Best-Fit Techniques to Estimate SBU/MCU Cross Sections From Radiation-Ground Tests in Memories

This article studies the probability distribution for the expected number of bitflips per round of reading in radiation-ground tests on a memory device where only single-bit upsets (SBUs) and multiple-cell upsets (MCUs) occur. This distribution is used to estimate the soft error cross sections in actual experiments by means of two best-fit approaches: one based on the gradient descent (GD) algorithm and the other on genetic algorithms (GAs). Besides, it is investigated how this mathematical study is suitable to detect possible variations in the soft error rate (SER) due to different reasons, such as variations in the radiation flux. Finally, the inherent stochastic characteristics of the experiments are used to provide tools to detect forgery in experiment data.

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Publication Details

Journal
IEEE Transactions on Nuclear Science
Published
2025-02-07
DOI
https://doi.org/10.1109/tns.2025.3539956
Citations
1
Primary Topic
Radiation Effects in Electronics
Type
article
Field-Weighted Citation Impact
0.73
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article

Best-Fit Techniques to Estimate SBU/MCU Cross Sections From Radiation-Ground Tests in Memories

G. Hubert, Hortensia Mecha, Mohammadreza Rezaei, Juan Carlos Fabero et al.
1 citations
IEEE Transactions on Nuclear Science
Radiation Effects in Electronics
0.73
article

Best-Fit Techniques to Estimate SBU/MCU Cross Sections From Radiation-Ground Tests in Memories

G. Hubert, Hortensia Mecha, Mohammadreza Rezaei, Juan Carlos Fabero, Francisco J. Franco, Juan Antonio Clemente
article en
1 citations

Abstract

This article studies the probability distribution for the expected number of bitflips per round of reading in radiation-ground tests on a memory device where only single-bit upsets (SBUs) and multiple-cell upsets (MCUs) occur. This distribution is used to estimate the soft error cross sections in actual experiments by means of two best-fit approaches: one based on the gradient descent (GD) algorithm and the other on genetic algorithms (GAs). Besides, it is investigated how this mathematical study is suitable to detect possible variations in the soft error rate (SER) due to different reasons, such as variations in the radiation flux. Finally, the inherent stochastic characteristics of the experiments are used to provide tools to detect forgery in experiment data.

IEEE Transactions on Nuclear ScienceVol. 72(4)
Universidad Complutense de Madrid (ES), Institut Superieur de l'Aeronautique et de l'Espace (ISAE-SUPAERO) (FR), Office National d'Études et de Recherches Aérospatiales (FR)
Climate action
Openalex Percentile: Top 32%
Radiation Effects in Electronics
0.73
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Best-Fit Techniques to Estimate SBU/MCU Cross Sections From Radiation-Ground Tests in Memories — G. Hubert, Hortensia Mecha, et al. · IEEE Transactions on Nuclear Science (2025) | TGRS Research Map | TGRS