Observation and analysis of Single Event Effects on-board the SOHO satellite

Harboe-Sørensen, R.; Daly, E.; Teston, F.; Schweitzer, H.; Nartallo, R.; Perol, P.; Vandenbussche, F. C.; Dzitko, H.; Cretolle, J.

doi:10.1109/radecs.2001.1159256

Cited by 6 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These energetic particles can induce SEUs either by direct ionization or by indirect ionization [2]. Since the particle flux directly determines the SEU rate of an electronic system, based on the data obtained from past space missions [3] [4], the SEU rate may increase hundreds to thousands of times during the SPE peak periods. Thus, it is important to track the variation in particle flux in real-time, and consequently activate the suitable mitigation techniques to protect the sensitive elements in the on-board electronic systems.…”

Section: Introductionmentioning

confidence: 99%

Solar Particle Event and Single Event Upset Prediction from SRAM-based Monitor and Supervised Machine Learning

Chen

Lange

Andjelković

et al. 2022

IEEE Trans. Emerg. Topics Comput.

View full text Add to dashboard Cite

The intensity of cosmic radiation may differ over five orders of magnitude within a few hours or days during the Solar Particle Events (SPEs), thus increasing for several orders of magnitude the probability of Single Event Upsets (SEUs) in spaceborne electronic systems. Therefore, it is vital to enable the early detection of the SEU rate changes in order to ensure timely activation of dynamic radiation hardening measures. In this paper, an embedded approach for the prediction of SPEs and SRAM SEU rate is presented. The proposed solution combines the real-time SRAM-based SEU monitor, the offline-trained machine learning model and online learning algorithm for the prediction. With respect to the state-of-the-art, our solution brings the following benefits: (1) Use of existing on-chip data storage SRAM as a particle detector, thus minimizing the hardware and power overhead, (2) Prediction of SRAM SEU rate one hour in advance, with the fine-grained hourly tracking of SEU variations during SPEs as well as under normal conditions, (3) Online optimization of the prediction model for enhancing the prediction accuracy during runtime, (4) Negligible cost of hardware accelerator design for the implementation of selected machine learning model and online learning algorithm. The proposed design is intended for a highly dependable and self-adaptive multiprocessing system employed in space applications, allowing to trigger the radiation mitigation mechanisms before the onset of high radiation levels.

show abstract

Section: Introductionmentioning

confidence: 99%