高耐圧パワー半導体素子の宇宙放射線環境下故障率の計算手法に関する研究

九州工業大学博士学位論文（要旨）学位記番号：工博甲第445号 学位授与年月日：平成29年9月22

Formulation of Single Event Burnout Failure Rate for High Voltage Devices in Satellite Electrical Power System

Single-Event Burnout (SEB) is a catastrophic failure in the high voltage devices that is initiated by the passage of particles during turn-off state. Previous papers reported that SEB failure rate increases sharply when applied voltage exceeds a certain threshold voltage. On the other hand, the high voltage devices for the artificial satellite have been increasing. In space, due to increase flux of particle, it is predicted that SEB failure rate will be higher. In this paper, we proposed the failure rate calculation method for high voltage devices based on SEB cross section and flux of particles. This formula can calculate the failure rate at space level and terrestrial level depending on the applied voltage of the high voltage devices.2017 29th International Symposium on Power Semiconductor Devices and IC\u27s (ISPSD), May 28 2017-June 1 2017, Sapporo, Japa

Failure rate calculation method for high power devices in space applications at low earth orbit

This paper discusses the universal calculation method for space proton induced failure rate on high power device. High energetic particles can be the reason of power device failure in both terrestrial and space. T-CAD simulation result gives a threshold charge value for the device destruction which is triggered by energetic proton from space. The amount of threshold charge depends on applied voltage for high power device. The probability of charge generation in silicon due to proton penetration is considered as well. This probability function variation depends on the thickness of device and incident energy of proton which studied before at there. Last consideration on this paper is 3.3 kV PiN diode\u27s Single Event Upset Cross section and failure rate which was calculated by proposed method in Low earth orbit environment condition

Aboveground Biomass Estimation and Time Series Analyses in Mongolian Grasslands Utilizing PlanetScope Imagery

Mongolia, situated in central Asia and bordered by Russia to the north and China to the south, experiences a semi-arid climate across most of its territory. Grasslands are pivotal in Mongolia’s agricultural sustainability and food security, facing rapid changes in the last two decades that underscore the ongoing need for innovative approaches to assess vegetation conditions. This study aims to evaluate grassland biomass measurement and prediction through the analysis of high-resolution satellite data. By conducting a time series assessment of grazing-induced changes in vegetation dynamics at the long-term monitoring sites of the Botanic Garden and Research Institute, Mongolian Academy of Sciences, we seek to refine our understanding. The investigation covers biomass estimation across various Mongolian grassland landscapes, encompassing desert, steppe, and mountain regions. Spanning the grassland growing season from May 2020 to October 2023, the research leveraged diverse ground data types, including surface reflectance measurements, geographic coordinates for satellite data correction, and aboveground dry biomass. These components were instrumental in developing a biomass estimation model reliant on establishing correlations between the satellite-derived Normalized Difference Vegetation Index and biomass. The predicted biomass facilitated the time series map analysis and dynamic analysis. The PlanetScope surface reflectance correlates strongly at 0.97 with field measurements, indicating robust relations. Biomass and the Normalized Difference Vegetation Index show correlations of 0.82 for dry grassland, 0.80 for mountain grassland, and 0.65 for desert grassland, with a combined correlation coefficient of 0.62, revealing distinct characteristics across these grasslands. Time series dynamic analysis reveals rising biomass differences between grazed and ungrazed areas, suggesting potential grassland degradation. Variations in the slope coefficient of biomass differences among grassland types indicate differing degradation patterns, emphasizing the need for effective grazing management practices to sustain and conserve Mongolian grasslands. This highlights the potential of remote sensing in monitoring and managing grassland ecosystems