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).
Author details: | Junchao ChenORCiDGND, Thomas Lange, Milos Andjelkovic, Aleksandar SimevskiORCiD, Miloš KrstićORCiDGND |
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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): | CC-BY-NC-ND - Namensnennung, nicht kommerziell, keine Bearbeitungen 4.0 International |