SEU Cross-Section Estimation Device, SEU Cross-Section Estimation Method, and SEU Cross-Section Estimation Program

Abstract

To estimate an SEU cross section of a semiconductor device whose SEU cross section is unknown with a simple method.SOLUTION: An SEU cross section estimation device comprises: a storage unit 15 that stores a soft error generation rate for each target in a plurality of reference devices with a known SEU cross section when a plurality of targets made of different materials is irradiated with each neutron generated by irradiating each target with a particle radiation; a calculation unit 12 that calculates a soft error generation rate when a measurement target device is irradiated with each neutron; a determination unit 16 that determines the degree of similarity between the soft error generation rate of a measurement target device 4 and the soft error generation rate of the reference device; and an estimation unit 17 that estimates an SEU cross section of the measurement target device 4 based on the SEU cross section of the reference device determined to have the highest degree of similarity to the measurement target device 4.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an SEU cross-section estimation device, an SEU cross-section estimation method, and an SEU cross-section estimation program.

Background Art

[0002] When cosmic rays pouring down from space collide with oxygen or nitrogen in the atmosphere, neutron rays are generated and pour down to the earth. On the other hand, when neutron rays are incident on a circuit such as an LSI used in many electronic devices, a soft error occurs in which the bits of data stored in the LSI are inverted by the charges generated by the nuclear reaction.

[0003] Soft errors are called SEU (Single Event Upset), and are events in which a single particle radiation (neutron, proton, heavy particle, etc.) is incident on a semiconductor device such as a memory, and the data (bits) stored in the semiconductor device are inverted by the charges generated by the nuclear reaction. Further, the rate at which particle radiation causes SEU is referred to as the SEU cross-section.

[0004] When the number of SEUs is N when a semiconductor is irradiated with particle radiation having a fluence φ [n / cm2], the SEU cross-section is represented by the following formula (1).

[0005] (SEU cross-section) = N / φ …(1) As a method for measuring the SEU cross-section, for example, the time-of-flight method disclosed in Non-Patent Documents 1 and 2 and Patent Document 1 is known. The time-of-flight method is a method of calculating the speed of particle radiation by measuring the time required for flight over a certain distance and converting it into particle energy.

[0006] By generating pulsed neutrons using an accelerator and a nuclear reactor, installing a detector at a certain distance, and measuring the difference between the generation time of the pulsed neutrons and the time when the neutrons are detected, that is, the flight time, it becomes possible to specify the energy of the neutrons.

Prior Art Documents

Non-Patent Documents

[0007]

Non-Patent Document 1

Non-Patent Document 2

Patent Documents

[0008]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0009] However, in order to measure neutrons with high energies on the order of MeV, large-scale facilities are required, and there are limited facilities in the world that can perform such measurements. It is difficult to measure the SEU cross-section using such large-scale facilities. Also, when the device to be measured is an FPGA (Field Programmable Gate Array), although a high-speed detection circuit can be incorporated to measure the neutron velocity by the time-of-flight method, for devices where it is difficult to incorporate a high-speed detection circuit such as SRAM, soft errors cannot be detected at high speed. For example, in order to scan the entire memory, it takes a long time of several milliseconds to several tens of milliseconds.

[0010] The present invention has been made to solve the above problems, and an object thereof is to provide an SEU cross-section estimation device, an SEU cross-section estimation method, and an SEU cross-section estimation program capable of estimating the SEU cross-section of a semiconductor device with an unknown SEU cross-section by a simple method.

Means for Solving the Problems

[0011] An SEU cross-section estimation device according to an aspect of the present invention includes a storage unit that stores, for each target, a soft error occurrence rate when each neutron generated by irradiating a plurality of reference devices with known SEU cross-sections with particle radiation to a plurality of targets having different materials is irradiated; an arithmetic unit that calculates a soft error occurrence rate when the measurement target device is irradiated with each of the neutrons; a determination unit that determines a similarity between the soft error occurrence rate of the measurement target device and the soft error occurrence rate of the reference device; and an estimation unit that estimates the SEU cross-section of the measurement target device based on the SEU cross-section of the reference device determined to have the highest similarity to the measurement target device.

[0012] An SEU cross-section estimation method according to an aspect of the present invention includes a step of storing, for each target, a soft error occurrence rate when each neutron generated by irradiating a plurality of reference devices with known SEU cross-sections with particle radiation to a plurality of targets having different materials is irradiated; a step of calculating a soft error occurrence rate when the measurement target device is irradiated with each of the neutrons; a step of determining a similarity between the soft error occurrence rate of the measurement target device and the soft error occurrence rate of the reference device; and a step of estimating the SEU cross-section of the measurement target device based on the SEU cross-section of the reference device determined to have the highest similarity to the measurement target device.

[0013] One aspect of the present invention is an SEU cross-section estimation program for causing a computer to function as the SEU cross-section estimation device described above.

Advantages of the Invention

[0014] According to the present invention, it becomes possible to estimate the SEU cross-section of a semiconductor device whose SEU cross-section is unknown by a simple method.

Brief Description of the Drawings

[0015]

Figure 1

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Mode for Carrying Out the Invention

[0016] [Description of the First Embodiment] Hereinafter, an SEU cross-section estimation device according to an embodiment will be described with reference to the drawings. FIG. 1 is a block diagram showing the configuration of a soft error estimation device (hereinafter abbreviated as "estimation device 1") according to an embodiment, and its peripheral devices, namely, an accelerator 2, a target 3, and a device under measurement 4.

[0017] The accelerator 2 accelerates and outputs particle radiation such as protons and heavy particles. In the present embodiment, an example using protons as the particle radiation will be described.

[0018] As shown in FIG. 2, when the target 3 is irradiated with protons output from the accelerator 2, neutrons are output. The target 3 is made of a material such as beryllium (Be), lead (Pb), tantalum (Ta), tungsten (W), etc., and is installed at an appropriate position on the output side of the accelerator 2. By changing the material of the target 3, the neutron fluence (the amount of neutrons radiated per unit area) and the neutron spectrum radiated from the target 3 change.

[0019] The neutron spectrum is data showing the relationship between neutron energy and the number of neutrons. The neutron fluence and the neutron spectrum in the target 3 of each material are known data. In the present embodiment, an example using beryllium (Be), lead (Pb), tantalum (Ta), and tungsten (W) as the material of the target 3 will be described, but the present invention is not limited thereto, and a target 3 of other materials may be used.

[0020] FIG. 3 is a graph showing the neutron spectrum emitted from target 3, where the horizontal axis represents neutron energy [MeV] and the vertical axis represents the number of neutrons. The curves p1, p2, p3, and p4 shown in FIG. 3 respectively represent the neutron spectra emitted from target 3 when the material of target 3 is tantalum (Ta), beryllium (Be), tungsten (W), and lead (Pb). The neutron fluence and the data of the neutron spectrum are output to the estimation device 1.

[0021] The device 4 to be measured is a semiconductor device for which the SEU cross-section is to be estimated. That is, the SEU cross-section of the device 4 to be measured is unknown. The device 4 to be measured is a substrate on which semiconductors such as FPGA and SRAM (Static Random Access Memory) are mounted. When the neutrons output from target 3 irradiate the device 4 to be measured, soft errors occur in the device 4 to be measured. The information on the soft errors that occurred in the device 4 to be measured is output to the estimation device 1.

[0022] The estimation device 1 (SEU cross-section estimation device) includes an input unit 11, a calculation unit 12, a calculation data storage unit 13, an existing data storage unit 14, a storage unit 15, a determination unit 16, an estimation unit 17, and an output unit 18.

[0023] The input unit 11 acquires information on the neutron fluence irradiated from target 3 when protons irradiate target 3 from the accelerator 2. The input unit 11 acquires information on the occurrence of soft errors that occur when the device 4 to be measured is irradiated with neutrons. The input unit 11 acquires information on the neutron fluence for each target 3 of various materials and information on the soft errors that occurred in the device 4 to be measured when the material of target 3 is changed to various materials.

[0024] Specifically, when the target 3 is beryllium (Be), lead (Pb), tantalum (Ta), and tungsten (W), the input unit 11 acquires information on neutron fluence and soft error information. The input unit 11 outputs the acquired information to the calculation unit 12.

[0025] Based on the information on soft errors generated in the device under measurement 4, the calculation unit 12 calculates the soft error occurrence rate for each target (i.e., for each neutron spectrum). Specifically, based on the information on soft errors generated in the device under measurement 4 when the target 3 is beryllium (Be), lead (Pb), tantalum (Ta), and tungsten (W), the calculation unit 12 calculates the soft error occurrence rate for each target 3. The calculation unit 12 calculates the soft error occurrence rates as shown by the reference signs P1 to P4 in FIG. 5 described later, for example. That is, the calculation unit 12 calculates the soft error occurrence rate when the device under measurement 4, which is the measurement target of the SEU cross-section, is irradiated with neutrons generated by irradiating each target 3 with particle radiation.

[0026] The calculation data storage unit 13 stores the soft error occurrence rate calculated by the calculation unit 12.

[0027] The existing data storage unit 14 stores data on the SEU cross-sections of a plurality of FPGAs. The SEU cross-section of each FPGA is known data previously measured using well-known techniques such as the time-of-flight method and the monochromatic method. Hereinafter, a semiconductor device such as an FPGA with a known SEU cross-section is referred to as a "reference device". In this embodiment, an example in which "FPGA 28nm", "FPGA 40nm", and "FPGA 55nm" are used as reference devices will be described. The existing data storage unit 14 stores data on the SEU cross-sections for each of "FPGA 28nm", "FPGA 40nm", and "FPGA 55nm".

[0028] Note that "28nm", "40nm", and "55nm" indicate the line width formed on the substrate of the FPGA. For example, "FPGA28nm" is an FPGA on which a pattern with a line width of 28nm is formed. In this embodiment, an example of using an FPGA as a reference device will be described, but the present invention is not limited to this, and other semiconductor devices may be used.

[0029] Figure 4 is a graph showing the SEU cross-section of each reference device, where the horizontal axis represents the neutron energy [MeV] and the vertical axis represents the SEU cross-section [cm2 / Mbit]. The curve p11 shown in Figure 4 represents the SEU cross-section of FPGA28nm, the curve p12 represents the SEU cross-section of FPGA40nm, and the curve p13 represents the SEU cross-section of FPGA55nm.

[0030] The storage unit 15 stores the soft error occurrence rate caused by neutrons generated using various targets 3 for each reference device. That is, it stores the soft error occurrence rate for different neutron spectra. As shown in Figure 3, based on the graph showing the relationship between neutron energy and the number of neutrons when using targets 3 of multiple materials, and the data of the SEU cross-sections of multiple reference devices (FPGAs) shown in Figure 4, the soft error occurrence rate caused by neutrons generated using various targets 3 for each reference device is obtained.

[0031] Specifically, as shown in Figure 5, data with the horizontal axis as the target 3 (Be, Pb, Ta, W) and the vertical axis as the soft error occurrence rate is obtained, and this data is stored in the storage unit 15. Figure 5 shows the soft error occurrence rate of each reference device normalized by the soft error occurrence rate of FPGA28nm. The curve p21 shown in Figure 5 represents the soft error occurrence rate of FPGA28nm, the curve p22 represents the soft error occurrence rate of FPGA40nm, and the curve p23 represents the soft error occurrence rate of FPGA55nm. That is, the storage unit 15 stores the soft error occurrence rate for each target 3 when irradiating neutrons generated by irradiating a plurality of reference devices with known SEU cross-sections with particle radiation from a plurality of targets 3 with different materials.

[0032] The determination unit 16 compares the soft error occurrence rate of the device under measurement 4 stored in the calculation data storage unit 13 with the soft error occurrence rate of the reference device stored in the storage unit 15, and determines which reference device's soft error occurrence rate the soft error occurrence rate of the device under measurement 4 is similar to. That is, the determination unit 16 determines the similarity between the soft error occurrence rate of the device under measurement 4 and the soft error occurrence rate of the reference device.

[0033] For example, assume that when the device under measurement 4 is irradiated with neutrons using targets 3 of various materials, the soft error occurrence rates occurring in the device under measurement 4 are those indicated by reference signs P1, P2, P3, and P4 shown in FIG. 5. In this case, it is determined that the similarity between the soft error occurrence rate of the device under measurement 4 and the soft error occurrence rate of the FPGA 55nm shown by the curve p23 is high. As an example of a specific determination method, there is a determination method using the least squares method. Let the respective soft error occurrence rates be aP1, aP2, aP3, and aP4, search for the coefficient a that minimizes the sum of the squares of the residuals of each of p23, p22, and p11 of the model curve, and determine that the model curve with the smallest sum of the squares of the residuals is the similar curve.

[0034] When the reference device with a high similarity is specified by the determination unit 16, the estimation unit 17 recognizes the relationship between the soft error occurrence rate of this reference device and the soft error occurrence rate of the device under measurement 4. For example, as shown in FIG. 5, the soft error occurrence rate of the device under measurement 4 is similar to the soft error occurrence rate of the FPGA 55nm, and further, it is recognized that the soft error occurrence rate is about 20% lower than that of the FPGA 55nm.

[0035] The estimation unit 17 estimates that the SEU cross section of the device under measurement 4 is about 20% lower than the SEU cross section of the FPGA 55nm shown by the curve p23. The estimation unit 17 estimates that the SEU cross section of the device under measurement 4 is data that is about 20% lower than the SEU cross section of the FPGA 55nm shown by the curve p13 in FIG. 4.

[0036] That is, the estimation unit 17 calculates the ratio between the soft error occurrence rate of the reference device determined to have a high similarity by the determination unit 16 and the soft error occurrence rate of the device under measurement 4, and estimates the SEU cross section of the device under measurement 4 based on the SEU cross section of the reference device determined to have a high similarity and the ratio.

[0037] The output unit 18 outputs the data estimated by the estimation unit 17 to the outside.

[0038] Next, the procedure of the SEU cross section estimation process in the estimation device 1 shown in the first embodiment will be described with reference to the flowchart shown in FIG. 6.

[0039] First, in step S11 shown in FIG. 6, the existing data storage unit 14 acquires and stores data indicating the relationship between the neutron energy and the SEU cross section of a plurality of reference devices whose SEU cross sections are known. Specifically, the data of the SEU cross sections of FPGA28nm, FPGA40nm, and FPGA55nm shown in FIG. 4 are stored. The cross section of the reference device is obtained, for example, by using a large-scale facility and, for example, by the time-of-flight method.

[0040] In step S12, the input unit 11 acquires the soft error occurrence rate when neutrons generated by irradiating a plurality of targets 3 with protons are irradiated to the device under measurement 4. Specifically, the data of points P1 to P4 shown in FIG. 5 are acquired.

[0041] In step S13, the determination unit 16 identifies a reference device having a high similarity in soft error occurrence rate with respect to the device under measurement 4. Specifically, since the data of points P1 to P4 shown in FIG. 5 changes similarly to the curve p23, it is identified that the reference device with a high similarity is FPGA55nm.

[0042] In step S14, the estimation unit 17 recognizes the relationship between the soft error occurrence rate of the device under measurement 4 and the soft error occurrence rate of the reference device identified in the process of step S13. Specifically, it is recognized that the data of points P1 to P4 shown in FIG. 5 is about 20% lower than the graph of FPGA 55nm. Further, based on the SEU cross-section of FPGA 55nm, the SEU cross-section of the device under measurement 4 is estimated.

[0043] Specifically, it is estimated that the SEU cross-section of the device under measurement 4 will have a graph that is about 20% lower than the curve p13 (SEU cross-section of FPGA 55nm) shown in FIG. 4. This estimation result is output from the output unit 18. Then, this process ends. The user can recognize the SEU cross-section of the device under measurement 4 based on this estimation result.

[0044] As described above, the estimation device 1 (SEU cross-section estimation device) according to this embodiment irradiates each neutron generated by irradiating a plurality of reference devices with known SEU cross-sections with particle radiation to a plurality of targets 3 made of different materials, and stores the soft error occurrence rate for each target 3 in a storage unit 15, calculates the soft error occurrence rate when irradiating each neutron to the device under measurement 4 in an arithmetic unit 12, determines the similarity between the soft error occurrence rate of the device under measurement 4 and the soft error occurrence rate of the reference device in a determination unit 16, and estimates the SEU cross-section of the device under measurement 4 based on the SEU cross-section of the reference device determined to have the highest similarity to the device under measurement 4 in an estimation unit 15.

[0045] By using the estimation device 1 according to this embodiment, it is possible to estimate the SEU cross-section of a semiconductor device (device under measurement 4) with an unknown SEU cross-section by using the SEU cross-section of the reference device. Therefore, it becomes possible to estimate the SEU cross-section of the device under measurement 4 in an extremely simple method without using a large-scale facility.

[0046] In addition, as the target 3, beryllium (Be), lead (Pb), tantalum (Ta), and tungsten (W) are used. By irradiating protons onto the target 3 made of each material, different neutron spectra can be generated, and it becomes possible to accurately estimate the SEU cross section.

[0047] In addition, the ratio between the soft error occurrence rate of the reference device determined to have a high similarity by the determination unit 16 and the soft error occurrence rate of the device under measurement 4 is calculated. For example, in the example shown in FIG. 5, it is determined that the soft error occurrence rates (P1 to P4) of the device under measurement 4 are about 20% lower than the soft error occurrence rate (curve p23) of the FPGA 55 nm. Since the estimation unit 17 estimates the SEU cross section of the device under measurement 4 based on this ratio, the estimation accuracy of the SEU cross section can be improved.

[0048] In addition, even if the device to be measured is a device in which it is difficult to incorporate a high-speed detection circuit such as SRAM, it becomes possible to easily estimate the SEU cross section.

[0049] [Description of the Second Embodiment] Next, the second embodiment will be described. The estimation device 1 according to the second embodiment has the same configuration as the first embodiment described above. In the second embodiment, the structure of the target 3 that generates neutrons for irradiating the device under measurement 4 is different from that of the first embodiment.

[0050] In addition, in the second embodiment, the storage unit 15 stores a graph showing the relationship between the thickness of aluminum (the second material 32) and the soft error occurrence rate as shown in FIG. 9 to be described later, which is different from the first embodiment. Hereinafter, the second embodiment will be described in detail.

[0051] FIG. 7 is an explanatory diagram showing the configuration of the target 30 used when estimating the SEU cross section by the estimation device 1 according to the second embodiment. As shown in FIG. 7, in the estimation device 1 according to the second embodiment, a target 30 having a two-layer structure in which a second material 32 is laminated on a first material 31 is used.

[0052] As an example, a target 30 formed by laminating a second material 32 with a variable thickness on a first material 31 having a constant thickness (for example, 3 mm) is used. The first material 31 is, for example, beryllium (Be), and the second material is, for example, aluminum (Al). By using a plurality of targets 30 with different thicknesses of aluminum, the neutron spectrum output from the target 30 is changed and the measurement target device 4 is irradiated.

[0053] In the first embodiment described above, a plurality of targets 3 made of different materials are used, whereas in the second embodiment, a plurality of targets 30 with different thicknesses of the second material 32 are used to generate neutrons. That is, the plurality of targets 30 includes a first target and a second target. The first target and the second target have a laminated structure of a member of the first material (the first material 31) and a member of the second material (the second material 32), and the members of the second material of the first target and the members of the second material of the second target have different plate thicknesses. The storage unit 15 stores the soft error occurrence rate for each target 30 when neutrons generated by irradiating the first target and the second target with protons (particle radiation) are irradiated to each reference device.

[0054] FIG. 8 is a graph showing the neutron spectrum when the thickness of aluminum is changed in the range of 0.0 to 1.5 mm. The curves q1, q2, q3, q4, and q5 shown in FIG. 8 are graphs showing the neutron spectrum when the thickness of aluminum used as the second material 32 is 0.0 mm, 0.2 mm, 0.5, 1.0 mm, and 1.5 mm, respectively.

[0055] As shown in FIG. 9, the storage unit 15 stores a graph showing the relationship between the aluminum thickness and the soft error rate for each reference device. The graph shown in FIG. 9 shows data normalized by the soft error rate of FPGA 28 nm. The curve q21 shown in FIG. 9 indicates the soft error rate for FPGA 28 nm, the curve q22 indicates the soft error rate for FPGA 40 nm, and the curve q23 indicates the soft error rate for FPGA 55 nm.

[0056] Points Q1, Q2, Q3, and Q4 shown in FIG. 9 indicate the soft error rates that occur in the measurement target device 4 when the measurement target device 4 is irradiated with neutrons using a plurality of targets 30 having different aluminum thicknesses.

[0057] That is, in the second embodiment, the storage unit 15 irradiates a plurality of targets 30 in which a member of a second material having various plate thicknesses is laminated on a member of a first material having a fixed plate thickness with particle radiation, and stores the soft error rates for each target 30 when the generated neutrons are irradiated to each reference device.

[0058] As understood from FIG. 9, points Q1 to Q4 are similar to the curve q22. Therefore, the determination unit 16 determines that the soft error rate of the measurement target device 4 has a high similarity to that of FPGA 40 nm, which is a reference device.

[0059] Also, the soft error rates of points Q1 to Q4 are about 20% higher than those of the curve q22. The estimation unit 17 estimates that the SEU cross section of the measurement target device 4 is 20% higher than the SEU cross section of FPGA 40 nm. Specifically, it is estimated that the value is 20% higher than the curve p12 shown in FIG. 4.

[0060] Thus, in the second embodiment, a target 30 having a laminated structure of a first material 31 (a member of the first material) and a second material 32 (a member of the second material) is used, and neutrons are generated. Based on the soft error occurrence rates detected using each target 30 with a different thickness of the second material 32, the SEU cross-section of the device under measurement 4 is estimated.

[0061] Therefore, in the estimation device 1 according to the second embodiment, similar to the first embodiment described above, the SEU cross-section of a semiconductor device (device under measurement 4) with an unknown SEU cross-section can be estimated using the SEU cross-section of a reference device. Thus, the SEU cross-section of the device under measurement 4 can be estimated without using a large-scale facility.

[0062] In the estimation device 1 according to the second embodiment, neutrons are generated using a plurality of types of targets 30 with appropriately changed thicknesses of the second material 32. Therefore, it is not necessary to prepare targets of many materials, and the estimation of the SEU cross-section can be further simplified.

[0063] For the SEU cross-section estimation device (estimation device 1) according to the above-described embodiment, as shown in FIG. 10, for example, a general-purpose computer system including a CPU (Central Processing Unit), a memory 902, a storage 903 (HDD: Hard Disk Drive, SSD: Solid State Drive), a communication device 904, an input device 905, and an output device 906 can be used. The memory 902 and the storage 903 are storage devices. In this computer system, each function of the estimation device 1 is realized by the CPU 901 executing a predetermined program loaded onto the memory 902.

[0064] Note that the estimation device 1 may be implemented by one computer or may be implemented by a plurality of computers. Further, the estimation device 1 may be a virtual machine implemented on a computer.

[0065] Note that the program for the estimation device 1 can be stored in a computer-readable recording medium such as an HDD, SSD, USB (Universal Serial Bus) memory, CD (Compact Disc), or DVD (Digital Versatile Disc), or can be distributed via a network.

[0066] Note that the present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the gist thereof.

Explanation of Reference Numerals

[0067] 1 Estimation device (SEU cross-section estimation device) 2 Accelerator 3, 30 Target 4 Measurement target device 11 Input unit 12 Arithmetic unit 13 Arithmetic data storage unit 14 Existing data storage unit 15 Storage unit 16 Determination unit 17 Estimation unit 18 Output unit 31 First material (member of the first material) 32 Second material (member of the second material)

Claims

1. A storage unit that stores, for each target, the soft error rate when each neutron generated by irradiating a plurality of reference devices with known SEU cross-sections with particle radiation to irradiate a plurality of targets made of different materials; An arithmetic unit that calculates the soft error rate when irradiating the device under measurement with each of the neutrons; A determination unit that determines the similarity between the soft error rate of the device under measurement and the soft error rate of the reference device; An estimation unit that estimates the SEU cross-section of the device under measurement based on the SEU cross-section of the reference device determined to have the highest similarity to the device under measurement; An SEU cross-section estimation device comprising:

2. The estimation unit calculates a ratio between the soft error rate of the reference device determined to have the highest similarity by the determination unit and the soft error rate of the device under measurement, and estimates the SEU cross-section of the device under measurement based on the SEU cross-section of the reference device determined to have the highest similarity and the ratio. The SEU cross-section estimation device according to claim 1.

3. The material of each of the targets is any one of beryllium (Be), tantalum (Ta), lead (Pb), and tungsten (W). The SEU cross-section estimation device according to claim 1 or 2.

4. The plurality of targets include a first target and a second target. The first target and the second target have a laminated structure of a member of a first material and a member of a second material. The member of the second material of the first target and the member of the second material of the second target have different plate thicknesses. The storage unit stores, for each target, the soft error rate when neutrons generated by irradiating the first target and the second target with the particle radiation are irradiated to each of the reference devices. The SEU cross-section estimation device according to claim 1 or 2.

5. The member of the first material is beryllium (Be), and the member of the second material is aluminum (Al). The SEU cross-section estimation device according to claim 4.

6. When each of the targets is irradiated with the particle radiation, different neutron spectra are generated. The SEU cross-section estimation device according to any one of claims 1 to 5.

7. Storing, for each target, the soft error rate when each neutron generated by irradiating each of a plurality of targets made of different materials with particle radiation is irradiated to a plurality of reference devices with known SEU cross-sections; Calculating the soft error rate when the measurement target device is irradiated with each of the neutrons; Determining the similarity between the soft error rate of the measurement target device and the soft error rate of the reference device; Estimating the SEU cross-section of the measurement target device based on the SEU cross-section of the reference device determined to have the highest similarity to the measurement target device; An SEU cross-section estimation method comprising:

8. An SEU cross-section estimation program that causes a computer to function as the SEU cross-section estimation device according to any one of Claims 1 to 6.

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