- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE 2018 IEEE 6th Workshop on Wide Bandgap Power Devices and Applications (WiPDA) - Atlanta, GA, USA (2018.10.31-2018.11.2)] 2018 IEEE 6th Workshop on Wide Bandgap Power Devices and Applications (WiPDA) - C-V Measurement under Different Frequencies and Pulse-mode Voltage Stress to Reveal Shallow and Deep Trap Effects of GaN HEMTs
摘要： In this work, the influence of interface traps at the Si3N4/ (GaN) /AlGaN interface and carbon-related buffer traps on AlGaN/GaN-on-silicon high electron mobility transistors (HEMTs) has been studied using high-frequency capacitance-voltage (HFCV) and quasi-static C-V (QSCV) measurement. The correlation between Ron degradation and two different traps distributions subjected to different operation conditions have been investigated. Deep-level traps from the hole-emission process of carbon-related buffer have been activated by high drain voltage under off-state in pulse-mode condition and shallow-level traps from interface states are observed with an increase in gate voltage under on-state.
关键词： Traps,GaN HEMTs,Pulse-mode Stress,C-V measurement
更新于2025-09-04 15:30:14
-
Classification of Rice Heavy Metal Stress Levels Based on Phenological Characteristics Using Remote Sensing Time-Series Images and Data Mining Algorithms
摘要： Heavy metal pollution in crops leads to phenological changes, which can be monitored by remote sensing technology. The present study aims to develop a method for accurately evaluating heavy metal stress in rice based on remote sensing phenology. First, the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to blend Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat to generate a time series of fusion images at 30 m resolution, and then the vegetation indices (VIs) related to greenness and moisture content of the rice canopy were calculated to create the time-series of VIs. Second, phenological metrics were extracted from the time-series data of VIs, and a feature selection scheme was designed to acquire an optimal phenological metric subset. Finally, an ensemble model with optimal phenological metrics as classification features was built using random forest (RF) and gradient boosting (GB) classifiers, and the classification of stress levels was implemented. The results demonstrated that the overall accuracy of discrimination for different stress levels is greater than 98%. This study suggests that fusion images can be utilized to detect heavy metal stress in rice, and the proposed method may be applicable to classify stress levels.
关键词： ensemble model,feature selection,time-series,MODIS and Landsat,remote sensing phenology,heavy metal stress
更新于2025-09-04 15:30:14