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Prediction of solar particle events with SRAM-based soft error rate monitor and supervised machine learning

  • This work introduces an embedded approach for the prediction of Solar Particle Events (SPEs) in space applications by combining the real-time Soft Error Rate (SER) measurement with SRAM-based detector and the offline trained machine learning model. The proposed approach is intended for the self-adaptive fault-tolerant multiprocessing systems employed in space applications. With respect to the state-of-the-art, our solution allows for predicting the SER 1 h in advance and fine-grained hourly tracking of SER variations during SPEs as well as under normal conditions. Therefore, the target system can activate the appropriate mechanisms for radiation hardening before the onset of high radiation levels. Based on the comparison of five different machine learning algorithms trained with the public space flux database, the preliminary results indicate that the best prediction accuracy is achieved with the recurrent neural network (RNN) with long short-term memory (LSTM).

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Author details:Junchao ChenORCiDGND, Thomas Lange, Milos Andjelkovic, Aleksandar Simevski, Miloš KrstićORCiDGND
DOI:https://doi.org/10.1016/j.microrel.2020.113799
ISSN:0026-2714
Title of parent work (English):Microelectronics reliability
Publisher:Elsevier
Place of publishing:Oxford
Publication type:Article
Language:English
Date of first publication:2020/10/31
Publication year:2020
Release date:2023/01/25
Volume:114
Article number:113799
Number of pages:6
Funding institution:European UnionEuropean Commission [722325]
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik und Astronomie
DDC classification:5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik
Peer review:Referiert
Publishing method:Open Access / Hybrid Open-Access
License (German):License LogoCC-BY-NC-ND - Namensnennung, nicht kommerziell, keine Bearbeitungen 4.0 International
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