parameters of the model formula includes changing the film thickness of each layer and changing at least part of the optical constants of each layer.

[0114] Furthermore, for each of the three knowledge data sets, the measurement processing apparatus 100 calculates one or more index values for each measurement result based on the comparison result between the acquired spectrum and the theoretical spectrum calculated by applying each measurement result to the model formula (step S36). The measurement processing apparatus 100 determines the measurement result representing the optimum index value. That is, the measurement processing apparatus 100 determines the measurement result representing each knowledge data set.

[0115] In Figure 8 the example shown, for knowledge data set 3, the measurement result obtained when all parameters included in the optical constant are changed is determined to be output. For knowledge data set 5, the measurement result obtained when the optical constant is fixed is determined to be output. For knowledge data set 8, the measurement result obtained when part of the parameters included in the optical constant are changed is determined to be output.

[0116] The measurement processing apparatus 100 compares one or more index values of the measurement results representing respective knowledge data sets with each other to determine an optimum measurement result (step S42).

[0117] <E. Output of Measurement Results>

[0118] The finally determined measurement results such as film thickness, refractive index and extinction coefficient of each layer can be output to the display of the measurement processing apparatus 100, or can be output to the display of any measurement processing terminal. Alternatively, the measurement results can also be sequentially stored as electronic data in the storage device of the measurement processing apparatus 100 or a server. For example, data representing results obtained by sequentially measuring samples can also be sequentially stored in the storage device of the server.

[0119] As will be described later, when a knowledge data set generated based on measurement results is added to the database 200, not only the film thickness, refractive index and extinction coefficient of each layer can be output, but also information on the measurement process can be output together. Information of the measurement process includes the film model, analysis conditions, etc.

[0120] <F. Generation and Management of Database 200>

[0121] Next, an example of generation and management of the database 200 of the optical measurement system 1 according to the present embodiment will be described.

[0122] A knowledge data set is generated based on measurement results obtained through past measurements. One knowledge data set can also be generated based on the measurement result of one sample. The knowledge data set may be the same as the knowledge data set obtained through measurement, or may be a result obtained after applying certain corrections to the knowledge data set obtained through measurement.

[0123] The measurement processing apparatus 100 can also perform processing of generating a knowledge data set based on the film thickness of each layer of the sample included in the measurement result, and adding the knowledge data set to the database 200.

[0124] The knowledge data set can also be a knowledge data set based on simulation results.

[0125] When adding a knowledge data set based on a certain measurement result to the measurement knowledge database 210, for example, a condition can be set that the reliability of the measurement result exceeds a predetermined threshold.

[0126] In a case where the sample structure of the sample corresponding to a certain measurement result is the same as the sample structure of a knowledge data set already registered in the measurement knowledge database 210, the condition that the reliability of the current measurement result is higher than that of the registered knowledge data set can be set for adding the knowledge data set based on the current measurement result to the database 200.

[0127] The above description illustrates an example where database 200 includes a measurement knowledge database 210 and a materials information database 220. The information described can be registered in database 200 in any form. For example, the measurement knowledge database 210 can be implemented using a combination of multiple tables, or it can be a table formed by merging the measurement knowledge database 210 and the materials information database 220.

[0128] Database 200 can be configured in at least one of measurement processing device 100 and server.

[0129] Reference Figure 9 This section will illustrate an example of the application of the measurement and processing apparatus 100 in this embodiment. Figure 9 As an example, the optical measurement system 1 was introduced in companies A through D respectively.

[0130] Each of the measurement processing devices 100A to 100D, respectively installed at companies A to D, has a database 200B containing basic data. The basic data may be, for example, a collection of knowledge datasets generated based on measurement results of general samples. The basic data may also be prepared by, for example, the manufacturer or seller of the optical measurement system 1.

[0131] The server 300, accessible from the measurement and processing devices 100A to 100D, has a database 200A containing additional data. This additional data may be, for example, a collection of knowledge datasets generated based on the measurement results of a specific sample.

[0132] Alternatively, under specified conditions, the measurement and processing devices 100A~100D can access the database 200A of the server 300. By accessing the database 200A, the knowledge dataset of the first candidate group can be extracted from a larger knowledge dataset.

[0133] For example, the performance of a contract (e.g., payment of usage fees) between the user (e.g., Company A to Company D) and the operator of server 300 (e.g., the manufacturer or seller of optical measurement system 1) can be set as a condition.

[0134] Server 300 can also have a database 200CA~200CC containing user-specific data. Figure 9 In the example shown, database 200CA contains inherent data of company A, database 200CB contains inherent data of company B, and database 200CC contains inherent data of company C.

[0135] Each user may also register a knowledge dataset based on measurement results into the database 200CC as required. Alternatively, the manufacturer or seller of the optical measurement system 1 may also register the knowledge dataset into the database 200CC based on the measurement results from each user.

[0136] The measurement processing apparatus 100 may also have a function of generating a knowledge dataset based on the measurement result according to a user operation or the like and registering the generated knowledge dataset in the database 200. As preprocessing for generating a knowledge dataset based on measurement results, the measurement processing apparatus 100 may also have a filtering function for extracting a measurement result to be registered in the database 200 from one or more measurement results.

[0137] <G.Modified Example>

[0138] In the above description, a structural example in which the measurement processing apparatus 100 directly acquires detection results from the spectroscopic detector 20 is shown, but the measurement processing apparatus 100 may also acquire the detection results output by the spectroscopic detector 20 by any method (for example, via a recording medium or a network).

[0139] In the above example of the measurement processing ( Figure 7 and Figure 8 ), the processing of extracting the first candidate group and the second candidate group respectively is adopted, but part or all of these processes may also be simplified.

[0140] For example, when the number of knowledge datasets contained in the measurement knowledge database 210 is small, the process of extracting the first candidate group may also be omitted. That is, the processing of steps S6 to S12 may be omitted, and the processing of steps S14 to S22 may be performed on all knowledge datasets contained in the measurement knowledge database 210.

[0141] For example, the measurement result with a good index value among the measurement results (steps S14 to S22) calculated based on each knowledge dataset contained in the first candidate group may also be determined as the final measurement result. In this case, the process of determining the final measurement result based on one or more index values is performed instead of the process of extracting the second candidate group (step S24). At this time, the processing after step S24 may also be omitted. As such, the process of extracting the second candidate group may also be omitted.

[0142] In addition, both the process of extracting the first candidate group and the process of extracting the second candidate group can also be omitted. In this case, measurement results are calculated respectively based on all knowledge datasets contained in the measurement knowledge database 210. The measurement result with a good index value among the calculated measurement results may also be determined as the final measurement result.

[0143] <H.Advantages>

[0144] According to the optical measurement system of this embodiment, even without pre-determining the model formula corresponding to the sample, an appropriate sample structure and the corresponding model formula can be determined by referring to a database. Therefore, even users with limited or no expertise can easily measure the film thickness of each layer of the sample. Furthermore, since the sample structure is selected using indicators, the accuracy of the measurement can be improved.

[0145] Embodiments of the present invention have been described, but should be considered as illustrative in all respects and not restrictive. The scope of the invention is set forth in the claims and is intended to include all modifications within the meaning and scope equivalent to the claims.

Claims

1. A measuring and processing apparatus configured to perform the following processing: Obtain the spectrum of the observation light produced on the sample by irradiating it with the measurement light; Referring to a database that includes one or more datasets containing at least membrane models, analysis conditions, and spectra, one or more datasets containing spectra similar to the observed light are extracted as candidate groups; For each dataset in one or more datasets included in the candidate group, the parameters of the model formula are fitted in a manner that makes the spectrum calculated based on the model formula determined according to the film model included in each dataset consistent with the spectrum of the observed light, according to the analysis conditions included in each dataset, thereby calculating the measurement results of the film thickness of each layer included in the sample. as well as The film thickness of each layer of the sample is determined based on one or more measurement results calculated from the measurement results corresponding to each dataset included in the candidate group.

2. The measuring and processing apparatus according to claim 1, wherein, The measurement processing device is configured to further perform the following processing: calculating one or more index values ​​based on a comparison between the spectrum of the observed light and the theoretical spectrum calculated by applying the measurement results to the model formula. The thickness of each layer of the sample was determined based on one or more index values ​​from the measurement results.

3. The measuring and processing apparatus according to claim 1, wherein, Fitting the parameters of the model involves changing the film thickness of each layer and changing at least a portion of the optical constants of each layer.

4. The measuring and processing apparatus according to claim 3, wherein, The measurement processing device is configured to further perform the following processing: based on a first measurement result calculated by fitting the parameters of the model formula while maintaining the optical constants of each layer and changing the film thickness of each layer, if the change in a second measurement result calculated by fitting the parameters of the model formula while changing the film thickness of each layer and at least a portion of the optical constants of each layer exceeds a predetermined threshold, the second measurement result is discarded.

5. The measuring and processing apparatus according to any one of claims 1 to 4, wherein, At least a portion of the database resides on a server independent of the measurement and processing device.

6. The measuring and processing apparatus according to any one of claims 1 to 4, wherein, The measurement processing apparatus is configured to further perform the following processing: generate a dataset based on the film thickness of each layer of the sample obtained from making the decision, and append the dataset to the database.

7. The measuring and processing apparatus according to any one of claims 1 to 4, wherein, The membrane models included in the dataset contain default values ​​for the optical constants of each layer.

8. The measuring and processing apparatus according to any one of claims 1 to 4, wherein, The analysis conditions include the specification of an analysis method for fitting the parameters of the model.

9. A determination method, comprising the following steps: Obtain the spectrum of the observation light produced on the sample by irradiating it with the measurement light; Referring to a database that includes one or more datasets containing at least membrane models, analysis conditions, and spectra, one or more datasets containing spectra similar to the observed light are extracted as candidate groups; For each dataset in one or more datasets included in the candidate group, the parameters of the model formula are fitted in a manner that makes the spectrum calculated based on the model formula determined according to the film model included in each dataset consistent with the spectrum of the observed light, according to the analysis conditions included in each dataset, thereby calculating the measurement results of the film thickness of each layer included in the sample. as well as The film thickness of each layer of the sample is determined based on one or more measurement results calculated from the measurement results corresponding to each dataset included in the candidate group.

10. A program product containing a measurement program that causes a computer to perform the following steps: Obtain the spectrum of the observation light produced on the sample by irradiating it with the measurement light; Referring to a database that includes one or more datasets containing at least membrane models, analysis conditions, and spectra, one or more datasets containing spectra similar to the observed light are extracted as candidate groups; For each dataset in one or more datasets included in the candidate group, the parameters of the model formula are fitted in a manner that makes the spectrum calculated based on the model formula determined according to the film model included in each dataset consistent with the spectrum of the observed light, according to the analysis conditions included in each dataset, thereby calculating the measurement results of the film thickness of each layer included in the sample. as well as The film thickness of each layer of the sample is determined based on one or more measurement results calculated from the measurement results corresponding to each dataset included in the candidate group.

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