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Journal Articles

Measurement of radionuclide production probabilities in negative muon nuclear capture and validation of Monte Carlo simulation model

Yamaguchi, Yuji; Niikura, Megumi*; Mizuno, Rurie*; Tampo, Motonobu*; Harada, Masahide; Kawamura, Naritoshi*; Umegaki, Izumi*; Takeshita, Soshi*; Haga, Katsuhiro

Nuclear Instruments and Methods in Physics Research B, 567, p.165801_1 - 165801_11, 2025/10

 Times Cited Count:2 Percentile:66.26(Instruments & Instrumentation)

As part of the development of a sample radioactivity calculation program, we have measured radionuclide production probabilities in negative muon nuclear capture to update experimental data and to validate a calculation dataset obtained by a Monte Carlo simulation code. The probabilities have been obtained by an activation experiment on $$^{27}$$Al, $$^mathrm{nat}$$Si, $$^{59}$$Co, and $$^mathrm{nat}$$Ta targets. The obtained probabilities expand the validation scope to the radionuclide production processes outside of the existing data coverage. By comparing the resultant probabilities with the calculated dataset, it has been revealed that the dataset is generally on the safe side in radioactivity estimation and needs to be corrected in the following three cases: (i) isomer production; (ii) radionuclide production by the multiple neutron emission; (iii) radionuclide production by particle emissions involving a proton. The present probabilities and the new findings on the correction provide valuable clues to improvements of the simulation models.

Journal Articles

Muon-induced SEU analysis and simulation for different cell types in 12-nm FinFET SRAMs, and 28-nm planar SRAMs and register files

Gomi, Yuibi*; Takami, Kazusa*; Mizuno, Rurie*; Niikura, Megumi*; Yasuda, Ryuichi*; Deng, Y.*; Kawase, Shoichiro*; Watanabe, Yukinobu*; Abe, Shinichiro; Liao, W.*; et al.

IEEE Transactions on Nuclear Science, 72(8), p.2751 - 2762, 2025/08

 Times Cited Count:4 Percentile:65.74(Engineering, Electrical & Electronic)

The study of muon-induced soft errors in terrestrial settings remains underexplored even though the incidence of muon-induced soft errors tends to increas. In this study, we conducted positive and negative muon irradiation experiments on four types of 12-nm FinFET SRAMs. At the momentum where the SEU cross section peaks, 2-fin cells exhibit larger SEU cross sections than 1-fin cells for negative muons. Conversely, for positive muons, the SEU cross section is larger in 1-fin cells than in 2-fin cells due to the increased diffusion capacitance in 2-fin cells. We also performed the Monte Carlo simulations. The results suggest that a single sensitive volume with the same critical charge value cannot consistently reproduce the SEU cross sections for both types of muons.

Journal Articles

Comprehensive Bayesian machine learning approach to estimating the total nuclear capture rate of a negative muon

Iwamoto, Hiroki; Niikura, Megumi*; Mizuno, Rurie*

Physical Review C, 111(3), p.034614_1 - 034614_13, 2025/03

 Times Cited Count:2 Percentile:66.26(Physics, Nuclear)

A negative muon in the 1$$s$$ orbital can be captured by a nucleus, leading to subsequent nuclear dacay processes. The accurate prediction of total nuclear capture rates, which could be crucial in fields such as geochemistry, nuclear astrophysics, and semiconductor device developement, remains challenging with curren physics models. This study aims to develop a comprehensive machine learning (ML) model to estimate the total nuclear capture rate of a negative muon, integrating physical information and experimental data within a Bayesian framework. The study employs an ML model based on Gaussian process regression, using experimental data with evaluated uncertainties as training data. The model incoporates the Goulard-Primakoff formula as prior information and applies a transfer learning approach to improve estimations, particularly in data-sparse regions. The developed ML model is shown to outperform theoretical physics models in both accuracy and comprehensiveness, with key experiments identified to further refine the model performance. The estimates generated in this study will be incorporated into muon nuclear data applied accross a variety of research fields.

Journal Articles

Development of wide range photon detection system for muonic X-ray spectroscopy

Mizuno, Rurie*; Niikura, Megumi*; Saito, Takeshi*; Matsuzaki, Teiichiro*; Sakurai, Hiroyoshi*; Amato, A.*; Asari, Shunsuke*; Biswas, S.*; Chiu, I.-H.; Gianluca, J.*; et al.

Nuclear Instruments and Methods in Physics Research A, 1060, p.169029_1 - 169029_14, 2024/03

 Times Cited Count:4 Percentile:40.05(Instruments & Instrumentation)

Journal Articles

Muon-induced SEU cross sections of 12-nm FinFET and 28-nm planar SRAMs

Gomi, Yuibi*; Takami, Kazusa*; Mizuno, Rurie*; Niikura, Megumi*; Deng, Y.*; Kawase, Shoichiro*; Watanabe, Yukinobu*; Abe, Shinichiro; Liao, W.*; Tampo, Motonobu*; et al.

Proceedings of 23rd European Conference on Radiation and its Effects on Components and Systems (RADECS 2023)(Internet) , 4 Pages, 2023/09

 Times Cited Count:2 Percentile:56.64(Engineering, Electrical & Electronic)

It has been suggested that the effect of muon-induced soft errors will increase by shrinking of the semiconductor process. However, muon irradiation experiments for FinFETs has not been reported. We performed positive and negative muon irradiation experiments on 12-nm FinFET and 28-nm planar SRAMs at MUSE in J-PARC. The negative muon-induced single event upset (SEU) cross section is more than ten times larger than that of the positive muon. The negative muon-induced SEU cross section of FinFETs decreases by the same ratio as neutron-induced one compared to planar SRAMs.

Oral presentation

Comprehensive estimation of muon nuclear capture rates using machine learning

Iwamoto, Hiroki; Niikura, Megumi*; Mizuno, Rurie*

no journal, , 

Negative muons with a mass of 105.6 MeV, which have the same charge as an electron, are trapped in the Coulomb field of nuclei in matter to form muon atoms. Negative muons in the 1s orbital of the lowest level either decay spontaneously with a half-life of 2.2 $$mu$$s or are trapped in the nucleus to form an excited compound nucleus with one lower atomic number. This compound nucleus undergoes a de-excitation process and is transmuted into other nuclei. In recent years, nuclear data on negative muon-induced nuclear reactions (hereinafter referred to as "muon nuclear data") have been developed in anticipation of applications to transmutation technology using this reaction. Among the muon nuclear data, many experimental data have been accumulated on the nuclear capture rate of negative muons (hereinafter referred to as "capture rate"). However, with a few exceptions, experimental data on capture rates are limited to elements of natural composition. In this study, we developed and validated a machine learning model to evaluate the capture rate comprehensively and reliably for each nuclide. The results of the validation showed that the developed model can comprehensively evaluate the capture rate including uncertainties, although there are some issues that need to be improved in some cases such as light nuclei.

Oral presentation

Measurement of yield of radionuclides produced by nuclear capture of negative muons

Yamaguchi, Yuji; Niikura, Megumi*; Mizuno, Rurie*; Tampo, Motonobu*; Harada, Masahide; Kawamura, Naritoshi*; Umegaki, Izumi*; Takeshita, Soshi*; Haga, Katsuhiro

no journal, , 

The yield of radionuclides produced by nuclear capture of negative muons has been measured at Muon Science Establishment of Materials and Life Science Experimental Facility (MLF) to accurately estimate radioactivity of muon-irradiated samples at MLF, J-PARC. In this measurement, new data which were difficult to measure at preliminary experiment last year have been obtained by improving the negative-muon counting method.

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