TY  - JOUR
A1  - Chen, Junchao
A1  - Lange, Thomas
A1  - Andjelkovic, Milos
A1  - Simevski, Aleksandar
A1  - Krstić, Miloš
T1  - Prediction of solar particle events with SRAM-based soft error rate monitor and supervised machine learning
T2  - Microelectronics reliability
N2  - 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).
Y1  - 2020
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/57623
SN  - 0026-2714
VL  - 114
PB  - Elsevier
CY  - Oxford
ER  -