Zhang Zhan’gang, Lei Zhifeng, En Yunfei. Radiation environment of typical satellite orbits and on-orbit soft error rate prediction model analysis[J]. High Power Laser and Particle Beams, 2015, 27: 094002. doi: 10.11884/HPLPB201527.094002
Citation:
Zhang Zhan’gang, Lei Zhifeng, En Yunfei. Radiation environment of typical satellite orbits and on-orbit soft error rate prediction model analysis[J]. High Power Laser and Particle Beams, 2015, 27: 094002. doi: 10.11884/HPLPB201527.094002
Zhang Zhan’gang, Lei Zhifeng, En Yunfei. Radiation environment of typical satellite orbits and on-orbit soft error rate prediction model analysis[J]. High Power Laser and Particle Beams, 2015, 27: 094002. doi: 10.11884/HPLPB201527.094002
Citation:
Zhang Zhan’gang, Lei Zhifeng, En Yunfei. Radiation environment of typical satellite orbits and on-orbit soft error rate prediction model analysis[J]. High Power Laser and Particle Beams, 2015, 27: 094002. doi: 10.11884/HPLPB201527.094002
Science and Technology on Reliability Physics and Application of Electronic Component Laboratory,the Fifth Electronics Research Institute of Ministry of Industry and Information Technology,Guangzhou 510610,China;
2.
School of Electronic and Information Engineering,South China University of Technology,Guangzhou 510641,China
The space radiation environments of typical satellite orbits including geostationary orbit, medium earth orbit, and low earth orbit are extracted and calculated using the latest version of Space Radiation 7.0 toolkit. The ion flux-energy spectrums and flux-LET spectrums under different space weather and shielding conditions are analyzed to reveal the characteristics. Taking an SOI SRAM as an example, combining the SEU cross section versus LET relationship obtained by accelerator-based heavy ions testing, the on-orbit soft error rate is predicted. The influence trend and inner mechanism of key parameters on the prediction results are analyzed. Following results are concluded. Four input modes of Space Radiation software result into soft error rates with difference up to about five orders. The on-orbit soft error rate decreases by several orders of magnitude as the thickness of the sensitive volume increases, which is attributed to the fact that the thickness of the sensitive volume is directly related to the average projected area of the sensitive volume and the qualified space ion flux. The prediction result also depends on the funnel length. Finally, the applicability and development of soft error rate prediction model are discussed.