WO2018186155A1 - Système et procédé de surveillance de l'intensité des neutrons - Google Patents

Système et procédé de surveillance de l'intensité des neutrons Download PDF

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WO2018186155A1
WO2018186155A1 PCT/JP2018/010675 JP2018010675W WO2018186155A1 WO 2018186155 A1 WO2018186155 A1 WO 2018186155A1 JP 2018010675 W JP2018010675 W JP 2018010675W WO 2018186155 A1 WO2018186155 A1 WO 2018186155A1
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soft error
data
occurrence rate
error occurrence
neutron
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PCT/JP2018/010675
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English (en)
Japanese (ja)
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巧 上薗
鳥羽　忠信
長崎　文彦
健一 新保
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株式会社日立製作所
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
    • G01T3/00—Measuring neutron radiation

Definitions

  • the present invention relates to environmental neutron monitoring technology.
  • Patent Document 1 JP-A-2002-40147 (Patent Document 1).
  • This publication includes a reactive substance that emits ⁇ -rays by neutron reaction, an ⁇ -ray detector that detects ⁇ -rays by a soft error phenomenon, and a control circuit that reads data from the ⁇ -ray detector.
  • a neutron detector that detects the amount of neutrons from the detected amount of alpha rays obtained from a circuit is described.
  • Patent Document 2 discloses a neutron dose measurement technique including a step of obtaining a fast neutron beam dose at an actual measurement location from a ratio of occurrence frequency of soft errors.
  • a commonly used neutron detector is, for example, a Bonner ball type neutron detector.
  • the Bonner ball type neutron detector is composed of a spherical neutron moderator and a 3He proportional counter arranged at the center thereof. Normally, the 3He proportional counter is used for detecting low-energy neutrons and cannot detect high-energy neutrons. However, a high-energy neutron can be detected by arranging a neutron moderator around it.
  • the Bonner Ball type detector requires a high voltage signal processing circuit exceeding 1 kV, and has a problem that the moderator is large and cannot be miniaturized.
  • Patent Document 1 discloses a neutron detector in which a neutron converter material and a semiconductor RAM are combined. Neutrons are indirectly detected by converting neutrons to ⁇ -rays using a neutron converter material, generating soft errors in the semiconductor RAM using the ⁇ -rays, and observing soft error events.
  • a neutron detector for the purpose of neutron measurement in an environment where people live
  • Patent Document 1 discloses a neutron detector in which a neutron converter material and a semiconductor RAM are combined. Neutrons are indirectly detected by converting neutrons to ⁇ -rays using a neutron converter material, generating soft errors in the semiconductor RAM using the ⁇ -rays, and observing soft error events.
  • there is an advantage of low power consumption as compared with a semiconductor detector used as a radiation detector but there is a problem that detectable neutrons depend on the energy characteristics of the reaction between the converter material and the neutrons.
  • neutrons generally collide with hydrogen nuclei and decelerate, so the neutron shielding effect may change depending on the environment and weather conditions.
  • conventional neutron detectors consider the neutron shielding effect due to environmental and weather conditions. There is a problem that is not.
  • an object of the present invention is to provide an environmental neutron intensity monitoring system that can remove local measurement variations due to weather factors and improve neutron measurement accuracy.
  • One aspect of the present invention includes a semiconductor memory, an input unit for inputting environmental sensor data from the environmental sensor, read access to the semiconductor memory to detect a soft error, time information regarding the detection time, and the number of detected soft errors.
  • a soft error detection unit that outputs soft error detection result data, and a calculation unit that calculates a soft error occurrence rate from the soft error detection result data, corrects it with environmental sensor data, and obtains a corrected soft error occurrence rate.
  • a neutron intensity monitoring system is provided.
  • Another aspect of the present invention provides a first input device that receives soft error detection result data detected by read access to a semiconductor memory, and a soft error based on the soft error detection result data.
  • a software error rate calculation unit for calculating the rate of occurrence; a second input device that inputs data relating to the environment of the semiconductor memory and changes with time; and the environment for the soft error rate
  • a neutron intensity monitoring system including a soft error occurrence rate correction unit that performs correction based on data and generates a corrected soft error occurrence rate, and a storage device that stores the corrected soft error occurrence rate as time-series data.
  • One aspect of the present invention is a method for monitoring neutron intensity, wherein data is written to and read from a semiconductor memory to detect a soft error, and a soft error occurrence rate is calculated based on the detected soft error.
  • Step 1 and the information processing apparatus obtains a correction coefficient corresponding to the environmental data corresponding to the detection of the soft error, corrects the soft error occurrence rate with the correction coefficient, and generates a corrected soft error occurrence rate.
  • the perspective view which is an example of a structure of the neutron monitoring system which concerns on 1st embodiment of this invention.
  • the flowchart which is an example of the flow of a process in the soft error detection circuit which concerns on 1st embodiment of this invention.
  • the flowchart which is an example of the flow of a process in the soft error incidence calculation part which concerns on 1st embodiment of this invention.
  • the table which is an example of the neutron increase / decrease rate which concerns on 1st embodiment of this invention.
  • the perspective view which is an example of a structure of the neutron monitoring system which concerns on 2nd embodiment of this invention.
  • the flowchart which is an example of the flow of a process in the soft error incidence prediction part which concerns on 2nd embodiment of this invention.
  • the perspective view which is an example of a structure of the neutron monitoring system which concerns on 3rd embodiment of this invention.
  • the flowchart which is an example of the flow of a process in the neutron energy distribution prediction part which concerns on 3rd embodiment of this invention.
  • the perspective view which is an example of a structure of the neutron monitoring system which concerns on 4th embodiment of this invention.
  • the block diagram which is an example of a structure of the neutron monitoring system which concerns on 5th embodiment of this invention.
  • Notations such as “first”, “second”, and “third” in this specification and the like are attached to identify the constituent elements, and do not necessarily limit the number, order, or contents thereof. is not.
  • a number for identifying a component is used for each context, and a number used in one context does not necessarily indicate the same configuration in another context. Further, it does not preclude that a component identified by a certain number also functions as a component identified by another number.
  • a representative environmental neutron intensity monitoring system includes a semiconductor memory and an environmental sensor, and performs read access to the semiconductor memory to detect occurrence of a soft error, and at least a detection operation period and a soft error detection.
  • a soft error detection unit that outputs soft error detection data consisting of numbers, and a soft error occurrence rate calculation unit that calculates a soft error occurrence rate from the soft error detection data and corrects it with environmental sensor data.
  • FIG. 1 is a configuration diagram of an environmental neutron monitoring system according to an embodiment of the present invention.
  • the neutron monitoring system 1 detects one or more semiconductor memories 2 and a soft error by performing read / write access to the semiconductor memory 2 and outputs soft error detection result data 7.
  • a circuit 3 an environmental sensor 6 that senses environmental information such as atmospheric pressure and outputs environmental data 8, a soft error rate calculation unit 4 that corrects the soft error detection result data 7 with the environmental data 8, and a soft error rate
  • the display unit 9 displays the calculation result of the calculation unit 4 as a display screen 10.
  • FIG. 1 shows an example in which the semiconductor memory 2, the soft error detection circuit 3, and the soft error occurrence rate calculation unit 4 are mounted on the same substrate 5, and the apparatus is configured to operate independently. ing.
  • the semiconductor memory 2, the soft error detection circuit 3, and the soft error occurrence rate calculation unit 4 can be stored in separate substrates or cases.
  • the parts other than the semiconductor memory 2 serving as a sensor such as the soft error detection circuit 3 and the soft error occurrence rate calculation unit 4, use electronic components with high neutron resistance so that soft errors due to neutrons do not occur. good. For example, an element that is not miniaturized is used. Alternatively, a shield against neutrons is added. Further, the semiconductor memory 2, the soft error detection circuit 3, and the soft error occurrence rate calculation unit 4 may be installed in a physically isolated place and connected via a network.
  • a nuclear spallation reaction is caused by the Si nuclei and neutrons of the semiconductor memory 2.
  • charged particles are generated, data stored in the semiconductor memory 2 is inverted, and a soft error may occur.
  • the soft error detection circuit 3 detects the soft error by performing read / write access to the semiconductor memory 2 and comparing the read data with the write data.
  • FIG. 2 is a flowchart showing an example of processing of the soft error detection circuit 3. The operation based on the flowchart of FIG. 2 is as follows.
  • Step 101 Write an initial value to the semiconductor memory 2. Since the initial value is arbitrary, for example, all “0” may be used. Since the data itself is not meaningful, it may be random, but since it is used later as a reference, it is necessary to understand the contents.
  • Step 102 Hold the write data as reference data.
  • the reference data is stored in a memory element that is strong against the soft error, for example, MRAM (magneto-resistive Random Access Memory).
  • MRAM magnetic-resistive Random Access Memory
  • the memory element or the soft error detection circuit 3 including the memory element is shielded with a neutron shielding material such as boron-containing polyethylene.
  • neutron shielding material such as boron-containing polyethylene.
  • memory type selection and shielding are used in combination.
  • Step 103 Read data from the same address of the semiconductor memory 2. That is, the data written in step 101 is read. Note that when data is read from the memory, error correction may be performed by ECC (Error Correction Code) or the like, but error correction is not performed in Step 103.
  • ECC Error Correction Code
  • Step 104 The reference data held in step 102 is compared with the read data acquired in step 103. If they do not match, it is determined that a soft error has occurred.
  • Step 105 When the data does not match, it is determined that a soft error is detected, and the number of detected soft errors is counted and added.
  • a soft error has a phenomenon called MBU (MultipleMultiBit Upset) in which a plurality of bits are inverted at a time. In this case, it is generally known that the memory cell holding the data to be inverted is in a physically close position. Therefore, in counting the number of detected soft errors, the number of groups of bits that are inconsistent (physically close) is counted.
  • the range of bits to be grouped can be arbitrarily set, for example, 10 memory cells squarely.
  • the counted number of detected soft errors is added to the result of the previous cycle.
  • Step 106 Update the reference data with the previous read data.
  • Step 107 It is determined whether a certain time has elapsed from the start of measurement or from the previous output of the software error detection result data 7. This fixed time represents the time granularity of the measurement result.
  • Processing 103 to processing 106 are performed in a predetermined cycle (cycle) until a predetermined time elapses.
  • the mismatch detected in the comparison process 104 is a difference from the read data of the previous cycle. Since the number of soft error detections based on this mismatch is added in processing 105, the number of soft error detections output as soft error detection data after a lapse of a certain time is an integrated value for the certain time.
  • Step 108 After a predetermined time has elapsed, the software error detection result data 7 is output to the software error occurrence rate calculation unit 4.
  • the soft error detection result data 7 includes the elapsed time from the start of measurement or the previous execution of step 108 and the number of soft error detections.
  • Step 109 The number of detected soft errors is initialized to zero.
  • Step 110 It is determined whether or not to end the measurement. For example, whether or not a measurement end signal has been received from an external terminal, whether or not a certain time has elapsed since the start of measurement, and the like can be considered.
  • the data is not compared immediately before and after the start of the fixed time (107), for example, when 10 bits of the memory are inverted, it is inverted bit by bit. This is because it is necessary to identify whether 10 bits have been inverted simultaneously (10 soft errors) or 10 bits have been inverted simultaneously (1 soft error). Even if two soft errors have occurred, if the locations where the first and second soft errors occur are physically close, this may be determined as one MBU. In addition, during the lapse of a certain time, there is a possibility that a soft error has occurred twice in the same bit and has returned to the original value. Therefore, as shown in the processes 103 to 106 in FIG. 2, the comparison is repeated at time intervals obtained by subdividing a certain time.
  • the fixed time of the process 107 that is the time granularity of the measurement time can be arbitrarily determined.
  • the period of the processes 103 to 106 can be arbitrarily determined as long as soft errors can be separated in time.
  • the time interval of neutron beam generation may be measured with another detector such as a counter tube, and the period may be determined with reference to this.
  • FIG. 3 shows an example of calculation by the soft error rate calculation unit 4.
  • Step 201 Calculate the soft error occurrence rate using the measurement time of the soft error detection result data 7 and the number of detected soft errors during measurement.
  • the unit of measurement time T is hour
  • the unit of soft error occurrence rate SER is FIT (Failures-In-Time).
  • Step 202 Referring to the environmental data 8 corresponding to the detection of the soft error, which is the basis of the soft error occurrence rate, the neutron increase / decrease rate in the current environment is calculated.
  • the neutron increase / decrease rate is set by standard environmental conditions (for example, temperature 20 degrees, humidity 50%, no rain, no clouds, 1 atm, 139 longitude east latitude 35 degrees north latitude, etc.) Expressed as a rate.
  • standard environmental conditions for example, temperature 20 degrees, humidity 50%, no rain, no clouds, 1 atm, 139 longitude east latitude 35 degrees north latitude, etc.
  • a method of preliminarily holding the increase / decrease rate of neutrons in a table format is conceivable.
  • FIG. 4 is an example of a correction table 400 in which the neutron increase / decrease rate with respect to the atmospheric pressure is stored in a table format.
  • atmospheric pressure not described in the table, for example, it can be obtained by interpolation using previous and subsequent data.
  • the atmospheric pressure is handled as the environmental data 8
  • the neutron increase / decrease rate under each environmental condition is acquired in the same manner as in the case of the above atmospheric pressure, and all the neutron increase / decrease rates are obtained.
  • the neutron increase / decrease rate in the current environment can be obtained. For example, if the neutron increase / decrease rate due to atmospheric pressure is ap, the neutron increase / decrease rate due to clouds is ac, and the neutron increase / decrease rate in the current environment is ae, the following equation is obtained.
  • LIDAR Light Detection and Ranging, Laser Imaging Detection and Ranging
  • LIDAR is a radar that uses laser light instead of radio waves. It emits pulsed laser light into the atmosphere and collects and detects reflected light from the atmosphere, clouds, and aerosols with a reflective telescope, and its strength and Doppler speed.
  • parameters such as atmospheric trace components such as ozone, methane, nitrogen oxides, water vapor, wind, temperature, aerosol, cloud distribution, rainfall, etc. can be measured.
  • the environmental data is not limited to weather information such as temperature, humidity, rainfall, cloud thickness, and atmospheric pressure, but may include position information such as latitude and longitude and astronomical information such as solar flare.
  • the time interval for acquiring environmental data can be acquired in real time for LIDAR data. Since the weather and the weather fluctuate in a cycle of several hours to several days, the time granularity of the data is about 1 week or less, preferably 1 day or less.
  • the solar flare may be an annual cycle, and the position information may be fixed according to the position. Needless to say, if the environmental neutrons are monitored for solar activity or geographical differences, they are not corrected. If the solar activity origin and geographical differences are removed as environmental factors, it is possible to monitor by focusing on neutrons originating from radioactive materials existing near the measurement site. In the present embodiment, an example in which weather and weather are corrected as environmental factors and neutrons originating from solar activity are mainly described.
  • the environmental sensor 6 is attached to the apparatus. However, the environmental sensor 6 or a server storing environmental data may be connected via a network to receive the environmental data. Since it is assumed that the environmental data 8 changes with time, the latest environmental data is basically used in subsequent processing. In the following embodiment, a configuration using LIDAR data (for example, updated every 3 minutes) for correction will be described as an example.
  • Step 203 The corrected soft error rate is calculated by multiplying the soft error rate calculated in step 201 by the neutron increase / decrease rate in the current environment. If the neutron intensity at the measurement location is the same and the environmental conditions are the same, the soft error occurrence rate of the semiconductor memory 2 depends only on the characteristics of the device itself and does not change with time. Since the corrected soft error rate excludes differences in environmental conditions, the corrected soft error rate can be read as relative neutron intensity. In order to relatively evaluate the intensity, it is only necessary to display the relative neutron intensity as compared with the soft error occurrence rate under a reference condition (for example, measured in a neutron shielding environment) measured in advance.
  • a reference condition for example, measured in a neutron shielding environment
  • the display device 9 displays the calculation result of the soft error rate calculation unit 4.
  • the display screen 10 displays a graph 11 representing the correspondence between the time and the corrected soft error rate, that is, the relative neutron intensity.
  • the connection between the soft error rate calculation unit 4 and the display device 9 may be wired or wireless. In FIG. 1, the two are arranged close to each other, but the display screen 10 may be in a remote place connected by a network.
  • SRAM Static Random Access Memory
  • FROM Flash Memory: Flash Memory
  • MLC Multi-Level-Cell
  • the neutron dose is measured using a small neutron detector based on the reaction between Si and neutron, which is the main cause of the soft error. Obtain and correct with time-varying environmental sensor information. Accordingly, it is possible to provide an environmental neutron intensity monitoring system that eliminates temporal measurement variations due to environmental factors such as atmospheric pressure and improves the measurement accuracy of environmental neutrons.
  • FIG. 5 shows a second embodiment.
  • the soft error detection result data 7 is corrected using the environment data 8 instead of the soft error occurrence rate calculation unit 4, and the corrected soft error occurrence rate is predicted from the transition of the corrected soft error occurrence rate.
  • the error occurrence rate prediction unit 12 is provided. Therefore, the soft error occurrence rate prediction unit 12 has a function of predicting the corrected soft error occurrence rate in addition to the function of the soft error occurrence rate calculation unit 4.
  • FIG. 6 is a flowchart showing an example of processing of the soft error occurrence rate prediction unit 12.
  • symbol is attached
  • the operation based on the flowchart of FIG. 6 is as follows.
  • Step 204 Acquire the correction software error occurrence rate acquired in the past, and acquire time series data of the correction software error occurrence rate.
  • the past corrected soft error occurrence rate may be held inside the soft error occurrence rate prediction unit 12, or a database may be created and held outside the soft error occurrence rate prediction unit 12. .
  • Step 205 Apply time series data of the correction software error occurrence rate to the model.
  • the fluctuation of the soft error occurrence rate is a combination of the environmental factor represented by atmospheric pressure and the global fluctuation caused by the solar activity factor. Since environmental factors depend on atmospheric pressure fluctuations and weather fluctuations, they are fluctuations with a short period of several hours to several days. Since the solar activity factor mainly depends on the sunspot cycle of the sun, it is a fluctuation of a long cycle of about 10 years.
  • the corrected soft error rate excluding environmental factors is considered to include only fluctuations in solar activity factors.
  • the sunspot period of the sun is modeled in advance using, for example, a trigonometric function, and applied to the model.
  • Step 206 Use the model created in step 205 to predict the trend of the correction software error rate.
  • the prediction is performed by, for example, an extrapolation method using a model.
  • the display device 9 displays the prediction result of the soft error occurrence rate prediction unit 12.
  • the display screen 13 displays a graph 14 indicating the correspondence between the time and the correction software error occurrence rate and the prediction result, and a status area 15 indicating a specific correction software error value.
  • the graph 14 shows the predicted value in addition to the actually measured value.
  • a specific value of a specific correction software error occurrence rate is displayed, for example, the current correction soft error occurrence rate is displayed.
  • the correction soft error occurrence rate corrected by the environmental data 8 is used to model the correction soft error occurrence rate by using the dependence on the solar activity. Becomes predictable.
  • FIG. 7 shows a third embodiment.
  • the same parts as those in FIG. 1 are denoted by the same reference numerals, and the configuration and operation are the same, and thus description thereof is omitted.
  • semiconductor memories 16, 17, and 18 having different susceptibility to soft errors due to neutrons (reaction cross section) are combined as the semiconductor memory 2, and the neutron energy dependence of the reaction cross section is used to obtain environmental neutrons.
  • the neutron energy distribution predicting unit 19 for predicting the energy distribution is mounted in place of the soft error occurrence rate calculating unit 4.
  • the reaction cross-sectional area of the semiconductor memory depends on the processing size (e.g., evaluated by the minimum line width and the minimum processing dimension of the device), the material constituting the semiconductor memory, and the data holding structure of the semiconductor memory. For example, as the processing size increases, the amount of charge stored in the semiconductor memory increases, and soft errors are less likely to occur with low energy neutrons. Further, in a FROM having a structure in which the retained charge is difficult to diffuse, a soft error does not occur unless the neutron has a high energy. Therefore, neutron energy distribution can be measured by using semiconductor memories having different reaction cross sections.
  • FIG. 8 is a flowchart showing an example of processing performed by the neutron energy distribution prediction unit 19. The operation based on the flowchart of FIG. 8 is as follows.
  • Step 207 SER 3 is derived from the corrected soft error rate SER 1 for each type of semiconductor memory, that is, for all three types of semiconductor memories 16, 17, and 18.
  • the derivation method is the same as in FIG.
  • Step 208 Obtain reaction cross sections ⁇ 1 , ⁇ 2 , and ⁇ 3 of the semiconductor memories 16, 17, and 18.
  • a method for obtaining the reaction cross section for example, a method of calculating in advance by simulation, holding it in a table format or a modeled mathematical formula, and reading it out can be considered.
  • Step 209 The neutron energy distribution is divided into a plurality of sections. This section function is the particle size in the neutron energy direction of the flux that is finally predicted. The predicted flux of each section and phi n from phi 1.
  • Step 210 For the three types of semiconductor memories 16, 17, and 18, simultaneous equations are constructed by creating equations representing the relationship between the soft error rate, the reaction cross section, and the neutron energy. If the reaction cross section of the neutron energy section i is ⁇ 1, i , ⁇ 2, i , ⁇ 3, i , the following simultaneous equations can be established.
  • Step 211 Solve the simultaneous equations established in Step 210 to derive the flux ⁇ i for each section. If the division function n of the neutron energy matches the number of types of semiconductor memory, it is solved analytically. If they do not match, an approximate solution is derived using, for example, a maximum likelihood estimation method.
  • the display device 9 displays the prediction result of the neutron energy distribution prediction unit 19.
  • the display screen 20 displays a graph 22 showing the relationship between the neutron energy and the neutron flux, in addition to the graph 21 showing the time change of the correction soft error occurrence rate of each semiconductor memory.
  • the prediction of the energy distribution of environmental neutrons using three types of semiconductor memories is shown, but two types or four or more types of semiconductor memories may be combined.
  • the number of types of semiconductor memory it is expected that the number of simultaneous equations will increase and the prediction accuracy of the environmental neutron energy distribution will be improved.
  • the present embodiment it is possible to predict the energy distribution of the environmental neutron flux by utilizing the fact that the dependence of the soft error rate on the neutron energy differs for each type of semiconductor memory.
  • FIG. 9 shows a fourth embodiment.
  • the semiconductor memory 2 is arranged in a plurality of directions.
  • the semiconductor memory 23 is arranged with the upper surface of the semiconductor memory facing in the vertical direction, the semiconductor memory 27 in the horizontal direction, and the semiconductor memory 25 in the depth direction.
  • the semiconductor memory is a device incorporating a semiconductor chip, it may be considered that the widest surface (main surface) of the semiconductor chip corresponds to the upper surface of the semiconductor memory.
  • the semiconductor memories 23, 25, and 27 are mounted on the memory mounting substrates 24, 26, and 28, respectively, and are connected to the soft error detection circuit 29 through the mother substrate 31.
  • the soft error detection circuit 29 outputs the soft error detection data from each board to the soft error occurrence rate calculation unit 30, and the soft error occurrence rate calculation unit 30 calculates the corrected soft error occurrence rate for each direction and displays it to the display unit. It is configured to output.
  • the soft error detection circuit 29 independently accesses the semiconductor memories 23, 25 and 27 arranged in a plurality of directions, and detects each soft error.
  • the soft error detection procedure is the same as that of the first embodiment, and is as shown in the flow of FIG. Therefore, the difference from the soft error detection circuit 3 shown in the first embodiment is that the soft error of the semiconductor memory arranged in a plurality of directions is detected independently.
  • the soft error occurrence rate calculation unit 30 independently processes the soft error detection data of a plurality of types of semiconductor memory detected by the soft error detection circuit 29, and derives the corrected soft error occurrence rate of the semiconductor memory for each direction.
  • the procedure for deriving the correction soft error occurrence rate is the same as that in the first embodiment, and is as shown in the flow of FIG. Therefore, the difference from the soft error occurrence rate calculation unit shown in the first embodiment is that the correction soft error occurrence rate of the semiconductor memory arranged in a plurality of directions is calculated independently.
  • the display device 9 displays the calculation result of the soft error occurrence rate calculation unit 30.
  • a graph 33 representing a change over time in the correction software error occurrence rate of the semiconductor memory for each direction is displayed. Since the semiconductor memories 23, 25, and 27 are of the same type, if the neutron intensity in each direction is the same, the solid line, broken line, and alternate long and short dash line in the graph 33 show the same value. Since the intensity is different, different values are shown. Therefore, the vertical axis correction soft error occurrence rate in the graph 33 can be read as the relative neutron intensity.
  • FIG. 9 shows an example in which semiconductor memories are arranged in three directions, they may be arranged in a spherical shape as long as they can be mounted. By increasing the arrangement direction, it is possible to obtain a neutron intensity distribution with better granularity.
  • FIG. 10 is a block diagram illustrating an example of a system configuration including a function of predicting a correction soft error occurrence rate, as in the second embodiment.
  • the system is housed in a single casing, and the calculation functions such as the soft error detection circuit 3 and the soft error occurrence rate prediction unit 12 are ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable). It was configured with hardware such as Gate Array, so that it could operate stand-alone.
  • ASIC Application Specific Integrated Circuit
  • FPGA Field Programmable
  • a soft error occurrence predicting unit 12 that performs calculation and control by arranging a semiconductor memory 2 and a soft error detection circuit 3 as a sensor at a neutron beam measurement site.
  • the server includes a normal processor, a memory, an input interface, and an output interface, and various functions and calculations are realized by the processor executing programs stored in the memory in cooperation with various hardware.
  • Various programs executed by the processor or functions to be executed may be referred to as “parts”, “units”, “means”, “functions”, “modules”, and the like.
  • the soft error occurrence rate prediction unit 12 has a function of predicting a corrected soft error occurrence rate in addition to the function of the soft error occurrence rate calculation unit 4.
  • the soft error detection circuit 3 uses the sensor memory 2 installed at the measurement site, the soft error detection circuit 3 performs the processing described with reference to FIG. 2 and generates soft error detection result data 7.
  • the soft error detection result data 7 is transmitted to the soft error occurrence rate prediction unit 12 via the network 1000.
  • the environmental sensor 6 also transmits the environmental data 8 to the soft error occurrence rate prediction unit 12 via the network 1000.
  • the network 1000 may be wired or wireless.
  • the soft error detection result data 7 is processed by the soft error occurrence rate calculation unit 4 as in the first or second embodiment, and the soft error occurrence rate 1001 is calculated.
  • the soft error occurrence rate 1001 is corrected by the soft error occurrence rate correction unit 1002 using the environment data 8 corresponding to the detection time of the soft error, and the corrected soft error occurrence rate 1003 is calculated. Is done.
  • a correction table 400 (FIG. 4) stored in a storage device (not shown) is used.
  • the soft error occurrence rate 1001 and the environmental data 8 that change with time are used for the calculation of the latest data at that time.
  • the latest data possessed by the soft error rate correction unit 1002 is used.
  • the corrected soft error occurrence rate 1003 is stored in the memory 1004 in time series.
  • the memory 1004 may be a large-capacity storage device such as a magnetic disk device.
  • the soft error rate variable analysis unit 1005 generates a model by applying the time series data of the corrected soft error rate 1003 to, for example, a trigonometric function that models the sunspot period of the sun. As a result, the soft error occurrence rate variable analysis unit 1005 outputs data 1006 that models global fluctuations in the soft error occurrence rate due to solar activity or the like.
  • the soft error occurrence rate prediction unit 1007 predicts the future soft error occurrence rate by an extrapolation method using the modeled data 1006 with respect to the current soft error occurrence rate, for example.
  • soft error occurrence rate transition data 1008 is output, and the soft error occurrence rate prediction unit 1007 outputs soft error occurrence prediction data 1009. These are displayed on the display device 1010.
  • the neutron soft error rate is sequentially corrected with the environmental information, so that local measurement variations in time can be removed and the measurement accuracy of relative neutron intensity can be improved.
  • the relative environmental neutron intensity change is a combination of global global fluctuations and local fluctuations due to temporal changes in installation environment conditions such as weather and atmospheric pressure. In calibration using a constant value that does not change with time, only the sum of global fluctuations and local fluctuations can be measured. On the other hand, in the present embodiment, both fluctuations can be separated from each other based on the environmental information, and more accurate calibration is possible by utilizing the fact that the frequency of global fluctuations is low.
  • the present embodiment since it is composed of a semiconductor memory, an access circuit, a monitor circuit, and an environmental sensor, a high voltage device and an analog circuit are unnecessary, and the size is small.
  • the main cause of soft errors in semiconductor devices is Si spallation reaction by neutrons.
  • the frequency of occurrence of this reaction can be directly observed in principle. Therefore, in principle, all neutrons with energy that can cause a spallation reaction with Si can be detected.
  • the present invention is not limited to the above-described embodiment, and includes various modifications.
  • a part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment.
  • the present invention can be used for monitoring environmental neutrons.

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  • Measurement Of Radiation (AREA)

Abstract

La présente invention concerne un système de surveillance de l'intensité des neutrons dans l'environnement, qui est capable d'améliorer la précision de mesure des neutrons en éliminant la variabilité de mesure localisée résultant de facteurs météorologiques et analogue. Ce système de surveillance de l'intensité des neutrons est pourvu : d'une mémoire à semi-conducteurs ; d'une unité d'entrée pour introduire des données de capteur d'environnement provenant d'un capteur d'environnement ; d'une unité de détection d'erreurs logicielles qui accède en lecture à la mémoire à semi-conducteurs pour détecter des erreurs logicielles, et produit des données de résultat de détection d'erreurs logicielles comprenant des informations temporelles relatives au temps de détection et le nombre détecté d'erreurs logicielles ; et d'une unité de calcul, qui calcule un taux d'occurrence d'erreurs logicielles à partir des données de résultat de détection d'erreurs logicielles et corrige les données de capteur d'environnement afin d'obtenir un taux d'occurrence d'erreurs logicielles corrigé.
PCT/JP2018/010675 2017-04-05 2018-03-19 Système et procédé de surveillance de l'intensité des neutrons WO2018186155A1 (fr)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116046809A (zh) * 2023-02-07 2023-05-02 中国科学院西北生态环境资源研究院 山地土壤水信息获取方法、装置、存储介质及电子设备

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP7306945B2 (ja) * 2019-10-03 2023-07-11 ファナック株式会社 メモリエラー判別装置及びメモリエラー判別用コンピュータプログラム
JP7339587B2 (ja) * 2020-05-12 2023-09-06 日本電信電話株式会社 核反応検出装置及び核反応検出方法並びに核反応検出プログラム
CN114859397B (zh) * 2022-03-17 2022-11-22 合肥金星智控科技股份有限公司 一种中子活化的能谱的处理方法、装置、设备及介质

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH04158209A (ja) * 1990-10-22 1992-06-01 Fuji Electric Co Ltd 放射線厚さ計及び放射線を用いた厚さ測定方法
JP2002040147A (ja) * 2000-07-28 2002-02-06 Matsushita Electric Ind Co Ltd 中性子検出装置
JP2013120426A (ja) * 2011-12-06 2013-06-17 Hitachi Ltd ソフトエラー耐性調整機能を備えた電子システム装置及びソフトエラー耐性調整方法
JP2014126555A (ja) * 2012-12-27 2014-07-07 Canon Electronics Inc 放射線量測定装置

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2013210276A (ja) * 2012-03-30 2013-10-10 Toshiba Corp 放射線検出器校正システムおよび放射線検出器校正方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH04158209A (ja) * 1990-10-22 1992-06-01 Fuji Electric Co Ltd 放射線厚さ計及び放射線を用いた厚さ測定方法
JP2002040147A (ja) * 2000-07-28 2002-02-06 Matsushita Electric Ind Co Ltd 中性子検出装置
JP2013120426A (ja) * 2011-12-06 2013-06-17 Hitachi Ltd ソフトエラー耐性調整機能を備えた電子システム装置及びソフトエラー耐性調整方法
JP2014126555A (ja) * 2012-12-27 2014-07-07 Canon Electronics Inc 放射線量測定装置

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116046809A (zh) * 2023-02-07 2023-05-02 中国科学院西北生态环境资源研究院 山地土壤水信息获取方法、装置、存储介质及电子设备
CN116046809B (zh) * 2023-02-07 2023-09-05 中国科学院西北生态环境资源研究院 山地土壤水信息获取方法、装置、存储介质及电子设备

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