Information processing method, computer program, information processing device, and radiation detection device

Abstract

Provided are an information processing method, a computer program, an information processing device, and a radiation detection device with which it is possible to analyze the characteristics of a sample.　The measurement spectrum of radiation produced from a radiation-exposed sample and detected using a radiation detector is acquired. A characteristic of the sample is assumed. Monte Carlo ray tracing using the assumed characteristic of the sample is performed for each of a plurality of photons included in the radiation to which the sample is exposed and radiation produced from the sample, whereby energy identified when each respective photon is detected is calculated. The number of photons is counted for each unit of calculated energy, whereby a virtual spectrum of the detected radiation is generated. The assumed characteristic of the sample is corrected in accordance with a comparison between the measurement spectrum and the virtual spectrum. The corrected characteristic is determined as a characteristic of the sample.

Description

Information processing method, computer program, information processing device, and radiation detection device

[0001] The present invention relates to an information processing method, a computer program, an information processing device, and a radiation detection device for analyzing characteristics of a sample.

[0002] A technique is used to analyze the distribution of elements contained in a sample by irradiating a sample with primary radiation, detecting secondary radiation generated from the sample, and analyzing the distribution of elements contained in the sample based on the spectrum of the secondary radiation. The radiation spectrum represents the relationship between the radiation energy and intensity. The energy of the secondary radiation is related to the type of element, and the intensity of the secondary radiation is related to the concentration of the element. The radiation is incident on a radiation detector and detected through interaction with the radiation detection element within the radiation detector. Ideally, the radiation spectrum should contain only peaks corresponding to the energies related to the elements contained in the sample. However, in reality, many peaks can occur due to various phenomena, such as phenomena caused by the radiation inside the sample or radiation detection element, and phenomena caused by the radiation impinging on other objects. Peaks caused by signal processing, such as so-called sum peaks, can also occur. In actual spectra, many peaks can overlap, making elemental analysis difficult.

[0003] Patent No. 3412606

[0004] Patent Document 1 discloses a technique for analyzing the characteristics of a condenser lens using a ray tracing technique in the field of particle size distribution measurement using the diffraction and scattering of laser light. In radiation detection, it is also desirable to use the ray tracing technique to analyze sample characteristics such as element distribution.

[0005] An object of the present invention is to provide an information processing method, a computer program, an information processing device, and a radiation detection device that are capable of analyzing the characteristics of a sample.

[0006] An information processing method according to the present invention is characterized in that it acquires a measured spectrum of radiation generated from a sample irradiated with radiation and detected using a radiation detector, assumes characteristics of the sample, performs Monte Carlo ray tracing using the assumed characteristics of the sample for each of a plurality of photons contained in the radiation irradiated to the sample and the radiation generated from the sample, calculates the energy specified when each photon is detected, counts the number of photons for each calculated energy, and generates a virtual spectrum of the detected radiation, modifies the assumed characteristics of the sample in accordance with a comparison between the measured spectrum and the virtual spectrum, and determines the modified characteristics as the characteristic of the sample.

[0007] In one aspect of the present invention, a process is performed in which sample characteristics such as elemental distribution are assumed, and the energy at which individual photons contained in radiation from the sample are detected is calculated using Monte Carlo ray tracing to generate a virtual spectrum of the radiation. The assumed sample characteristics are modified based on a comparison between the measured spectrum of radiation and the virtual spectrum. Calculations using Monte Carlo ray tracing and modification of the sample characteristics are repeated, and the results of Monte Carlo ray tracing of photons contained in the radiation emitted from the sample change based on the modified characteristics. The assumed sample characteristics can be modified so that the measured spectrum and the virtual spectrum approximately match, and the modified characteristics can be determined to be the characteristics of the actual sample.

[0008] The information processing method according to the present invention is characterized in that, in the Monte Carlo ray tracing, an object that interacts with each photon is identified, a type of interaction between each photon and the object is determined probabilistically, the energy or direction of travel of the photon that is generated or changed by the interaction is calculated probabilistically, and the energy of the photon that is detected as a result of the interaction between the photon and a radiation detection element included in the radiation detector is calculated.

[0009] In one aspect of the present invention, Monte Carlo ray tracing involves identifying an object that interacts with a photon and probabilistically determining the type of interaction. Furthermore, it involves probabilistically calculating the energy or direction of travel of a photon that is generated or changed by the interaction, and calculating the energy of a photon that is detected by the interaction. In this way, ray tracing of each photon contained in radiation is performed, and it is calculated what energy the photon ultimately has when it is detected.

[0010] The information processing method according to the present invention is characterized in that, in the Monte Carlo ray tracing, the flight distance of each photon is calculated probabilistically, and objects that interact with each photon are identified based on the position and size of objects onto which each photon may be incident and the flight distance.

[0011] In one aspect of the present invention, Monte Carlo ray tracing involves probabilistically calculating the flight distance of a photon and using the flight distance to identify an object that interacts with the photon. By using the probabilistically calculated flight distance, it becomes possible to easily perform processing to identify an object that interacts with the photon.

[0012] The information processing method according to the present invention is characterized in that it probabilistically determines whether the time interval at which multiple photons whose energies have been calculated are detected is short or not, and modifies the energy calculated using the Monte Carlo ray tracing by adding up the energies of multiple photons whose detection intervals are determined to be short, and setting the sum of the energies of the multiple photons whose detection intervals are determined to be short as the energy of a single photon.

[0013] In one aspect of the present invention, whether or not the time interval at which multiple photons are detected is short is determined probabilistically, and multiple photons detected at a short time interval are considered to be detected as a single photon, and the energy of the single photon is the sum of some or all of the energies of the multiple photons, thereby generating a virtual spectrum including a sum peak.

[0014] The information processing method according to the present invention is characterized in that the assumed characteristics of the sample are the composition, shape, structure of the sample, or the type, concentration, position, or distribution of elements contained in the sample.

[0015] In one aspect of the present invention, the characteristics of the sample include the composition, shape, and structure of the sample, or the type, concentration, location, or distribution of elements contained in the sample, which allows the composition, shape, and structure of the sample, or the type, concentration, location, or distribution of elements contained in the sample to be determined with high accuracy.

[0016] The information processing method according to the present invention is characterized in that the shape of the sample is specified, and Monte Carlo ray tracing is performed using assumed characteristics of the sample and the specified shape of the sample.

[0017] In one aspect of the present invention, the shape of the sample is identified before Monte Carlo ray tracing processing is performed. The trajectories of photons contained in radiation emitted from the sample are also affected by the shape of the sample. Therefore, the trajectories of photons contained in radiation emitted from the sample are calculated with high accuracy, and the characteristics of the sample are analyzed with high accuracy.

[0018] A computer program according to the present invention causes a computer to perform the following processes: calculate the energy specified when each photon is detected using a radiation detector by performing Monte Carlo ray tracing using assumed characteristics of the sample for each of the radiation irradiated to a sample and the multiple photons contained in the radiation generated from the sample; generate a virtual spectrum of the detected radiation by counting the number of photons for each calculated energy; modify the assumed characteristics of the sample in accordance with a comparison between the virtual spectrum and an actual measured spectrum of the radiation generated from the sample and detected using the radiation detector; and determine the modified characteristics as the characteristics of the sample.

[0019] In one aspect of the invention, a computer program determines the energy and intensity of the radiation using Monte Carlo ray tracing.

[0020] An information processing device according to the present invention includes a calculation unit that acquires a measured spectrum of radiation generated from a sample irradiated with radiation and detected using a radiation detector, assumes characteristics of the sample, and performs Monte Carlo ray tracing using the assumed characteristics of the sample for each of a plurality of photons contained in the radiation irradiated to the sample and the radiation generated from the sample to calculate the energy specified when each photon is detected, counts the number of photons for each calculated energy to generate a virtual spectrum of the detected radiation, and modifies the assumed characteristics of the sample in accordance with a comparison between the measured spectrum and the virtual spectrum, and determines the modified characteristics as the characteristic of the sample.

[0021] In one aspect of the present invention, a data processing device including an arithmetic unit and a parallel computing unit determines characteristics of a sample using Monte Carlo ray tracing.

[0022] In the information processing device according to the present invention, the calculation unit has a parallel calculation unit, and the parallel calculation unit executes in parallel a process of calculating, for each of a plurality of photons, the energy identified when each photon is detected by performing the Monte Carlo ray tracing.

[0023] In one aspect of the present invention, the calculation unit includes a parallel calculation unit, and the parallel calculation unit executes the Monte Carlo ray tracing process in parallel for a plurality of photons contained in the radiation, thereby executing the Monte Carlo ray tracing process for a large number of photons contained in the radiation at high speed.

[0024] A radiation detection apparatus according to the present invention is characterized by comprising an irradiation unit that irradiates a sample with radiation, a radiation detector that detects radiation from the sample, and the information processing apparatus according to the present invention.

[0025] In one aspect of the present invention, a radiation detection device includes an irradiation unit that irradiates a sample with radiation, a radiation detector that detects radiation from the sample, and an information processing device, which can accurately analyze the characteristics of the sample by processing to determine the characteristics of the sample using Monte Carlo ray tracing.

[0026] The present invention has excellent effects such as being able to analyze the characteristics of a sample with high precision.

[0027] 1 is a block diagram showing an example of the functional configuration of a radiation detection device. FIG. 2 is a schematic cross-sectional view showing an example of the configuration of a radiation detector. FIG. 3 is a schematic cross-sectional view showing an example of the configuration of a radiation detection element and a collimator. FIG. 4 is a block diagram showing an example of the internal configuration of an analysis unit. FIG. 5 is a diagram showing the spectrum of radiation having a single energy. FIG. 6 is a diagram showing an example of the spectrum of radiation generated when radiation having a single energy is generated from a sample. FIG. 7 is a flowchart showing an example of the processing procedure executed by the analysis unit. FIG. 8 is a flowchart showing an example of the processing procedure of Monte Carlo ray tracing. FIG. 9 is a graph showing an example of the measured spectrum of a Sn alloy containing Cd and the hypothetical spectrum of a Sn alloy not containing Cd. FIG. 10 is a graph showing an example of the measured spectrum of a Sn alloy containing Cd, the hypothetical spectrum of a Sn alloy not containing Cd, and the hypothetical spectrum of a Sn alloy containing Cd.

[0028] The present invention will be described in detail below with reference to the drawings illustrating embodiments thereof. FIG. 1 is a block diagram showing an example of the functional configuration of a radiation detection apparatus 100. The radiation detection apparatus 100 is, for example, an X-ray fluorescence analysis apparatus. The radiation detection apparatus 100 includes an irradiation unit 26 that irradiates a sample 4 with radiation such as an electron beam or X-rays, a sample stage 27 on which the sample 4 is placed, and a radiation detector 1. Radiation is irradiated from the irradiation unit 26 to the sample 4, causing radiation such as fluorescent X-rays to be generated in the sample 4, and the radiation detector 1 to detect the radiation generated from the sample 4. In the drawing, the radiation is indicated by arrows. Note that the radiation detection apparatus 100 may also be configured to hold the sample 4 by a method other than placing it on the sample stage 27.

[0029] The radiation detector 1 includes a radiation detection element 11 and a preamplifier 12. A part of the preamplifier 12 may be included inside the radiation detector 1, with the other part being arranged outside the radiation detector 1. The radiation detector 1 is connected to a voltage application unit 21 that applies a voltage required for radiation detection to the radiation detection element 11, and a signal processing unit 22. The voltage application unit 21 also applies a voltage required for the preamplifier 12 to operate, to the preamplifier 12. The signal processing unit 22 is connected to an analysis unit 3. The analysis unit 3 is configured using a computer.

[0030] The radiation detection device 100 includes a control unit 25. The control unit 25 is connected to the voltage application unit 21, the signal processing unit 22, the analysis unit 3, and the irradiation unit 26. The control unit 25 controls the operations of the voltage application unit 21, the signal processing unit 22, the analysis unit 3, and the irradiation unit 26. For example, the control unit 25 is configured using a computer having a calculation unit and a memory. The control unit 25 and the analysis unit 3 are connected to an operation unit 23 and a display unit 24. The operation unit 23 receives input of information such as text by receiving operations from the user. The operation unit 23 is, for example, a touch panel, a keyboard, or a pointing device. The display unit 24 displays images. The display unit 24 is, for example, a liquid crystal display or an electroluminescent display (EL display). The control unit 25 may be configured to receive operations from the user using the operation unit 23 and control each unit of the radiation detection device 100 in accordance with the received operations.

[0031] FIG. 2 is a schematic cross-sectional view showing an example of the configuration of the radiation detector 1. The radiation detector 1 is an SDD (Silicon Drift Detector). The radiation detector 1 includes a housing 16 shaped like a cylinder with a truncated cone connected to one end. The housing 16 includes a plate-shaped bottom plate covered with a cap-shaped cover. A window 161 made of a window material that transmits radiation is provided at the tip of the housing 16. The radiation detection element 11, the collimator 13, the circuit board 14, the Peltier element 15, and the cold finger 17 are arranged inside the housing 16. The housing 16 accommodates the radiation detection element 11, the collimator 13, the circuit board 14, and the Peltier element 15. The housing 16 accommodates the radiation detection element 11, physically protects the radiation detection element 11, and shields it from light. The shape of the housing 16 is not limited to the shape shown in FIG. 2 and may be other shapes.

[0032] The radiation detection element 11 is mounted on the surface of the circuit board 14 and is disposed in a position facing the window 161. The collimator 13 is cylindrical with both ends open and made of a radiation-shielding material. The collimator 13 is disposed between the radiation detection element 11 and the window 161. Radiation mainly passes through the window 161 and enters the inside of the housing 16, and the collimator 13 blocks part of the radiation. The radiation detection element 11 detects incident radiation that is not blocked by the collimator 13.

[0033] A circuit is formed on the circuit board 14, and the preamplifier 12 is mounted on it. The back surface of the circuit board 14 is in thermal contact with one end of the Peltier element 15, either directly or via an intervening object. The other end of the Peltier element 15 is in thermal contact with the cold finger 17. The cold finger 17 has a flat portion with which the other end of the Peltier element 15 is in thermal contact, and a portion that passes through the bottom plate of the housing 16. Heat from the radiation detection element 11 is absorbed by the Peltier element 15 through the circuit board 14. The heat is conducted from the Peltier element 15 to the cold finger 17 and dissipated to the outside of the radiation detector 1 through the cold finger 17.

[0034] The radiation detector 1 includes a plurality of lead pins 18 that pass through the bottom plate portion of the housing 16. The lead pins 18 are connected to the circuit board 14 by a method such as wire bonding. The application of voltage to the radiation detection element 11 by the voltage application unit 21 and the output of a signal from the preamplifier 12 are performed through the lead pins 18. The radiation detector 1 may further include other components.

[0035] FIG. 3 is a schematic cross-sectional view showing an example of the configuration of the radiation detection element 11 and the collimator 13. The radiation detection element 11 is a silicon drift type radiation detection element. The radiation detection element 11 is generally flat. The radiation detection element 11 includes a plate-shaped semiconductor portion 112 made of Si (silicon). The radiation detection element 11 has an incident surface 111 located on the incident side where radiation to be detected is incident, and an electrode surface 116 located on the back side of the incident surface 111. A portion of the incident surface 111 is covered with the collimator 13. The radiation detection element 11 is arranged so that the electrode surface 116 faces the circuit board 14 and the incident surface 111 faces the window 161. An electrode layer 113 is provided on a portion of the semiconductor portion 112 on the incident surface 111 side.

[0036] A signal output electrode 115, which is an electrode that outputs a signal when radiation is detected, and a plurality of curved electrodes 114 that are multiple annular in plan view are provided in a portion of the semiconductor portion 112 on the electrode surface 116 side. The plurality of curved electrodes 114 surround the signal output electrode 115, and the distance between the signal output electrode 115 and each curved electrode 114 varies. Although four curved electrodes 114 are shown in FIG. 3 , more curved electrodes 114 are actually provided. The innermost curved electrode 114 and the outermost curved electrode 114 are connected to a voltage application unit 21. The electrode layer 113 is also connected to the voltage application unit 21. When a voltage is applied from the voltage application unit 21 to the curved electrodes 114 and the electrode layer 113, an electric field (potential gradient) is generated inside the semiconductor portion 112, with the potential increasing toward the signal output electrode 115.

[0037] Radiation is irradiated from the irradiation unit 26 onto the sample 4. The irradiated sample 4 generates radiation consisting of characteristic X-rays such as fluorescent X-rays. The radiation generated from the sample 4 enters the radiation detector 1. The radiation consisting of characteristic X-rays mainly passes through the window 161 and enters the interior of the radiation detector 1. A portion of the radiation that enters the interior of the radiation detector 1 enters the radiation detection element 11 and then enters the semiconductor portion 112. The radiation is absorbed within the semiconductor portion 112, and electrons and holes are generated in amounts corresponding to the energy of the absorbed radiation. In this embodiment, the electric field within the semiconductor portion 112 causes electrons to move and flow into the signal output electrode 115. The signal output electrode 115 outputs a current signal according to the electrons that have flowed in.

[0038] The semiconductor portion 112 includes a sensitive region 117 capable of detecting radiation. The region surrounded by a dashed line in FIG. 3 is the sensitive region 117. The sensitive region 117 includes the central portion of the semiconductor portion 112 and most of the semiconductor portion 112 except for the portion close to the periphery of the semiconductor portion 112. Compared to the central portion of the semiconductor portion 112, the portion close to the periphery of the semiconductor portion 112 has a weaker electric field, making it difficult for electrons generated by incident radiation to move to the signal output electrode 115. The portion close to the periphery of the semiconductor portion 112 is covered by the collimator 13, making it difficult for radiation to be incident thereon. The sensitive region 117 is the portion not covered by the collimator 13. Furthermore, the region of the semiconductor portion 112 close to the incident surface 111 and the region close to the electrode surface 116 are not included in the sensitive region 117.

[0039] The signal output electrode 115 is connected to the preamplifier 12. The signal output by the signal output electrode 115 is input to the preamplifier 12. The preamplifier 12 converts the current signal into a voltage signal. The preamplifier 12 is connected to the signal processing unit 22. When the preamplifier 12 outputs a signal, the radiation detector 1 outputs a signal of an intensity corresponding to the energy of the radiation to the signal processing unit 22. The signal processing unit 22 receives the signal output by the radiation detector 1 and determines the signal intensity, thereby detecting the signal intensity corresponding to the energy of the radiation detected by the radiation detector 1. The signal processing unit 22 counts the signals by signal intensity, and outputs data indicating the relationship between the signal intensity and the count number to the analysis unit 3.

[0040] The analysis unit 3 receives data indicating the relationship between the signal intensity and the count number output by the signal processing unit 22. The analysis unit 3 generates a spectrum of the radiation incident on the radiation detector 1 based on the data from the signal processing unit 22. Since the signal intensity corresponds to the energy of the radiation and the count number corresponds to the number of times the radiation is detected, i.e., the intensity of the radiation, the spectrum of the radiation can be obtained from the relationship between the signal intensity and the count number. The spectrum represents the relationship between the energy of the radiation and the intensity of the radiation. The process of counting the signals output by the radiation detector 1 by signal intensity may be performed by the analysis unit 3 instead of the signal processing unit 22. The generation of the radiation spectrum may be performed by the signal processing unit 22. The analysis unit 3 stores spectral data representing the spectrum of the radiation. The display unit 24 displays the spectrum of the radiation.

[0041] FIG. 4 is a block diagram showing an example of the internal configuration of the analysis unit 3. The analysis unit 3 is an information processing device that executes an information processing method. The analysis unit 3 is configured using a computer such as a personal computer. The analysis unit 3 includes a calculation unit 31, a memory 32, a reading unit 33, a storage unit 34, an interface unit 35, and a parallel calculation unit 36. The calculation unit 31 is configured using, for example, a central processing unit (CPU), a graphics processing unit (GPU), a multi-threaded CPU, or a multi-core CPU. The calculation unit 31 is a processor and may be configured using a quantum computer. The memory 32 stores temporary data generated in conjunction with calculations. The memory 32 is, for example, a random access memory (RAM). The reading unit 33 reads information from a recording medium 30 such as an optical disk or a portable memory. The storage unit 34 is non-volatile, for example, a hard disk or a non-volatile semiconductor memory. The parallel calculation unit 36 ​​is a device for executing multiple calculations in parallel. For example, the parallel computing unit 36 ​​is configured using a GPU. The arithmetic unit 31 and the parallel computing unit 36 ​​correspond to a computing unit.

[0042] The calculation unit 31 causes the reading unit 33 to read a computer program (program product) 341 recorded on the recording medium 30, and stores the read computer program 341 in the storage unit 34. The calculation unit 31 executes processing required for the analysis unit 3 in accordance with the computer program 341. The computer program 341 may be downloaded from outside the analysis unit 3. Alternatively, the computer program 341 may be stored in advance in the storage unit 34. In these cases, the analysis unit 3 does not need to include the reading unit 33.

[0043] The computer program 341 can be deployed to run on a single computer, or on multiple computers located at one site or distributed across multiple sites and interconnected by a communications network. That is, the analysis unit 3 may be configured with multiple computers, and the computer program 341 may be executed on multiple computers connected via a communications network. The analysis unit 3 may be configured using a cloud server. The analysis unit 3 and the control unit 25 may be configured with the same computer.

[0044] The processing of each step described below for executing the information processing method can be executed by multiple computers. The processing of each step can also be executed by different computers. Data used during the processing can be stored in multiple computers. The processing of each step can also be executed using a virtual machine. The processing of each step can be executed by multiple computing units. The processing of each step can also be executed by different computing units. For example, part of the processing can be executed by one computer, and another part of the processing can be executed by another computer.

[0045] The analysis unit 3 is connected to an operation unit 23 and a display unit 24. The interface unit 35 is connected to a signal processing unit 22 and a control unit 25. A user operates the operation unit 23 to input various instructions, such as an instruction to start analysis, to the analysis unit 3. The analysis unit 3 accepts the instructions input using the operation unit 23. The display unit 24 displays an image. The analysis unit 3 outputs information necessary for radiation detection or analysis of the sample 4 by displaying an image including the information on the display unit 24. The analysis unit 3 accepts information from the signal processing unit 22 and the control unit 25 via the interface unit 35. Some or all of the processing for executing the information processing method may be executed by a computer external to the radiation detection device 100, and the analysis unit 3 may transmit information necessary for processing to the external computer and obtain processing results from the external computer.

[0046] The radiation spectrum generated by the analysis unit 3 indicates the relationship between the energy and intensity of the radiation generated from the sample 4 irradiated with radiation and detected using the radiation detector 1. Radiation is made up of multiple photons, and the intensity of the radiation corresponds to the number of photons contained in the radiation. The radiation is detected by detecting each photon. In the radiation spectrum, each energy is associated with the intensity of the radiation having that energy, i.e., the number of detected photons having that energy.

[0047] When radiation is irradiated onto the sample 4, elements contained in the sample 4 are excited by the radiation, and radiation is generated when the excited elements return to their ground state. The energy of the radiation generated from the irradiated sample 4 has a value specific to the type of element contained in the sample 4. The intensity of the radiation has a value corresponding to the concentration of the element contained in the sample 4. Therefore, it is possible to identify the type and concentration of the element contained in the sample 4 from the radiation spectrum.

[0048] FIG. 5 is a diagram showing the spectrum of radiation having a single energy. The horizontal axis in the diagram represents the radiation energy, and the vertical axis represents the radiation intensity. The spectrum includes a peak corresponding to the radiation energy. It is assumed that the sample 4 contains a single type of element, and that irradiation with radiation generates radiation having a single energy corresponding to that element. In this case, by detecting the radiation generated from the irradiated sample 4, a spectrum including only a single peak, as shown in FIG. 5, should ideally be obtained.

[0049] However, in reality, due to various phenomena occurring in the sample 4 and the radiation detector 1, peaks and background other than the peaks corresponding to the energies depending on the elements occur. Peaks due to signal processing may also occur. As a result, a complex spectrum is generated in which many peaks and backgrounds overlap. FIG. 6 is a diagram showing an example of a spectrum of radiation generated when radiation having a single energy is generated from the sample 4. The horizontal axis in the diagram indicates the energy of the radiation, and the vertical axis indicates the intensity of the radiation. FIG. 6 shows an example of a spectrum generated when radiation having the energies shown in FIG. 5 is generated from the sample 4. The spectrum includes a peak corresponding to the energy of the radiation, and also includes many peaks corresponding to other energies.

[0050] For example, when radiation is incident on an object other than the sample 4 and the radiation detection element 11, characteristic X-rays are generated, and characteristic X-rays having an energy different from that of the radiation to be detected are detected. For example, radiation undergoes inelastic scattering within the radiation detection element 11, and electrons generated by the inelastic scattering and radiation with attenuated energy are detected. For example, when radiation is detected multiple times at very short intervals, the signal processing unit 22 is unable to separate the multiple signals corresponding to the multiple detections, and ends up detecting radiation with an energy equal to the sum of the energies of the multiple radiations. A peak caused by this phenomenon is a so-called sum peak, and the sum peak may correspond to an energy higher than the original energy of the radiation. As such, the actually obtained spectrum contains many peaks corresponding to energies different from those corresponding to the elements in the sample 4 due to various phenomena.

[0051] In reality, radiation having multiple energies may be generated due to one type of element. Furthermore, the sample 4 may contain multiple types of elements. The relative intensities of multiple radiations corresponding to the multiple types of elements are values ​​that correspond to the relative concentrations of the multiple types of elements. Therefore, the actually obtained radiation spectrum may be more complex than the spectrum shown in FIG. 6 . It is difficult to identify the types and concentrations of elements contained in the sample 4 from the actually obtained complex spectrum.

[0052] The analysis unit 3 performs information processing to determine the characteristics of the sample 4. The characteristics of the sample 4 include the composition, shape, and structure of the sample 4, or the type, concentration, position, or distribution of elements contained in the sample 4. The structure of the sample 4 includes, for example, that the sample 4 has a multilayer structure and the thickness of each layer. The analysis unit 3 calculates a hypothetical spectrum that can be obtained based on assumptions about the characteristics of the sample 4, compares the actually obtained spectrum with the hypothetical spectrum, and adjusts the assumed characteristics of the sample 4, thereby determining the characteristics of the sample 4. Hereinafter, the hypothetical spectrum of radiation calculated based on assumptions about the characteristics of the sample 4 will be referred to as the hypothetical spectrum. Furthermore, the spectrum of radiation actually obtained by actually detecting radiation using the radiation detector 1 will be referred to as the actually measured spectrum.

[0053] The memory unit 34 stores information necessary for information processing to identify the energy and intensity of radiation. The memory unit 34 stores detector data including information about multiple objects included in the radiation detector 1. The detector data records the position, size, and material of each object included in the radiation detector 1. The detector data records the material properties of each object. For example, the detector data records the type, absorption coefficient, and density of the elements contained as material properties. The detector data also includes information representing the positional relationship between the sample 4 placed on the sample stage 27 and the radiation detector 1.

[0054] The storage unit 34 stores element data including information about elements. The element data includes the energy value of radiation generated due to a specific element. The element data includes the intensity ratio of radiation having multiple energies generated due to a specific element. The element data includes information indicating the relationship between the concentration of an element and the intensity of radiation. The element data includes information about multiple elements.

[0055] The analysis unit 3 executes information processing to determine the characteristics of the sample 4. FIG. 7 is a flowchart showing an example of the processing procedure executed by the analysis unit 3. Hereinafter, steps are abbreviated as S. The calculation unit 31 executes information processing in accordance with the computer program 341, causing the analysis unit 3 to execute the following processing. The radiation detection device 100 irradiates the sample 4 with radiation from the irradiation unit 26, detects radiation emitted from the sample 4 using the radiation detector 1, and generates a spectrum of the detected radiation. The analysis unit 3 generates a spectrum of the detected radiation, thereby obtaining a measured spectrum of the radiation emitted from the sample 4 (S101). The calculation unit 31 stores spectral data representing the measured spectrum in the storage unit 34.

[0056] The analysis unit 3 identifies the characteristics of the radiation irradiated from the irradiation unit 26 to the sample 4 (S102). The characteristics of the radiation include the energy and intensity of the radiation. If the radiation contains multiple types of radiation with different energies, the characteristics of the radiation include the energies and intensities of the multiple types of radiation. The energy of the radiation is the energy of each of the multiple photons contained in the radiation, and the intensity of radiation having a specific energy corresponds to the number of photons having the specific energy. The characteristics of the radiation include the spatial distribution of the multiple photons contained in the radiation and the traveling direction of each photon. The characteristics of the radiation identified in S102 may be the characteristics of the radiation immediately after it is emitted from the irradiation unit 26 or may be the characteristics of the radiation immediately before it reaches the sample 4.

[0057] In S102, the user operates the operation unit 23 to input the radiation characteristics, and the calculation unit 31 identifies the energy and intensity of the radiation. In S102, the calculation unit 31 may identify the radiation characteristics as predetermined characteristics. In S102, the calculation unit 31 may identify the radiation characteristics based on a control signal used by the control unit 25 to control the irradiation unit 26. In S102, some of the radiation characteristics are input, and the calculation unit 31 may identify other characteristics as predetermined characteristics, or may identify other characteristics by calculation in accordance with predetermined rules based on the input characteristics. Some of the radiation characteristics, such as the photon propagation direction, may be determined probabilistically.

[0058] The analysis unit 3 identifies the shape of the sample 4 (S103). In S103, the user operates the operation unit 23 to input the shape of the sample 4, and the calculation unit 31 then identifies the shape of the sample 4. In S103, the calculation unit 31 may identify the shape of the sample 4 as a predetermined shape. An image of the sample 4 taken with a camera may be input to the analysis unit 3, and the calculation unit 31 may analyze the image to identify the shape of the sample.

[0059] The analysis unit 3 assumes characteristics of the sample 4 (S104). The characteristics of the sample 4 include the composition and structure of the sample 4, or the type, concentration, location, or distribution of elements contained in the sample 4. The structure of the sample 4 includes, for example, that the sample 4 has a multilayer structure and the thickness of each layer. The element distribution may include information representing the distribution state of multiple types of elements. In S104, the user operates the operation unit 23 to input the characteristics of the sample 4, and the calculation unit 31 assumes the characteristics of the sample 4. In S104, the calculation unit 31 may assume some or all of the characteristics of the sample 4 to predetermined values ​​or may assume them randomly. When assuming the element concentrations of the sample 4, the fundamental parameter method (FPM), a conventional method for calculating element concentrations, may be applied to the measured spectrum, and the element concentrations may be assumed using the FPM calculation results. The element concentrations may also be assumed based on the peak intensities of the measured spectrum.

[0060] The analysis unit 3 then performs Monte Carlo ray tracing to track each of the multiple photons contained in the radiation (S105). The analysis unit 3 executes the process of S105 using the parallel calculation unit 36. The calculation unit 31 inputs information necessary for the calculation, such as the characteristics of the radiation irradiated onto the sample 4, the shape of the sample 4, and the characteristics of the sample 4, to the parallel calculation unit 36, and causes the parallel calculation unit 36 ​​to execute the process. In S105, the parallel calculation unit 36 ​​executes calculations in parallel to track the multiple photons contained in the radiation irradiated onto the sample 4 and the radiation generated from the sample 4, using the assumed energy and intensity of the radiation.

[0061] 8 and 9 are flowcharts showing an example of the processing procedure of Monte Carlo ray tracing. The parallel computing unit 36 ​​selects one photon from multiple photons contained in the radiation irradiated onto the sample 4 (S201). In S201, the parallel computing unit 36 ​​determines initial values ​​for the position, energy, and traveling direction of the photon based on the characteristics of the radiation identified in S102. The parallel computing unit 36 ​​sets the number of photons to a value corresponding to the intensity of the radiation included in the characteristics of the radiation identified in S102.

[0062] The parallel computing unit 36 ​​stochastically calculates the flight distance of the selected photons (S202). The flight distance is a dimensionless quantity that represents the distance traveled by a photon. A representative value of the flight distance, such as an average value, or a probability distribution of the flight distance is determined in advance and stored in the storage unit 34. The parallel computing unit 36 ​​stochastically calculates the flight distance.

[0063] The parallel computing unit 36 ​​determines whether the photon is incident on any object within the radiation detection device 100 (S203). In S203, the parallel computing unit 36 ​​tracks the selected photon. For example, the parallel computing unit 36 ​​tracks the photon assuming that the photon travels in a straight line while consuming a flight distance. The amount of flight distance consumed in air is calculated according to the absorption coefficient of air. For photons contained in the radiation before being irradiated onto the sample 4, the parallel computing unit 36 ​​determines whether the photon is incident on the sample 4 based on the position, traveling direction, and flight distance of the photon and the shape of the sample 4. For photons contained in the radiation generated from the sample 4, the parallel computing unit 36 ​​determines whether the photon is incident on an object based on the position, traveling direction, and flight distance of the photon and the position and size of each object included in the radiation detector 1 recorded in the detector data.

[0064] For example, if an object is present at a position shorter than the flight distance, the parallel computing unit 36 ​​determines that the photon is incident on an object. For example, if no object is present before the photon consumes its entire flight distance, the parallel computing unit 36 ​​determines that the photon is not incident on any object. Note that the positions of objects other than the sample 4 and the radiation detector 1 included in the radiation detection device 100 may be stored in advance in the storage unit 34, and the parallel computing unit 36 ​​may determine whether the photon is incident on an object other than the sample 4 and the radiation detector 1.

[0065] If the photon does not enter any object inside the radiation detection device 100 (S203: NO), the parallel calculation unit 36 ​​ends the Monte Carlo ray tracing process and returns the process to the main process executed by the calculation unit 31. If the photon does not enter an object, the photon disappears without being detected. Note that photons do not consume a flight distance in a vacuum. When calculations are performed assuming that the inside of the radiation detection device 100 is a vacuum, the parallel calculation unit 36 ​​performs calculations assuming that the photon will always enter some object.

[0066] If the photon is incident on an object within the radiation detection device 100 (S203: YES), the parallel computing unit 36 ​​determines whether the photon passes through the object (S204). The parallel computing unit 36 ​​identifies the object on which the photon is incident based on the position of each object. In S204, the parallel computing unit 36 ​​compares the distance traveled by the photon within the object with the size of the object. If the absorption coefficient of the object is μ, the distance traveled by the photon within the object becomes shorter as μ increases, and becomes longer as μ decreases.

[0067] The parallel calculation unit 36 ​​calculates the distance traveled by the photon within the object by using the absorption coefficient of the object recorded in the detector data and correcting the flight distance, which is obtained by subtracting the flight distance consumed by the photon before it enters the object, according to μ. When the object on which the photon is incident is the sample 4, the parallel calculation unit 36 ​​performs calculations using a value estimated from assumed characteristics of the sample 4 as the absorption coefficient of the sample 4. The parallel calculation unit 36 ​​may use a predetermined value as the absorption coefficient of the sample 4, or may use a value input by the user by operating the operation unit 23. The parallel calculation unit 36 ​​may calculate the distance traveled by the photon within the object by using the density of the object.

[0068] The parallel computing unit 36 ​​compares the distance traveled by the photon within the object with the length of the object along the direction of travel of the photon. The length of the object along the direction of travel of the photon is calculated from the shape of the sample 4 or the position and size of the object recorded in the detector data. The parallel computing unit 36 ​​determines that the photon has passed through the object if the distance traveled by the photon within the object exceeds the length of the object along the direction of travel of the photon. The parallel computing unit 36 ​​determines that the photon has not passed through the object if the distance traveled by the photon within the object does not exceed the length of the object along the direction of travel of the photon.

[0069] If the photon passes through the object (S204: YES), the parallel computing unit 36 ​​corrects the flight distance of the photon (S205). In S205, the parallel computing unit 36 ​​calculates the flight distance consumed by the photon within the object by correcting the length of the object along the flight direction of the photon according to μ. The parallel computing unit 36 ​​corrects the flight distance of the photon by subtracting from the flight distance of the photon the flight distance consumed by the photon before it enters the object and the flight distance consumed by the photon within the object. The parallel computing unit 36 ​​also calculates the position of the photon that has passed through the object. The parallel computing unit 36 ​​then returns the process to S203.

[0070] If the photon does not pass through the object (S204: NO), the parallel computing unit 36 ​​determines whether photoelectric absorption occurs (S206). If the photon does not pass through the object, the photon interacts with the object on which the photon is incident. The parallel computing unit 36 ​​identifies the object on which the photon is incident as an object that interacts with the photon, and calculates the position at which the photon interacts with the object. At this time, the parallel computing unit 36 ​​calculates the position that is the distance the photon travels within the object along the photon's flight direction from the position at which the photon is incident on the object.

[0071] If the object that interacts with the photon is the sample 4, the probability that photoelectric absorption will occur is determined by the type of element present at the position where the photon interacts with the sample 4 and the energy of the photon. The parallel computing unit 36 ​​identifies the element present at the position where the photon interacts with the sample 4 based on the assumed characteristics of the sample 4, and determines the probability that photoelectric absorption will occur according to the type of element identified and the energy of the photon. If an object other than the sample 4 interacts with the photon, the probability that photoelectric absorption will occur within the object is determined by the material of the object and the energy of the photon. In S206, the parallel computing unit 36 ​​calculates the probability that photoelectric absorption will occur according to the material of the object and the energy of the photon, and probabilistically determines whether or not photoelectric absorption will occur.

[0072] If photoelectric absorption occurs (S206: YES), the parallel computing unit 36 ​​determines whether fluorescence occurs when photoelectric absorption occurs (S207). Fluorescence occurs when an electron contained in an atom in an object is ejected by a photon, leaving the ejected electron's orbit empty, and an outermost electron transitions to the empty orbit. For example, fluorescence is fluorescent X-rays. If the object that interacts with the photon is the sample 4, the probability of fluorescence occurrence is determined by the type of element present at the position where the photon interacts with the sample 4. The parallel computing unit 36 ​​determines the probability of fluorescence occurrence based on the assumed characteristics of the sample 4. If an object other than the sample 4 interacts with the photon, the probability of fluorescence occurrence is determined by the material and shape of the object and is pre-stored in the memory unit 34. In S207, the parallel computing unit 36 ​​probabilistically determines whether fluorescence occurs.

[0073] If no fluorescence is generated (S207: NO), the parallel computing unit 36 ​​determines whether or not the object that interacts with the photon is within the sensitive region 117 of the radiation detection element 11 (S208). In S208, the parallel computing unit 36 ​​determines whether or not the position at which the photon interacts with the object is within the sensitive region 117 of the radiation detection element 11.

[0074] If the object that interacts with the photons is not the sensitive region 117 of the radiation detection element 11 (S208: NO), the parallel computing unit 36 ​​ends the Monte Carlo ray tracing process and returns the process to the main process executed by the calculation unit 31. Even if photoelectric absorption occurs, if the object that interacts with the photons is not the sensitive region 117 of the radiation detection element 11, the photons will not be detected.

[0075] If the object interacting with the photons is the sensitive region 117 of the radiation detection element 11 (S208: YES), the parallel computing unit 36 ​​calculates the energy of electrons and holes generated by photoelectric absorption (S209). Photon energy is not necessarily converted entirely into electron and hole energy by photoelectric absorption. Conversion probabilities are determined for different rates of photon energy conversion into electron and hole energy, and these probabilities are pre-stored in the storage unit 34. In S209, the parallel computing unit 36 ​​probabilistically calculates the energy of electrons and holes generated by photoelectric absorption. Furthermore, the energy of electrons detectable by the radiation detection element 11 is affected by the structure of the radiation detection element 11 and the position within the radiation detection element 11 where the photon interacts with the radiation detection element 11. For example, the rate at which generated electrons flow into the signal output electrode 115 differs between positions close to the electrode and positions far from the electrode, resulting in changes in the detected energy. In S209, the parallel computing unit 36 ​​calculates the energy, taking into account the influence of the structure of the radiation detection element 11 and the position where the interaction takes place within the radiation detection element 11.

[0076] The parallel computing unit 36 ​​corrects the energy due to the recombination of electrons and holes (S210). Some of the electrons and holes generated by photoelectric absorption may recombine. The energy of the electrons and holes decreases due to recombination. If the object that interacts with the photon is the sample 4, the probability of recombination is determined according to the type of element at the position where the photon interacts with the sample 4. The parallel computing unit 36 ​​identifies the probability of recombination based on the assumed characteristics of the sample 4. If an object other than the sample 4 interacts with the photon, the probability of recombination is determined according to the material of the object and is pre-stored in the storage unit 34. In S210, the parallel computing unit 36 ​​probabilistically calculates the energy decrease and corrects the energy of the electrons and holes by subtracting the calculated energy from the energy calculated in S209.

[0077] The parallel computing unit 36 ​​determines the energy detected by the radiation detector 1 (S211). In S211, the parallel computing unit 36 ​​determines the detected energy according to the corrected electron energy. For example, the parallel computing unit 36 ​​sets the corrected electron energy as the energy of the detected photon. For example, the parallel computing unit 36 ​​calculates the detected energy by multiplying the corrected electron energy by a predetermined proportionality coefficient. After S211 is completed, the parallel computing unit 36 ​​ends the Monte Carlo ray tracing process and returns the process to the main process executed by the calculation unit 31.

[0078] If fluorescence is generated in S207 (S207: YES), the parallel computing unit 36 ​​selects a trajectory of the electron ejected by the photon (S212). If the object that interacted with the photon is the sample 4, the probability of the trajectory being selected is determined according to the type of element at the position where the photon interacts with the sample 4. The parallel computing unit 36 ​​identifies the probability of each trajectory being selected based on the assumed characteristics of the sample 4. In S212, the parallel computing unit 36 ​​probabilistically selects a trajectory of the electron ejected by the photon. If fluorescence is generated, the energy required to eject the electron is reduced from the photon energy, and the photon energy consumed by photoelectric absorption is reduced.

[0079] The parallel computing unit 36 ​​calculates the photon energy consumed by photoelectric absorption and the fluorescence energy (S213). The photon energy consumed by photoelectric absorption is calculated by subtracting the energy required to eject the electron from the original photon energy. The energy required to eject the electron is determined depending on the material of the object. The fluorescence energy is determined depending on the material of the object and also depending on the difference between the orbit of the electron ejected by the photon and the orbit of the electron transitioning to the vacant orbit.

[0080] The parallel computing unit 36 ​​calculates the energy detected in response to the photoelectric absorption (S214). In S214, the parallel computing unit 36 ​​executes the same processes as S208 to S211 for calculating the energy detected in response to the photoelectric absorption.

[0081] The parallel computing unit 36 ​​then corrects the energy of the fluorescence due to Auger electrons (S215). When fluorescence is generated, Auger electrons may be generated, and the energy of the fluorescence decreases in response to the generation of Auger electrons. If the object that interacts with the photon is the sample 4, the probability of Auger electron generation is determined according to the type of element present at the position where the photon interacts with the sample 4. The parallel computing unit 36 ​​identifies the probability of Auger electron generation based on assumed characteristics of the sample 4. In S215, the parallel computing unit 36 ​​probabilistically calculates the energy that decreases in response to the generation of Auger electrons, and corrects the fluorescence energy by subtracting the calculated energy from the fluorescence energy.

[0082] The parallel computing unit 36 ​​determines the position where the photon interacts with the object as the position of the photon generated by the generation of fluorescence. When the object that interacts with the photon is the sample 4, the generated fluorescence is radiation emitted from the sample 4, and the photon generated by the generation of fluorescence is a photon included in the radiation emitted from the sample 4.

[0083] The parallel computing unit 36 ​​calculates the traveling direction of the photons generated by the generation of fluorescence (S216). The traveling direction of the photons generated by the generation of fluorescence does not depend on the angle, and the photons can travel equally in either direction. In S216, the parallel computing unit 36 ​​probabilistically calculates the traveling direction of the photons. After S216 is completed, the parallel computing unit 36 ​​returns the process to S202. In S202, the parallel computing unit 36 ​​calculates the traveling distance of the photons generated by the generation of fluorescence. If the fluorescence is generated from the sample 4, the parallel computing unit 36 ​​thereafter performs Monte Carlo ray tracing of the photons contained in the radiation generated from the sample 4.

[0084] If photoelectric absorption does not occur in S206 (S206: NO), the parallel computing unit 36 ​​determines whether inelastic scattering of the photon occurs within the object (S217). If the object that interacts with the photon is the sample 4, the probability of inelastic scattering occurring depends on the type of element present at the position where the photon interacts with the sample 4. The parallel computing unit 36 ​​identifies the probability of inelastic scattering occurring based on the assumed characteristics of the sample 4. In S217, the parallel computing unit 36 ​​probabilistically calculates whether inelastic scattering occurs. Inelastic scattering is Compton scattering, which occurs between a photon and an electron within the object. In Compton scattering, part of the photon's energy is transferred to the electron, resulting in a decrease in the photon's energy.

[0085] If inelastic scattering occurs (S217: YES), the parallel computing unit 36 ​​calculates the photon energy and electron energy after Compton scattering (S218). In S218, the parallel computing unit 36 ​​probabilistically calculates the energy imparted from the photon to the electron, sets the calculated energy as the electron energy, and calculates the photon energy after Compton scattering by subtracting the electron energy from the original photon energy.

[0086] The parallel computing unit 36 ​​calculates the detected energy of the electrons that have been given energy by Compton scattering (S219). The electrons that have been given energy by Compton scattering in the sensitive region 117 of the radiation detector 1 flow into the signal output electrode 115 and can be detected. In S219, the parallel computing unit 36 ​​executes processing equivalent to S208 to S211 for calculating the energy that is detected when the electrons are detected.

[0087] The parallel computing unit 36 ​​determines the position where the photon interacts with the object as the position of the photon after Compton scattering. If the object that interacts with the photon is the sample 4, the photon after Compton scattering is a photon contained in the radiation generated from the sample 4.

[0088] The parallel computing unit 36 ​​then calculates the traveling direction of the photon after Compton scattering (S220). The scattering angle of the photon due to Compton scattering is angularly dependent, and the probability distribution of the scattering angle is expressed by the Klein-Nishina formula. In S220, the parallel computing unit 36 ​​probabilistically calculates the traveling direction of the photon using the Klein-Nishina formula. After S220 is completed, the parallel computing unit 36 ​​returns the process to S202. In S202, the parallel computing unit 36 ​​calculates the flight distance of the photon after Compton scattering. If Compton scattering has occurred in the sample 4, the parallel computing unit 36 ​​thereafter performs Monte Carlo ray tracing of the photon contained in the radiation generated from the sample 4.

[0089] If inelastic scattering does not occur in S217, elastic scattering occurs within the object. Elastic scattering is Rayleigh scattering that occurs between photons and electrons within the object. In Rayleigh scattering, the direction of travel of the photon may change, but the energy of the photon does not change. If inelastic scattering does not occur in S217 (S217: NO), the parallel computing unit 36 ​​calculates the direction of travel of the photon after Rayleigh scattering (S221).

[0090] The scattering angle of photons due to Rayleigh scattering has angular dependence. If the scattering angle is θ, the probability distribution of the scattering angle P(θ) is P(θ) = sin 2θ. In S221, the parallel computing unit 36 ​​probabilistically calculates the traveling direction of the photon using an equation for the probability distribution of the scattering angle. The parallel computing unit 36 ​​determines the position where the photon interacts with the object as the position of the photon after Rayleigh scattering. If the object that interacts with the photon is the sample 4, the photon after Rayleigh scattering is a photon contained in the radiation generated from the sample 4. After S221 is completed, the parallel computing unit 36 ​​returns the process to S202. In S202, the parallel computing unit 36 ​​calculates the flight distance of the photon after Rayleigh scattering. If Rayleigh scattering has occurred in the sample 4, the parallel computing unit 36 ​​thereafter performs Monte Carlo ray tracing of the photon contained in the radiation generated from the sample 4.

[0091] In the Monte Carlo ray tracing process of S105, the processes of S201 to S221 are executed for each of the multiple photons contained in the radiation. As a result, the interaction between each photon contained in the radiation irradiated onto the sample 4 and the sample 4 is calculated, and the trajectories of photons generated by the interaction between the photon and the sample 4 are calculated. The photons generated by the interaction between the photon and the sample 4 are photons contained in the radiation generated from the sample 4. Therefore, Monte Carlo ray tracing is performed for photons contained in the radiation irradiated onto the sample 4, and Monte Carlo ray tracing is also performed for photons contained in the radiation generated from the sample 4. Since the interaction between the photons and the sample 4 changes depending on the elements contained in the sample 4, the results of Monte Carlo ray tracing of photons contained in the radiation generated from the sample 4 change depending on the characteristics of the sample 4. Note that in the Monte Carlo ray tracing process of S105, X-ray diffraction occurring in the sample 4 may also be processed as a physical phenomenon.

[0092] The process of S105 for multiple photons is executed in parallel by the parallel computing unit 36. More specifically, the parallel computing unit 36 ​​executes the process of S105 for one photon in one thread. The detected energies for multiple photons are calculated by the process of S105. By executing the process of S105 by the parallel computing unit 36, it is possible to execute the Monte Carlo ray tracing process for a large number of photons at high speed. After the process of S105 is completed, the calculation unit 31 stores the detected energies for the multiple photons in the memory 32 or the storage unit 34.

[0093] The analysis unit 3 then determines the time intervals at which the multiple photons whose energies have been calculated are detected (S106). In S106, the calculation unit 31 selects one detected photon and determines whether the time intervals at which the selected photon and other photons are detected are short. Under the assumption that photon detection occurs randomly, the calculation unit 31 probabilistically determines the time intervals at which the selected photon and other photons are detected, and determines that the time interval is short if the determined time interval is less than a predetermined threshold. The threshold is pre-stored in the storage unit 34 or is included in the computer program 341. The calculation unit 31 may also determine that the time interval is short if the determined time interval matches the predetermined threshold. The calculation unit 31 performs the determination of the detected time intervals for multiple combinations of two photons. For example, the calculation unit 31 determines the detected time intervals for combinations of two photons calculated in two adjacent threads of the parallel calculation unit 36. The calculation unit 31 may determine the time intervals at which the photons are detected for combinations of three or more photons.

[0094] The analysis unit 3 replaces multiple photons detected at short time intervals with a single photon (S107). In S107, the calculation unit 31 modifies the energy calculated in S105 by setting the sum of the energies detected for multiple photons determined to be detected at short time intervals as the energy of a single photon. As a result, multiple photons detected at short time intervals are replaced mathematically with a single photon having energy equal to the sum of some or all of the energies of the multiple photons. The process of S107 can reproduce a sum peak contained in the spectrum of radiation. In the process of S108 described below, it becomes possible to generate a virtual spectrum including the sum peak. The processes of S106 and S107 may be executed by the parallel calculation unit 36.

[0095] The analysis unit 3 generates a virtual spectrum of the radiation (S108). The calculation unit 31 counts photons for each calculated energy to generate a virtual spectrum that represents the relationship between the detected energy and the number of detected photons. The virtual spectrum is a virtual radiation spectrum calculated using Monte Carlo ray tracing. The calculation unit 31 stores data representing the virtual spectrum in the memory 32 or the storage unit 34.

[0096] The analysis unit 3 calculates the error between the measured spectrum and the virtual spectrum (S109). In S109, for example, the calculation unit 31 calculates the difference in intensity of radiation associated with each energy between the measured spectrum and the virtual spectrum, and calculates the error by summing the squares of the intensity differences across multiple energies. The calculation unit 31 may calculate the error by summing the squares of the intensity differences within a specific energy range, such as a range including a peak with a relatively high intensity. The calculation unit 31 may calculate the error as the difference in intensity for each energy or the square of the intensity difference. The calculation unit 31 may normalize the measured spectrum and the virtual spectrum before calculating the error. By calculating the error, the analysis unit 3 compares the measured spectrum with the virtual spectrum.

[0097] The analysis unit 3 then determines whether a specific condition is satisfied (S110). In S110, the calculation unit 31 determines whether a condition is satisfied that the error calculated in S109 is within a predetermined range. For example, the calculation unit 31 determines whether a condition is satisfied that the error value is less than a predetermined threshold. For example, the calculation unit 31 determines whether a condition is satisfied that the error is less than a threshold at a number of energies exceeding a predetermined number or within a specific energy range. The specific condition may be a condition that the amount of change in error updated by the repeated calculation in S109 is less than a predetermined lower limit. The specific condition may also be a condition that the number of repetitions of the processes of S105 to S111 or the calculation time reaches a predetermined upper limit.

[0098] If the specific condition is not satisfied (S110: NO), the analysis unit 3 modifies the assumed characteristics of the sample 4 (S111). In S111, for example, the calculation unit 31 modifies the assumed composition or structure of the sample 4, or the type, concentration, position, or distribution of elements contained in the sample 4. For example, the calculation unit 31 modifies the number of types of elements contained in the sample 4. In S109, the user may operate the operation unit 23 to input the modification details for the characteristics of the sample 4, and the calculation unit 31 may make the modification according to the input modification details. The calculation unit 31 may also make the modification without using input from the user.

[0099] In S111, the calculation unit 31 corrects the assumed characteristics of the sample 4 so as to reduce the error between the measured spectrum and the hypothetical spectrum. For example, the calculation unit 31 stores in the storage unit 34 a history of the correction of the characteristics of the sample 4 and the change in the error, and corrects the characteristics of the sample 4 so as to reduce the error based on the stored history.

[0100] In S111, the sample 4 may be analyzed based on the difference between the measured spectrum and the virtual spectrum. For example, a virtual spectrum may be generated so that some regions of the measured spectrum and the virtual spectrum match, and the sample 4 may be analyzed based on the difference in the remaining regions. By calculating the difference between the measured spectrum containing a component corresponding to a specific element and the virtual spectrum not containing the component corresponding to the specific element, components other than the component corresponding to the specific element can be removed as background from the measured spectrum. More specifically, the difference between the measured spectrum containing the component corresponding to the specific element and the virtual spectrum not containing the component corresponding to the specific element can be calculated after matching specific peak intensities, and the concentration of the specific element can be analyzed from the calculated difference. The calculation unit 31 modifies the assumed characteristics of the sample 4 based on this analysis of the sample 4.

[0101] After S111 is completed, the analysis unit 3 returns the process to S105. The analysis unit 3 repeats the processes of S105 to S111 until a specific condition is met. By repeating the processes of S105 to S111, the analysis unit 3 corrects the characteristics of the assumed sample 4 so that the measured spectrum and the hypothetical spectrum approximately match. If the measured spectrum and the hypothetical spectrum approximately match, it can be assumed that the characteristics of the assumed sample 4 approximately match the characteristics of the actual sample 4.

[0102] If a specific condition is satisfied (S110: YES), the analysis unit 3 determines the characteristics of the sample 4 (S112). In S112, the calculation unit 31 determines the corrected characteristics of the sample 4 as the characteristics of the actual sample 4. The assumed characteristics of the sample 4 are repeatedly corrected through the processes of S105 to S111, and it can be assumed that they substantially match the characteristics of the actual sample 4. Therefore, the analysis unit 3 can determine the corrected assumed characteristics of the sample 4 as the characteristics of the actual sample 4. Specifically, the calculation unit 31 determines the composition and structure of the sample 4, or the type, concentration, position, or distribution of elements contained in the sample 4, as the characteristics of the sample 4. The calculation unit 31 stores the determined characteristics of the sample 4 in the memory unit 34. The calculation unit 31 may display the results of the determined characteristics of the sample 4 on the display unit 24. After S112 is completed, the analysis unit 3 ends the processing.

[0103] As described above in detail, in this embodiment, the analysis unit 3 assumes characteristics of the sample 4, calculates the energy at which individual photons contained in radiation from the sample 4 are detected by Monte Carlo ray tracing, and generates a virtual spectrum of the radiation. The analysis unit 3 corrects the assumed characteristics of the sample 4 based on a comparison between the measured spectrum of the radiation and the virtual spectrum. Because the interaction between the photons and the sample 4 varies depending on the elements contained in the sample 4, the results of Monte Carlo ray tracing of photons contained in the radiation generated from the sample 4 change depending on the characteristics of the sample 4. The analysis unit 3 repeatedly corrects the assumed characteristics of the sample 4 and calculates the energy using Monte Carlo ray tracing, so as to reduce the error between the measured spectrum and the virtual spectrum.

[0104] The assumed characteristics of the sample 4 are corrected so that the measured spectrum and the hypothetical spectrum approximately match, and the analysis unit 3 can determine the corrected characteristics of the sample 4 as the actual characteristics of the sample 4. In this way, the analysis unit 3 can accurately analyze the characteristics of the sample 4. That is, the analysis unit 3 can accurately determine the type, concentration, and location of elements contained in the sample 4.

[0105] The trajectories of photons contained in the radiation generated from the sample 4 are also affected by the shape of the sample 4. In this embodiment, the analysis unit 3 performs Monte Carlo ray tracing processing after identifying the shape of the sample 4. As a result, the trajectories of photons contained in the radiation generated from the sample 4 are calculated with high accuracy, and the characteristics of the sample 4 are analyzed with high accuracy.

[0106] Examples of measured spectra and hypothetical spectra will be described. A measured spectrum of a Sn (tin) alloy containing 832 ppm of Cd (cadmium) and a hypothetical spectrum of a Cd-free Sn alloy were created. FIG. 10 is a graph showing examples of the measured spectrum of a Cd-containing Sn alloy and the hypothetical spectrum of a Cd-free Sn alloy. The horizontal axis in the graph represents the energy of radiation (X-rays), and the vertical axis represents the intensity of radiation (X-rays). In FIG. 10, the graph represented by circles shows the measured spectrum of a Cd-containing Sn alloy. The graph labeled "Simulation" shows the hypothetical spectrum of a Cd-free Sn alloy. The hypothetical spectrum of a Cd-free Sn alloy is an integration of hypothetical spectra caused by various phenomena. FIG. 10 shows multiple hypothetical spectra caused by multiple phenomena.

[0107] During radiation detection, various phenomena may occur in the radiation detection element 11. Photoelectric absorption occurs in the sensitive region 117, and the generated fluorescence and electrons are absorbed. Compton scattering also occurs in the sensitive region 117, and the scattered photons and electrons are absorbed in the sensitive region 117. The hypothetical spectrum resulting from these phenomena is marked with (1) in FIG. 10. Photoelectric absorption occurs in the sensitive region 117, and the generated fluorescence escapes outside the sensitive region 117. The hypothetical spectrum resulting from this phenomenon is marked with (2) in FIG. 10. Photoelectric absorption occurs in a region not included in the sensitive region 117 near the incident surface 111 of the radiation detection element 11, and the generated fluorescence escapes outside the sensitive region 117. The hypothetical spectrum resulting from this phenomenon is marked with (3) in FIG. 10.

[0108] Photoelectric absorption occurs in a region not included in the sensitive region 117 near the incident surface 111 of the radiation detection element 11, and the generated fluorescence enters the sensitive region 117. The hypothetical spectrum resulting from this phenomenon is marked with (4) in FIG. 10. Photoelectric absorption occurs in the sensitive region 117, and some of the generated charges recombine in a region not included in the sensitive region 117 and do not flow into the signal output electrode 115. The hypothetical spectrum resulting from this phenomenon is marked with (5) in FIG. 10. Compton scattering occurs in the sensitive region 117, and the scattered photons exit the sensitive region 117. The hypothetical spectrum resulting from this phenomenon is marked with (6) in FIG. 10. Compton scattering occurs within the circuit board 14, and the scattered photons reach the sensitive region 117 and are absorbed. The hypothetical spectrum resulting from this phenomenon is marked with (7) in FIG. 10. The virtual spectra resulting from each phenomenon are separated by phenomenon by recording what phenomenon each photon causes and is detected by the radiation detection element 11 .

[0109] In Figure 10, the hypothetical spectrum of the Cd-free Sn alloy marked with "Simulation" is the sum of multiple hypothetical spectra resulting from multiple phenomena. By comparing the measured spectrum of the Cd-containing Sn alloy with the hypothetical spectrum of the Cd-free Sn alloy, a Cd peak near 23 keV can be confirmed. In this example, it is clear that to quantify Cd, it is necessary to accurately estimate the spectra resulting from tailing marked with (5), Compton backscattering marked with (7), and fluorescence escape marked with (2).

[0110] 11 is a graph showing an example of the measured spectrum of a Sn alloy containing Cd, the hypothetical spectrum of a Sn alloy not containing Cd, and the hypothetical spectrum of a Sn alloy containing Cd. The horizontal axis in the figure represents the energy of radiation (X-rays), and the vertical axis represents the intensity of radiation (X-rays). In FIG. 11, the measured spectrum of a Sn alloy containing 832 ppm of Cd is labeled (1), the hypothetical spectrum of a Sn alloy not containing Cd is labeled (2), and the hypothetical spectrum of a Sn alloy containing Cd is labeled (3). The hypothetical spectrum of a Sn alloy containing Cd is closer to the measured spectrum of a Sn alloy containing Cd than the hypothetical spectrum of a Sn alloy not containing Cd. By creating a calibration curve for the Cd concentration, quantitative analysis of the Cd concentration becomes possible.

[0111] In this embodiment, the shape of the sample 4 is identified and then the characteristics of the sample 4 are analyzed, but the analysis unit 3 may be configured to use the shape of the sample 4 as a characteristic of the sample 4 to be analyzed. In this configuration, the analysis unit 3 does not perform the process of S103, but assumes the shape of the sample 4 as one of the characteristics of the sample 4 in S104, corrects the shape of the sample 4 in S111, and determines the shape of the sample 4 in S112.

[0112] In this embodiment, the Monte Carlo ray tracing process is performed using the flight distance of the photon, but the analysis unit 3 may be configured to perform the Monte Carlo ray tracing process without using the flight distance. In this configuration, the parallel computing unit 36 ​​performs the process of tracing the photon by sequentially calculating the interactions between the photon and objects on the flight path of the photon.

[0113] In this embodiment, the analysis unit 3 executes the Monte Carlo ray tracing process of S105 using the parallel calculation unit 36. However, the analysis unit 3 may execute the process of S105 without using the parallel calculation unit 36. For example, the analysis unit 3 may execute the Monte Carlo ray tracing process for a plurality of photons through calculations in the calculation unit 31. In this embodiment, the calculation unit includes the calculation unit 31 but does not include the parallel calculation unit 36. The analysis unit 3 can also execute the Monte Carlo ray tracing process of S105 without specifying the shape of the sample 4. For example, the analysis unit 3 executes the process assuming that the shape of the sample 4 is a predetermined shape.

[0114] In this embodiment, the radiation detection element 11 is made of a semiconductor such as Si, but the radiation detection element 11 may be made of a semiconductor other than Si. In this embodiment, the radiation detection element 11 is made of a silicon drift type radiation detection element, but the radiation detection element 11 may be made of a semiconductor other than a silicon drift type radiation detection element. Therefore, the radiation detector 1 may be a radiation detector other than an SDD.

[0115] In this embodiment, the radiation detector 1 includes the collimator 13, but the radiation detector 1 may not include the collimator 13. The radiation detector 1 may not include the cold finger 17. In this embodiment, for example, the heat dissipating end of the Peltier element 15 contacts the housing 16 directly or via an intervening material, and heat is dissipated through the housing 16. The radiation detector 1 does not need to be provided with the window 161, and an opening that is not covered by a window material may be formed at the tip of the housing 16. In this embodiment, the radiation detector 1 includes the housing 16, but the radiation detector 1 may not include the housing 16.

[0116] In the present embodiment, the radiation detection device 100 is shown to include the irradiation unit 26, but the radiation detection device 100 may be shown to not include the irradiation unit 26. In the present embodiment, the radiation detection element 11 made of semiconductor is shown to be used, but the radiation detection device 100 may be shown to detect radiation using a radiation detection element other than a semiconductor radiation detection element. In the present embodiment, the radiation detection device 100 is shown to handle a spectrum in which the horizontal axis indicates the energy of radiation, but the radiation detection device 100 may be shown to handle a spectrum in which the horizontal axis indicates the wavelength or wavenumber of radiation.

[0117] In this embodiment, the radiation detection device 100 is shown to irradiate the sample 4 with radiation consisting of a plurality of photons and detect the radiation, but the radiation detection device 100 may be configured to handle radiation consisting of a plurality of particles. For example, the radiation detection device 100 may be configured to irradiate the sample 4 with radiation consisting of a plurality of particles and detect radiation consisting of a plurality of photons generated from the sample 4. In this configuration, the radiation detection device 100 can perform processing in which some of the photons described in this embodiment are replaced with particles.

[0118] The present invention is not limited to the contents of the above-described embodiment, and various modifications are possible within the scope of the claims. In other words, embodiments obtained by combining technical means modified appropriately within the scope of the claims are also included in the technical scope of the present invention.

[0119] The matters described in each embodiment can be combined with each other. Furthermore, the independent claims and dependent claims described in the claims can be combined with each other in any and all combinations, regardless of the reference format. Furthermore, the claims use a format in which a claim references two or more other claims (multiple claim format), but this is not limited to this. A multiple claim (multi-multi claim) that references at least one other multiple claim may also be used.

[0120] REFERENCE SIGNS LIST 100 Radiation detection device 1 Radiation detector 11 Radiation detection element 117 Sensitive part 22 Signal processing part 26 Irradiation part 3 Analysis part (information processing device) 30 Recording medium 31 Calculation part 34 Storage part 341 Computer program 36 Parallel calculation part 4 Sample

Claims

1. An information processing method comprising: acquiring a measured spectrum of radiation generated from a sample irradiated with radiation and detected using a radiation detector; assuming characteristics of the sample; calculating the energy specified when each photon is detected by performing Monte Carlo ray tracing using the assumed characteristics of the sample for each of a plurality of photons contained in the radiation irradiated to the sample and the radiation generated from the sample; generating a virtual spectrum of the detected radiation by counting the number of photons for each calculated energy; modifying the assumed characteristics of the sample based on a comparison between the measured spectrum and the virtual spectrum; and determining the modified characteristics as the characteristics of the sample.

2. The information processing method according to claim 1, characterized in that the Monte Carlo ray tracing identifies an object that interacts with each photon, probabilistically determines the type of interaction between each photon and the object, probabilistically calculates the energy or direction of travel of the photon that is generated or changed by the interaction, and calculates the energy of the photon that is detected as a result of the interaction between the photon and a radiation detection element included in the radiation detector.

3. The information processing method according to claim 2, characterized in that the Monte Carlo ray tracing probabilistically calculates the flight distance of each photon, and identifies objects that interact with each photon based on the position and size of objects onto which each photon may be incident and the flight distance.

4. An information processing method according to any one of claims 1 to 3, characterized in that it probabilistically determines whether the time intervals at which multiple photons whose energies have been calculated are detected are short or not, and corrects the energy calculated using the Monte Carlo ray tracing by adding up the energies of multiple photons whose detection intervals are determined to be short, and setting the sum to the energy of a single photon.

5. An information processing method according to any one of claims 1 to 4, characterized in that the assumed characteristics of the sample are the composition, shape, structure of the sample, or the type, concentration, position or distribution of elements contained in the sample.

6. An information processing method according to any one of claims 1 to 5, characterized in that the shape of the sample is specified, and Monte Carlo ray tracing is performed using the assumed characteristics of the sample and the specified shape of the sample.

7. A computer program causing a computer to perform the following processes: for each of a plurality of photons contained in radiation irradiated to a sample and radiation generated from the sample, calculate the energy specified when each photon is detected using a radiation detector by performing Monte Carlo ray tracing using assumed characteristics of the sample; count the number of photons for each calculated energy to generate a virtual spectrum of the detected radiation; modify the assumed characteristics of the sample in accordance with a comparison between the virtual spectrum and the actual spectrum of radiation generated from the sample and detected using the radiation detector; and determine the modified characteristics as the characteristics of the sample.

8. An information processing device comprising a calculation unit, which acquires an actual measured spectrum of radiation generated from a sample irradiated with radiation and detected using a radiation detector, assumes characteristics of the sample, calculates the energy specified when each photon is detected by performing Monte Carlo ray tracing using the assumed characteristics of the sample for each of a plurality of photons contained in the radiation irradiated to the sample and the radiation generated from the sample, generates a virtual spectrum of the detected radiation by counting the number of photons for each calculated energy, modifies the assumed characteristics of the sample in accordance with a comparison between the actual measured spectrum and the virtual spectrum, and determines the modified characteristics as the characteristics of the sample.

9. The information processing device according to claim 8, wherein the calculation unit has a parallel calculation unit, and the parallel calculation unit executes in parallel a process of calculating, for each of a plurality of photons, the energy that is identified when each photon is detected by performing the Monte Carlo ray tracing.

10. A radiation detection device comprising: an irradiation unit that irradiates a sample with radiation; a radiation detector that detects radiation from the sample; and the information processing device according to claim 8 or 9.

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