The ESD effects on the E-mode AlGaN/GaN high-electron mobility transistors (HEMTs) with p-GaN gate are investigated under repetitive TLP pulses. Firstly, the degradation and recovery of output, transfer characteristics, gate-leakage characteristics and low-frequency noises (LFN) are analyzed in detail before and after reverse electrostatic discharge (ESD) stress. The experimental results show that the electrical characteristics of the devices gradually degraded as the transmission line pulse (TLP) pules increased. Subsequently, the LFN measurements are performed over the frequency range of 1 Hz-10 KHz by increasing TLP pulses. Finally, the recovery tendency of DC (direct current) characteristics and trap density are studied and discussed after resting the device at room temperature for 1 to 3 months. These results physically confirm that the mechanism of the performance degradation and recovery of the devices could be attributed to the trapping and releasing processes of electrons in the p-GaN layer and AlGaN barrier layer of AlGaN/GaN HEMTs, which change the electric field distribution under the gate.
The problem of health status prediction of insulated-gate bipolar transistors (IGBTs) gains a lot of attention in the field of health management of power electronic equipment. The performance degradation of IGBT gate oxide layer is one of the important failure modes. In view of failure mechanism analysis and easy implementation of monitoring circuit, this paper selects the gate leakage current of IGBT as the precursor parameter of gate oxide degradation, and uses time domain characteristic analysis, gray correlation degree, Mahalanobis distance, Kalman filter and other methods to carry out feature selection and fusion, and finally obtains a health indicator characterizing the degradation of IGBT gate oxide. Based on the Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) network, this paper constructs an IGBT gate oxide degradation prediction model, and performs experimental analysis on the dataset released by NASA-Ames Laboratory, and the average absolute error of performance degradation prediction is as low as 0.0216. Compared with Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Support Vector Regression (SVR) and CNN-LSTM models, CNN-LSTM network has the highest prediction accuracy. These results show the feasibility of gate leakage current as a precursor parameter of IGBT gate oxide layer failure, as well as the accuracy and reliability of the CNN-LSTM prediction model.
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