Information processing system, information processing method, and program

Abstract

According to one aspect of the present invention, an information processing system is provided. The information processing system includes at least one processor. The processor is configured to execute the program so as to perform the following steps. In the acquisition step, target measurement data indicating the result of measurement using X-rays on a sample to be analyzed and at least one reference data are acquired. In the designation acceptance step, designation of the reference data is accepted. In the setting step, an objective function for performing non-negative matrix factorization including a linear combination of at least one variable base and at least one fixed base is set based on the designation. In this case, the fixed substrates each correspond to the specified reference data. Components of the variable substrate are set to parameters that are variable when non-negative matrix factorization is performed. The components of the fixed substrate are set to parameters that are fixed when non-negative matrix factorization is performed.

Description

Technical Field

[0001] This invention relates to an information processing system, information processing method, and program. Background Technology

[0002] Patent document 1 discloses a technique that can return decomposition results close to the true substrate spectrum in the decomposition of spectral data in the expectation of a highly independent decomposition.

[0003] The nonnegative matrix factorization part involved in this technique finds multiple base spectral data and activation data by searching for the deviation of the observed spectral dataset and the estimated spectral dataset calculated based on multiple base spectral data and activation data, as well as the minimum value of the objective function including the regularization term that evaluates the first-order independence of the multiple base spectral data or the activation data. Existing technical documents Patent documents

[0004] Patent Document 1: Japanese Patent Application Publication No. 2019-087042 Summary of the Invention The problem the invention aims to solve

[0005] However, for example, when the peaks included in the spectrum overlap, it may be difficult to obtain stable analytical results when using nonnegative matrix factorization based on the above technique to analyze the X-ray measurement results. Solution for solving the problem

[0006] According to one aspect of the present invention, an information processing system is provided. This information processing system includes at least one processor. The processor is configured to execute a program in a manner that performs the following steps: In an acquisition step, object measurement data and at least one reference data representing the results of X-ray measurements applied to a sample being analyzed are acquired. In a designation and acceptance step, the designation of the reference data is accepted. In a setting step, based on the designation, an objective function for performing nonnegative matrix factorization is set, comprising a linear combination of at least one variable basis and at least one fixed basis. Here, the fixed basis corresponds to the designated reference data. The components of the variable basis are set as parameters that are variable during nonnegative matrix factorization. The components of the fixed basis are set as parameters that are fixed during nonnegative matrix factorization. Attached Figure Description

[0007] Figure 1 This is a structural diagram representing information processing system 1. Figure 2 This is a diagram showing an example of the structure of X-ray measuring device 2. Figure 3This is a block diagram representing the hardware structure of the information processing device 3. Figure 4 This is an activity diagram representing an example of the information processing flow performed in information processing system 1. Figure 5 It is a graph that represents the relationship between the object measurement data, the variable basis, and the fixed basis contained in the objective function. Figure 6 This is a diagram showing an example of the analysis screen IM1. Figure 7 This is a graph showing the compositional analysis results of SiO-based anode materials for lithium secondary batteries based on this information processing. Figure 8 The results of the compositional analysis of the SiO-based anode material for a lithium secondary battery based on linear combination fitting (LCF) are presented as comparative examples. Detailed Implementation

[0008] The embodiments of the present invention will now be described with reference to the accompanying drawings. Various features shown in the following embodiments can be combined with each other.

[0009] However, the program for implementing the software appearing in one embodiment can be provided as a non-transitory computer-readable medium, or it can be provided in a manner that allows it to be downloaded from an external server, or it can be provided in a manner that allows the program to be launched using an external computer and its functionality to be implemented on a client (so-called cloud computing).

[0010] Furthermore, in various information processing methods according to one embodiment, input and corresponding output can be realized. Here, the type of information referenced in this information processing (hereinafter referred to as reference information) is not limited, as long as the output is obtained as a result of the input. Reference information can be, for example, information from a database, lookup table, rule base such as a defined function (including a regression equation constructed using statistical methods), a pre-learned model that has learned the correlation between input and output, or a large-scale language model that can output the desired result through input prompts.

[0011] In another embodiment, "component" may include, for example, a component that combines hardware resources implemented by a generalized circuit with information processing software that can be specifically implemented by these hardware resources. Furthermore, in one embodiment, various types of information are processed, but this information may be represented, for example, by physical values ​​representing voltage / current signals, high or low signal values ​​as binary bit sets consisting of 0s or 1s, or by quantum superposition (so-called qubits), and communication / computation can be performed on a generalized circuit.

[0012] Furthermore, in a broad sense, a circuit refers to a circuit implemented by appropriately combining at least a circuit, a circuitry, a processor, and a memory. Additionally, a processor can be a general-purpose processor or a special-purpose circuit. That is, it includes Application Specific Integrated Circuits (ASICs), programmable logic devices (e.g., Simple Programmable Logic Devices (SPLDs), Complex Programmable Logic Devices (CPLDs), and Field Programmable Gate Arrays (FPGAs)).

[0013] 1. Hardware Structure This section describes the hardware architecture.

[0014] <Information Processing System 1> Figure 1 This is a structural diagram of the information processing system 1. The information processing system 1 of this embodiment is used for analyzing the state of a sample as the object of analysis using X-rays, such as its structure, composition, valence, and chemical bonding state. The information processing system 1 includes, for example, a database (DB), an X-ray measuring device (2), and an information processing device (3). The database (DB), the X-ray measuring device (2), and the information processing device (3) are configured to communicate via telecommunication lines. In one embodiment, the information processing system 1 consists of one or more devices or components. For example, if it consists only of the information processing device (3), the information processing system 1 can function as the information processing device (3). These components will be described below.

[0015] <Database DB> The database DB stores and uses reference data related to the measurement using X-rays. The reference data may include open data or may be provided limitedly to a part of users. The reference data is data that may be obtained when measuring a substance in a certain state using X-rays. For the reference data, for example, the reference data may include the results of measurements using X-rays on a specified reference sample, or predicted data of the results of measurements using X-rays obtained through simulations related to the reference sample. The reference sample is, for example, a sample having a known structure, composition, valence, chemical bonding state, etc. The reference sample is preferably, for example, a monomer sample having a purity of a predetermined value or more (e.g., 99.99% or more). In addition, simulations related to the reference sample may include, for example, any simulations such as first-principles calculations, band calculations, molecular orbital method simulations, and molecular dynamics simulations assuming a certain specified system.

[0016] <X-ray measurement device 2> Figure 2 FIG. is a diagram showing a structural example of the X-ray measurement device 2. The X-ray measurement device 2 is configured to spectroscopically analyze and detect the fluorescent X-rays generated by irradiating a sample S with X-rays, and generate the detection result as object measurement data. In the present embodiment, as an example, the X-ray measurement device 2 is configured to measure X-rays based on X-ray emission spectroscopy (XES) in a manner capable of analyzing information related to the microscopic chemical state such as the valence or spin of the elements constituting the sample S. Specifically, for example, the X-ray measurement device 2 includes an X-ray irradiation unit 21, a sample stage 22, two spectroscopic crystals 23a and 23b, and an X-ray detection unit 24.

[0017] The X-ray irradiation unit 21 is configured to irradiate X-rays X1. The X-ray irradiation unit 21 can irradiate characteristic X-rays from the target by irradiating thermoelectrons from an electron gun to targets such as Cu, Mo, W, and Rh. Hereinafter, for the sake of convenience of explanation, the X-rays X1 irradiated from the X-ray irradiation unit 21 are referred to as incident X-rays X1.

[0018] The sample stage 22 is configured to be able to set the sample S to be analyzed. The sample S placed on the sample stage 22 is arranged to be irradiated with the incident X-rays X1. The sample S irradiated with the incident X-rays X1 outputs fluorescent X-rays X2 corresponding to the constituent elements of the sample S.

[0019] Two spectroscopic crystals 23a and 23b are configured to form a dual-crystal spectrometer. For example, the first spectroscopic crystal 23a is configured to reflect the fluorescent X-rays X2 generated from the sample S. The second spectroscopic crystal 23b is configured to further reflect the fluorescent X-rays X2 reflected by the first spectroscopic crystal 23a. Each spectroscopic crystal 23a and 23b is configured to adjust the reflection angle of the fluorescent X-rays X2 by rotation. The two spectroscopic crystals 23a and 23b are, for example, crystals made of the same material, and can selectively reflect specific wavelengths according to the incident angle of the fluorescent X-rays X2 by Bragg reflection. Thus, by appropriately adjusting the reflection angle of the fluorescent X-rays X2 formed by the first spectroscopic crystal 23a and the second spectroscopic crystal 23b, monochromatic light of the desired energy band can be separated from the fluorescent X-rays X2 generated from the sample S. Furthermore, the specific configuration of the optical system used to separate the fluorescent X-rays X2 is not limited to this and is arbitrary.

[0020] The X-ray detection unit 24 is configured to detect X-rays that have passed through the sample S, and thereby output the measurement results of the X-rays. The specific configuration of the X-ray detection unit 24 is arbitrary; a proportional counter, a scintillation counter, a semiconductor detector, etc., can be used. In this embodiment, the X-ray detection unit 24 is configured to detect the component of the fluorescent X-rays X2 generated from the sample S due to the incident X-rays X1, which is dispersed by the spectroscopic crystals 23a and 23b.

[0021] <Information Processing Device 3> Figure 3 This is a block diagram showing the hardware structure of the information processing device 3. The information processing device 3 includes a communication bus 30, a communication unit 31, a storage unit 32, a processor 33, a display unit 34, and an input unit 35. These components are electrically connected inside the information processing device 3 via the communication bus 30.

[0022] The communication unit 31 preferably uses wired communication units such as USB, IEEE 1394, Thunderbolt (registered trademark), and wired LAN network communication, but may also include wireless LAN network communication, 3G / LTE / 5G mobile communication, BLUETOOTH (registered trademark) communication, etc., as needed. That is, it is more preferable to implement it as a collection of these multiple communication units. In other words, the information processing device 3 can also transmit various information from the outside via the communication unit 31 and the network.

[0023] Storage unit 32 stores various information defined as described above. It can be implemented, for example, as a storage device such as a solid-state drive (SSD) that stores various programs related to the information processing device 3 executed by processor 33, or as a memory such as random access memory (RAM) that stores temporarily needed information (arguments, arrays, etc.) related to program operations. Storage unit 32 stores various programs, variables, etc., related to the information processing device 3 executed by processor 33.

[0024] The processor 33 performs processing / control of the overall operations associated with the information processing device 3. The processor 33 is, for example, a central processing unit (CPU) not shown. The processor 33 implements various functions related to the information processing device 3 by reading a predetermined program stored in the storage unit 32. That is, information processing based on software stored in the storage unit 32 is specifically implemented by the processor 33, which is an example of hardware, and thus can be executed as various functional units included in the processor 33. These aspects will be further detailed in the next section. Furthermore, the processor 33 is not limited to a single processor; it can also be implemented with multiple processors 33 for each function. Alternatively, a combination of them is also possible.

[0025] The processor 33 is configured as an acquisition unit to acquire information from the X-ray measuring device 2 or other devices. The processor 33 is configured to acquire various types of information by reading various information stored in a storage area that is at least a part of the storage unit 32, and writing the read information into a working area that is at least a part of the storage unit 32. The storage area refers, for example, to an area in the storage unit 32 implemented as a storage device such as an SSD. The working area refers, for example, to an area implemented as a memory such as RAM. Furthermore, the acquisition based on the processor 33 includes acquiring the output results of each functional unit included in the processor 33.

[0026] The processor 33 is configured to display various information as a display processing unit. This information can be presented to the user via the display unit 34 (described later) or other devices. In this case, for example, the processor 33 controls the display unit 34 of the information processing device 3 to display screens, including still or moving images, icons, messages, and other visual information. The processor 33 may also generate only rendering information for the information processing device 3 to display visual information. Furthermore, the processor 33 may present the output information to the user without going through the information processing device 3 or other devices.

[0027] The display unit 34 can be a display unit included in the housing of the information processing device 3, or it can be an external display unit. The display unit 34 displays a screen of a user-operable graphical user interface (GUI). For example, it is preferable to use a display device such as a CRT monitor, liquid crystal display, organic EL monitor, or plasma display, depending on the type of information processing device 3.

[0028] The input unit 35 is configured to accept input from the user. The input unit 35 can be an input unit included in the housing of the information processing device 3, or it can be an external input unit. For example, the input unit 35 can be integrated with the display unit 34 and implemented as a touch panel. If it is a touch panel, the user can input click operations, swipe operations, etc. Of course, a switch button, mouse, QWERTY keyboard, voice recognition device, gesture detection device, gaze detection device, biometric signal detection device, camera device, etc., can be used instead of a touch panel. That is, the input unit 35 accepts operation input from the user. The input unit 35 responds with a signal corresponding to the operation input and transmits it to the processor 33 via the communication bus 30. The processor 33 can perform prescribed control or calculation as needed.

[0029] 2. Regarding information processing This section describes the information processing performed in the aforementioned information processing system 1.

[0030] 2.1. Information Processing Flow Figure 4 This is an activity diagram illustrating an example of the information processing flow performed in information processing system 1. Furthermore, this information processing may include any exception handling not shown. Exception handling includes interruption of the information processing and omission of certain processes. Selections or inputs made during this information processing may be based on user actions or may be performed automatically without user intervention.

[0031] [Activity A1] First, in activity A1, processor 33 sends measurement-related instructions to X-ray measuring apparatus 2. These instructions may include any information related to the incident X-ray X1 irradiating the sample S, such as the irradiation intensity, irradiation time, and irradiation position. Additionally, the instructions may include information related to the rotation speed of the spectrophotometers 23a and 23b (in other words, the cumulative time or step width of fluorescent X-rays X2 in a certain energy band). In other words, the measurement-related instructions may include the measurement sequence. Furthermore, the instructions related to the start of the measurement may include instructions related to this process.

[0032] [Activity A2] Next, in activity A2, the X-ray measuring apparatus 2 performs a measurement on the sample S based on the measurement-related instructions sent from the processor 33. As a result, the X-ray measuring apparatus 2 generates the results detected by the X-ray detection unit 24 as object measurement data. Object measurement data represents the results of X-ray measurements performed on the sample S, which is the object of analysis. The results of X-ray measurements, for example, represent the results of X-ray spectral measurements. In this embodiment, the X-ray measuring apparatus 2 is configured to perform XES-based measurements, and the object measurement data is spectral data obtained as the XES-based measurement results. This spectral data includes the energy (in other words, wavelength) of the X-rays detected by the X-ray detection unit 24 corresponding to the angles of the spectroscopic crystals 23a and 23b, and the number of photons (count) of each energy of X-ray detected by the X-ray detection unit 24.

[0033] [Activity A3] Subsequently, in activity A3, processor 33 causes display unit 34 to display analysis screen IM1 (see reference). Figure 6 The analysis uses the IM1 screen to receive various operations from the user.

[0034] [Activity A4] Next, in activity A4, processor 33 sends instructions to X-ray measuring device 2 to perform the import of object measurement data.

[0035] [Activity A5] Next, in activity A5, the X-ray measuring device 2 sends the object measurement data generated by the X-ray detection unit 24 to the information processing device 3 based on the data acquisition command sent.

[0036] [Activity A6] Next, in activity A6, processor 33 acquires object measurement data sent from X-ray measuring device 2. Furthermore, the information source for processor 33 is not limited to information processing device 3; other devices such as storage unit 32 storing object measurement data can also be used as information sources.

[0037] [Event A7] Next, in activity A7, processor 33 accepts the designation of reference data. For example, processor 33 accepts user input to the UI displayed on the analysis screen IM1 as the designation of reference data.

[0038] [Event A8] Next, in activity A8, processor 33 acquires at least one reference data. Processor 33 may acquire the specified reference data, for example, by reading it from a database DB, or by acquiring information pre-stored in a storage unit 32, etc., as the reference data. The reference data is configured to be comparable to object measurement data. For example, the reference data may be configured to represent the relationship between the energy of an X-ray and the number of photons in an X-ray of that energy.

[0039] [Event A9] Next, in activity A9, processor 33 sets the objective function for performing nonnegative matrix factorization based on the specified reference data. The objective function is an index that becomes the object to be maximized (or minimized) during optimization. The objective function comprises a linear combination of at least one variable basis and at least one fixed basis. The fixed basis corresponds to the specified reference data. The components of the fixed basis are set as parameters that are fixed during nonnegative matrix factorization. The components of the variable basis are set as parameters that are variable during nonnegative matrix factorization.

[0040] Here, we will illustrate an example of how to set the objective function. Figure 5 This is a graph representing the relationship between the object measurement data, the variable substrate, and the fixed substrate included in the objective function. Here, the object measurement data is the spectral data representing the result of measuring the number of photons in the fluorescence X-ray X2 in M ​​energy bands. The processor 33 processes the set of object measurement data from measurements performed under N different measurement conditions as an N×M matrix. Here, the set of object measurement data is represented as matrix X. Figure 5 In the matrix X, "n" represents the measurement data of the nth object, and V in the matrix X represents the number of photons per energy of the measurement data of the nth object. Furthermore, assuming that there is only one measurement data point, the processor 33 can process the 1×m matrix as a matrix X representing the set of measurement data of the object.

[0041] Next, the processor 33 sets a fixed substrate based on reference data, which is obtained from a database such as DB based on a specified reference. First, the processor 33 obtains the number of photons of fluorescence X-rays X2 in the M energy bands included in the object measurement data so that the reference data can be compared with the object measurement data. The processor 33 obtains, for example, the data points included in the spectrum included in the reference data, or the estimated value calculated based on the data points using spline interpolation or trapezoidal approximation. Furthermore, the photon number at this time is a relative value in the whole spectrum, and for example, it can be standardized in such a way that the maximum value is a specified value. For example, if S reference data related to different reference samples are specified, the processor 33 processes the set of R reference data as an S×M matrix. In this way, the processor 33 processes the spectrum obtained based on the reference data as a fixed substrate. For example, if S reference data are specified, the processor 33 sets a total of S fixed substrates based on the reference data and processes these fixed substrates as an S×M matrix B'. Matrix B' constitutes a set of fixed substrates, and the sequence of numbers in each row of matrix B' represents the spectrum of the reference data of the specified reference sample.

[0042] Next, an approximation matrix Y is generated relative to the measured data of the object. The approximation matrix Y consists of a linear combination of a variable basis and a fixed basis. For example, the approximation matrix Y may include the following terms. [Mathematical Expression 1]

[0043] Here, B represents the set of bases treated as variable parameters during the optimization of the objective function described later, and can be expressed as an R×M matrix. Here, R is an integer greater than or equal to 1, corresponding to the number of unspecified components contained in the sample S. The elements of the R×M matrix corresponding to matrix B are treated as variable parameters during optimization, as described later. W and W' are the coefficient matrices of matrix B corresponding to the variable basis and matrix B' corresponding to the fixed basis, respectively, and their elements are treated as variable parameters during optimization, as described later. For example, the coefficient matrix W of matrix B corresponding to the variable basis is formulated using an N×R matrix, and the coefficient matrix W' of matrix B' corresponding to the fixed basis is formulated using an N×S matrix. Furthermore, the approximation matrix Y may also include other elements such as a constant matrix.

[0044] Next, the processor 33 sets an objective function Z based on the established approximation matrix Y. The objective function Z is configured to approximate the matrix X representing the measured data of the object by means of the aforementioned approximation matrix Y. For example, the objective function Z can be defined by the following norm. [Mathematical Expression 2]

[0045] Furthermore, the specific form of the norm is arbitrary; for example, the L1 norm (Manhattan distance) and L2 norm (Euclidean distance) can be used. Thus, the objective function Z can be set to include a linear combination of a variable basis and a fixed basis, where at least the coefficients of the variable basis become variable parameters. Consequently, as the approximate matrix Y approaches the matrix X corresponding to the measured data through optimization, the objective function Z decreases, ideally becoming zero. At this point, the coefficient matrices W and W' in the objective function Z, and the elements included in the matrix B corresponding to the variable basis, are treated as variable parameters during optimization, while the elements included in the matrix B' corresponding to the fixed basis are treated as fixed values. Furthermore, the elements of each matrix X, B, B', W, and W' are defined as nonnegative. Therefore, the objective function Z is set to decompose matrix X into nonnegative matrix factorization of B and B' as nonnegative matrix factors.

[0046] [Activity A10] Back Figure 4 Following activity A9, in activity A10, processor 33 performs optimization based on nonnegative matrix factorization using the set objective function Z. Here, processor 33 adjusts the variable parameters by decreasing the value of the objective function Z. When the objective function Z satisfies the specified optimization conditions, optimization is considered complete. Processor 33 outputs the parameter whose value of the objective function Z is closest to 0 among the parameters until optimization is complete as the optimal solution. Specifically, processor 33 optimizes the objective function Z of the matrix X corresponding to the object measurement data by setting variable parameters and fixed values ​​as described above, under the constraint that at least a fixed basis component (corresponding to the elements of matrix B') is fixed in the objective function Z. This results in nonnegative matrix factorization of the matrix X corresponding to the object measurement data. The specific method of the search algorithm for the optimal solution achieved by adjusting the variable parameters is arbitrary; for example, local search, iterative improvement, and neighborhood search methods can be used. Furthermore, metaheuristic search methods such as cuckoo search, genetic algorithms, and particle swarm optimization can also be employed as the optimization algorithm.

[0047] [Event A11] Next, in activity A11, the processor 33 causes the display unit 34 to display visual information representing the optimization results in activity A10. In this embodiment, the processor 33 displays this visual information on the analysis screen IM1.

[0048] Based on the above structure, the information of known reference samples can be utilized to the maximum extent, and the search range of parameters during optimization can be reduced by subtracting the composition of the fixed substrate. Therefore, when analyzing results from X-ray measurements, the processing load of performing nonnegative matrix factorization on the target measurement data can be reduced. In particular, nonnegative matrix factorization can be applied even when the repetition of multiple X-ray peaks that may be included in the target measurement data is high. Therefore, especially for measurement data with repetitive X-ray peaks, the processing load can be reduced while effectively utilizing the characteristics of nonnegative matrix factorization.

[0049] 2.2. An example of a visual analysis. In this section, the above analysis will be illustrated using an example of screen IM1. Figure 6 This is an example of an analysis screen IM1. Here, as an example, the object measurement data represents the results of measurements performed on heat-treated Si-based materials using XES. Figure 6 As shown, the analysis screen IM1 includes, for example, an input button 4, a setting area 5, a specified receiving area 6, an execution button 7, a quantitative information area 8, a result display area 9, a base display area 10, a residual area 11, and a score display area 12.

[0050] The read button 4 is configured to send an instruction to import object measurement data based on the user's operation. When the read button 4 is operated, the processor 33 first accepts the specified object measurement data to be imported and imports the specified object measurement data.

[0051] Setting area 5 is a UI configured to accept and specify conditions related to optimization based on nonnegative matrix factorization. Setting area 5, for example, is configured to specify the number of components contained in sample S (“Num component”) and the number of optimization iterations (“Num iteration”). In this embodiment, setting area 5 may include a display setting area 51. Display setting area 51 is configured to specify whether to visually display the results of a measurement using X-rays corresponding to a variable substrate or the simulation results associated with that measurement.

[0052] The designated acceptance area 6 is an example of visual information configured to provide a list of candidate reference data based on the acquired reference data. The designated acceptance area 6 includes the name of the reference data (“Date Name”) and a checkbox (“Use for supervisor”) that allows input of a candidate reference data. The processor 33 processes the reference data with the checkbox selected as the designated reference data. Thus, the processor 33 accepts the designation from the user by displaying the designated acceptance area 6. In this embodiment, as an example, the processor 33 displays the spectral patterns of a-Si (amorphous Si), a-SiO2 (amorphous SiO2), and c-Si (crystalline silicon) as candidate reference data, and accepts the designation of the spectral patterns of these three substances.

[0053] The execute button 7 is the UI for optimizing the objective function. The processor 33 sets the objective function Z according to the conditions set in the setting area 5 and the designated receiving area 6 when the user operates the execute button 7, and starts optimization with the maximum number of repetitions input to "Numiteration" as the upper limit.

[0054] The quantitative information area 8, the result display area 9, the basis display area 10, the residual area 11, and the fraction display area 12 are areas that are displayed during optimization and represent the optimization process or results.

[0055] Quantitative Information Area 8 is a region that quantitatively displays the results of the nonnegative matrix factorization of matrix X, defined by the set of object measurement data. Each row displayed in Quantitative Information Area 8 corresponds to an object measurement data stored in a row of matrix X. The "Date Name" in Quantitative Information Area 8 indicates the filename of each object measurement data. The "Component" in Quantitative Information Area 8 indicates an element of the coefficient matrix W of the variable basis, i.e., a quantity related to unknown components other than those represented by the fixed basis in the composition of sample S, such as composition ratios.

[0056] The results display area 9 is an example of visual information representing the result of the nonnegative matrix factorization of matrix X corresponding to the object measurement data. The results display area 9 is configured to display, in a mutually distinguishable manner, a first contribution associated with a variable basis in the object measurement data and a second contribution associated with a fixed basis, obtained as a result of the nonnegative matrix factorization. For example, the results display area 9 may include the spectrum of the optimized variable basis (“Component”) as the first contribution and the respective spectra of the optimized fixed basis (“a-Si”, “a-SiO2”, “c-Si”) as the second contribution. These contributions are displayed in the results display area 9, for example, as curves that can be visually distinguished by line type, color, thickness, etc. Alternatively, the processor 33 can erase one of the first and second contributions (e.g., only the second contribution) so that the display unit 34 displays the first and second contributions in a visually distinguishable manner in the results display area 9.

[0057] The results show that region 9 can also visually display the spectrum of the object measurement data (“Measurement date”) and the optimized spectrum (“fitting”) corresponding to the approximate matrix Y. Based on this structure, it is possible to compare the spectrum of the object measurement data and the optimized spectrum while simultaneously viewing the contribution of each component.

[0058] The substrate display area 10 is configured to overlay the spectra of the variable substrate and the fixed substrate as a graph. This structure makes it easy to determine the relationship between the spectrum of the substrate and the spectrum of the object measurement data.

[0059] The horizontal axis of the results display area 9 and the substrate display area 10 represents, for example, the values ​​of parameters in the measurement conditions using X-rays (e.g., the energy of fluorescent X-ray X2, scattering angle, etc.). The vertical axis of the results display area 9 and the substrate display area 10 represents the detection results of the X-ray detection unit 24, such as the number of photons. Here, the vertical axis represents the quantity corresponding to the number of photons, i.e., the intensity of the X-rays. Furthermore, the intensity of the X-rays here is a relative intensity.

[0060] The substrate display area 10 is preferably configured to be viewable in conjunction with the result display area 9. In particular, the horizontal axis of the substrate display area 10 is preferably displayed in a manner corresponding to the horizontal axis of the result display area 9. With this configuration, it is easier to compare the test data and each substrate. Furthermore, the scale of the vertical axis in the substrate display area 10 is preferably configured to be set independently in a manner different from the scale of the result display area 9. With this configuration, the spectra of minute components that may be included in the test data can be obtained by expanding the substrate display area 10.

[0061] The residual region 11 is configured to visually display the difference between the spectrum of the measured object data and the spectrum of the optimized approximation matrix Y. This difference is an example of information related to the shift of an index representing the accuracy of optimization, such as the difference between the spectrum of the measured object data and the spectrum of the optimized approximation matrix Y, i.e., the residual. The residual region 11 is configured to visually represent this residual as a spectrum. The residual region 11 includes, for example, a baseline 111 and a residual spectrum 112. The baseline 111 represents a graph where the residual is 0 (e.g., a line graph). The residual spectrum 112 is a graph configured to represent this difference for each band. By comparing the position of the residual spectrum 112 relative to the baseline 111 for each band, the user can analyze in which band the optimization accuracy is lowest. The residual region 11 can be displayed in a way that is in view of the results display area 9 and / or the substrate display area 10. In this case, the horizontal axis of the residual region 11 is displayed in a way that corresponds to the horizontal axis of the results display area 9 and / or the substrate display area 10.

[0062] The fraction display area 12 is configured to represent, for example, the change in the objective function Z, which is related to the shift of information as an indicator of the accuracy of optimization during the optimization process. The fraction display area 12 can be updated by visually displaying the latest value of the objective function Z during the optimization process. Based on this structure, it is easy to determine whether optimization is progressing smoothly, and therefore, if the variable parameters of the objective function Z get trapped in a local optimum, the user can be prompted to interrupt the optimization. The horizontal axis of the fraction display area 12 represents the number of iterations, and the vertical axis represents the value of the objective function Z at that number of iterations. The fraction display area 12 may include a theoretical value 121 and a shift curve 122. The theoretical value 121 represents the value when the objective function Z is 0, i.e., when the approximate matrix Y and the matrix X corresponding to the measured data of the object are strictly consistent. The shift curve 122 shows the shift of the objective function Z with changes in the number of iterations. Under successful optimization, the value of the objective function Z gradually converges towards the theoretical value 121 as the number of iterations increases. On the other hand, when optimization fails to proceed smoothly due to local optima or fluctuations in the value of the objective function Z, the transition curve 122 takes on a different shape than when optimization proceeds smoothly. This difference in the shape of the transition curve 122 can prompt the user whether optimization should be interrupted.

[0063] In this way, the processor 33 visually displays information related to the shift of indicators representing the accuracy of optimization through the residual region 11 or the fractional display region 12. Based on this structure, it is possible to visually grasp problems such as optimization remaining at a local optimum or completing tasks with insufficient accuracy.

[0064] 2.3. An example of optimization Next, an example of the compositional analysis results of Si-based materials based on the above information processing method will be explained. Figure 7 This is a graph showing the compositional analysis results of SiO-based anode materials for lithium secondary batteries based on this information processing. Figure 8 The results of the compositional analysis of the SiO-based anode material for a lithium-ion secondary battery, based on linear combination fitting (LCF), are presented as a comparative example. The optimization used to obtain the compositional analysis results was performed on the basis of including at least amorphous Si, amorphous SiO2, and crystalline Si, with the number of unknown components assumed to be 1. Furthermore, the sample S, which is the object of X-ray measurement, was obtained by subjecting the SiO-based anode material for the lithium-ion secondary battery to a prescribed heat treatment.

[0065] and Figure 7 and Figure 8 The sample S corresponding to pattern 1 is obtained without heating the SiO anode material of the lithium secondary battery. The samples S corresponding to patterns 2 to 6 are obtained by heating the SiO anode material of the lithium secondary battery in air at heating temperatures of 700℃, 800℃, 900℃, 1000℃, and 1100℃, respectively.

[0066] like Figure 7 and Figure 8 As shown, both the compositional analysis results based on this information processing method and the compositional analysis results based on LCF include amorphous Si, amorphous SiO2, and crystalline Si, and also include unknown components. In the compositional analysis based on this information processing method, assuming the number of unknown component types, the spectrum corresponding to each unknown component (corresponding to each row of matrix B) and its contribution (corresponding to the elements of the coefficient matrix) are optimized as variable parameters to determine the spectrum and contribution of the unknown component. Here, the number of unknown component types is set to 1. Figure 7 In this process, the unknown component is denoted as "Component_1". Furthermore, in LCF-based component analysis, after determining the spectrum corresponding to the unknown component using a pre-defined analytical method, optimization is performed while fixing the state of that spectrum, thereby determining the contribution of the unknown component. Figure 8 In this context, the unknown component in LCF-based optimization is denoted as "peak 1".

[0067] The two-component analysis results show that the composition of amorphous Si and the unknown composition both decrease with increasing heating temperature. On the other hand, the composition of amorphous SiO2 and crystalline Si increases with increasing heating temperature. Thus, the compositional analysis results based on this information processing show the same trend as those based on LCF, which is frequently used, especially when peak repetition is high. Therefore, although the information processing method is based on an optimization algorithm of nonnegative matrix factorization, it demonstrates that it provides analysis results with no less accuracy than LCF, even when the repetition of spectra derived from each component is high.

[0068] [other] The above implementation method can also be modified as follows.

[0069] In the above embodiments, the optimized execution entity performed in activity A10 is not limited to processor 33, but can also be any device different from information processing device 3.

[0070] In the above embodiment, the fluorescent X-ray X2 detected by the X-ray detection unit 24 is obtained by irradiating the incident X-ray X1 from the X-ray irradiation unit 21, but is not limited thereto. For example, the fluorescent X-ray X2 can also be obtained by irradiating the sample S with an electron beam of a certain energy or higher. Specifically, for example, the X-ray measuring device 2 can also be a SEM-EDS system. In addition, the specific method of the X-ray measurement performed by the X-ray measuring device 2 is arbitrary, for example, measurements based on X-ray fluorescence analysis (XRF), X-ray emission spectroscopy (XES), X-ray photoelectron spectroscopy (XPS), or X-ray absorption spectroscopy (XAFS) can be cited. The specific configuration of the optical system of the X-ray measuring device 2 can be appropriately changed according to the type of X-ray measurement.

[0071] In this embodiment, the information processing device 3 functions as both a measurement terminal that sends instructions related to the measurement performed by the X-ray measuring device 2 and an analysis terminal for analyzing measurement data of the object. However, the analysis terminal may also be separate from the measurement terminal.

[0072] The information processing device 3 can be deployed locally (on-premises) or in the cloud. As a cloud-based information processing device 3, it can provide the above-mentioned functions and processing in a manner such as SaaS (Software as a Service) or cloud computing.

[0073] In the above embodiments, the information processing device 3 performs various storage / control functions, but multiple external devices can also be used instead of the information processing device 3. That is, various information or programs can also be distributed and stored in multiple external devices using technologies such as blockchain.

[0074] The above-described embodiments are not limited to information processing system 1, but may also be information processing methods or information processing programs. An information processing method includes the steps of information processing system 1. A program causes at least one computer to execute the steps of information processing system 1.

[0075] The aforementioned information processing system 1, etc., may also be provided in the manner described below.

[0076] (1) An information processing system comprising at least one processor configured to execute a program in a manner that performs the following steps: in an acquisition step, acquiring object measurement data and at least one reference data representing the results of X-ray measurement of a sample being analyzed; in a designation and acceptance step, accepting the designation of the reference data; and in a setting step, setting an objective function for performing nonnegative matrix factorization, comprising a linear combination of at least one variable basis and at least one fixed basis, based on the designation, wherein the fixed basis corresponds to the designated reference data, the components of the variable basis are set as parameters that are variable when performing the nonnegative matrix factorization, and the components of the fixed basis are set as parameters that are fixed when performing the nonnegative matrix factorization.

[0077] Based on this structure, the information from known reference samples can be utilized to the maximum extent, and the search range of parameters during optimization can be reduced by subtracting the composition of the fixed substrate. Therefore, when analyzing X-ray measurements, the processing load of nonnegative matrix factorization of the target measurement data can be reduced. In particular, nonnegative matrix factorization is applicable even when the overlap of multiple X-ray peaks contained in the target measurement data is high. Thus, while utilizing the properties of nonnegative matrix factorization, the processing load can be reduced, especially for measurement data with overlapping X-ray peaks.

[0078] (2) The information processing system according to (1) above, wherein the reference data includes the results of the X-ray measurement performed on a specified reference sample, or a prediction of the results of the X-ray measurement obtained by simulation related to the reference sample.

[0079] (3) The information processing system according to (1) or (2) above, wherein the objective function further includes a linear combination of the variable basis and the fixed basis, and at least the coefficients of the variable basis and the variable basis are set as variable parameters.

[0080] (4) The information processing system according to any one of (1) to (3) above, wherein, in the designation acceptance step, the designation is accepted from the user by displaying visual information that constitutes candidates for designable reference data based on the acquired reference data overview.

[0081] (5) The information processing system according to any one of (1) to (4) above, wherein, in the optimization step, the objective function is optimized on the matrix corresponding to the object measurement data under the constraint of fixing at least the components of the fixed basis in the objective function, and the matrix corresponding to the object measurement data is decomposed into a non-negative matrix factorization.

[0082] (6) The information processing system according to (5) above, wherein, in the analysis and display step, information related to the shift of the index representing the optimized accuracy is visually displayed.

[0083] (7) The information processing system according to any one of (1) to (6) above, wherein the result of the X-ray measurement represents the result of the X-ray spectral measurement.

[0084] (8) The information processing system according to any one of (1) to (7) above, wherein the measurement results of the X-rays are used to represent the results of measurements based on fluorescence X-ray analysis (XRF), X-ray emission spectroscopy (XES), X-ray photoelectron spectroscopy (XPS), or X-ray absorption spectroscopy (XAFS).

[0085] (9) The information processing system according to any one of (1) to (8) above, wherein, in the result display step, result visual information representing the result of the non-negative matrix factorization is displayed, the result visual information displaying, in a manner distinguishable from each other, the first contribution related to the variable basis and the second contribution related to the fixed basis in the object measurement data obtained as a result of the non-negative matrix factorization.

[0086] (10) The information processing system according to any one of (1) to (9) above, further comprising an X-ray measuring device, the X-ray measuring device comprising an X-ray irradiation unit, a sample stage and an X-ray detection unit, the X-ray irradiation unit being configured to irradiate the X-rays, the sample stage being configured to be a sample that can be set as the object of analysis, and the X-ray detection unit being configured to detect the X-rays that have passed through the sample, thereby outputting the result of the X-ray measurement.

[0087] (11) An information processing method, comprising each step of the information processing system described in any one of (1) to (10) above.

[0088] (12) A program that causes at least one computer to perform the steps of the system described in any one of (1) to (10) above. Of course, this is not the only one.

[0089] Finally, various embodiments of the invention have been described, but these descriptions are provided by way of example and are not intended to limit the scope of the invention. This novel embodiment can be implemented in various other ways, with various omissions, substitutions, and modifications possible without departing from the spirit of the invention. This embodiment and its variations are included within the scope and spirit of the invention, and are included within the scope of the invention as described in the claims and its equivalents. Explanation of reference numerals in the attached figures

[0090] 1: Information Processing System 2: X-ray measuring device 21: X-ray irradiation section 22: Sample stage 23a: First Spectroscopic Crystal 23b: Second spectroscopic crystal 24: X-ray Inspection Department 3: Information processing device 30: Communication bus 31: Ministry of Communications 32: Storage Department 33: Processor 34: Display Section 35: Input Section 4: Read button 5: Set area 51: Display setting area 6: Specify the receiving area 7: Execute button 8: Quantitative Information Area 9: Results display area 10: Substrate display area 11: Residual Area 111: Baseline 112: Residual Spectrum 12: Score display area 121: Theoretical value 122: Trend Chart DB: Database IM1: Analysis screen S: Sample X1: Incident X-rays X2: Fluorescent X-ray.

Claims

1. An information processing system, wherein, Equipped with at least one processor The processor is configured to execute the program in a manner that performs the following steps. In the acquisition step, object measurement data and at least one reference data are acquired, representing the results of X-ray measurements applied to the sample being analyzed. In the designated acceptance step, the specified reference data is accepted. In the setting step, based on the specified parameters, an objective function is set that includes a linear combination of at least one variable basis and at least one fixed basis for performing nonnegative matrix factorization. Here, the fixed base corresponds to the specified reference data respectively. The components of the variable basis are set as parameters that are variable during the nonnegative matrix factorization. The components of the fixed base are set to parameters that are fixed during the nonnegative matrix factorization.

2. The information processing system according to claim 1, wherein, The reference data includes the results of measurements using the X-rays performed on a specified reference specimen, or predictions of the results of measurements using the X-rays obtained through simulations related to the reference specimen.

3. The information processing system according to claim 1 or claim 2, wherein, The objective function also includes a linear combination of the variable basis and the fixed basis, wherein at least the coefficients of the variable basis and the variable basis are set as variable parameters.

4. The information processing system according to any one of claims 1 to 3, wherein, In the designated acceptance step, the user accepts the designation by displaying visual information that constitutes a list of candidates for the designated reference data based on the acquired reference data.

5. The information processing system according to any one of claims 1 to 4, wherein, Furthermore, in the optimization step, the objective function is optimized on the matrix corresponding to the object measurement data under the constraint of fixing at least the components of the fixed basis in the objective function, and non-negative matrix factorization of the matrix corresponding to the object measurement data is performed.

6. The information processing system according to claim 5, wherein, Further in the analysis and display step, information related to the shift of the index representing the optimized accuracy is visually displayed.

7. The information processing system according to any one of claims 1 to 6, wherein, The results of the measurements using the X-rays represent the results of the X-ray spectral measurements.

8. The information processing system according to any one of claims 1 to 7, wherein, The X-ray measurements used represent results based on X-ray fluorescence analysis (XRF), X-ray emission spectroscopy (XES), X-ray photoelectron spectroscopy (XPS), or X-ray absorption spectroscopy (XAFS).

9. The information processing system according to any one of claims 1 to 8, wherein, Furthermore, in the results display step, visual information representing the results of the nonnegative matrix factorization is displayed. The resulting visual information displays, in a mutually distinguishable manner, the first contribution related to the variable basis and the second contribution related to the fixed basis in the object measurement data obtained as a result of the nonnegative matrix factorization.

10. The information processing system according to any one of claims 1 to 9, wherein, It also has an X-ray measuring device. The X-ray measuring device includes an X-ray irradiation unit, a sample stage, and an X-ray detection unit. The X-ray irradiation unit is configured to irradiate the X-rays. The sample stage is configured to be used as the sample to be analyzed. The X-ray detection unit is configured to detect X-rays that have passed through the sample, thereby outputting the measurement result of the X-rays.

11. An information processing method, wherein, It includes each step of the information processing system according to any one of claims 1 to 10.

12. A program, wherein, To cause at least one computer to perform the steps of the system according to any one of claims 1 to 10.

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