Breakdown voltage analysis for the new RESURF AlGaN/GaN HEMTs

This paper demonstrates that the depletion process for AlGaN/GaN high electron mobility tran-sistors(HEMTs)is different than that for silicon power devices by analyzing active region depletion.Based on the special breakdown principle that occurs in AlGaN/GaN HEMTs,we propose a new reduced surface field AlGaN/GaN HEMT with a double low-density drain(LDD)and a positively charged region near the drain to optimize the surface electric field and increase the breakdown voltage.In this structure,two negative charge regions with different doses are introduced into the polarization AlGaN layer to form a double LDD and decrease the high electric field near the gate by depleting two-dimensional electron gas.A positively charged region is added to the electrode near the drain to decrease the high electric field peak at the drain edge.By applying ISE(integrated systems engineering)simulation software,we verify that the virtual gate effect occurs in the AlGaN/GaN HEMTs.The breakdown voltage is improved from 257 V in the conventional structure to 550 V in the proposed structure.     

HEMT器件力电耦合传感特性测试

设计了在Si微结构与AlGaN/GaN高电子迁移率晶体管(HEMT)集成的微机电系统(MEMS)。通过微机械加工工艺,完成了力电耦合传感结构的加工。通过实验测试发现HEMT器件具备很强的力电耦合特性,测试结果在-1.5 V栅压下,HEMT器件对应力的灵敏度为123.96 MPa/mA,线性度为6.95%。     

双轴应变下GaN有效质量的计算及其对迁移率的影响

通过计算双轴应变下氮化镓的电子能带结构,给出了GaN有效质量与应变的变化关系。在弛豫时间近似的条件下,这种关系决定了双轴应变AlGaN/GaN中二维电子气(2DEG)的迁移率的改变。在其他物理参量不变的情况下,这种二维电子气迁移率将随着张应变的增加而增加,并随着压应变的增加而减小。计算结果表明,张应变对2DEG迁移率的影响要比压应变大。此外,GaN有效质量的变化在低温时对迁移率的作用更明显。而在低温低浓度的条件下,迁移率却对有效质量的依赖很小。     

A simplification method for capacitance models in AlGaN/GaN high electron mobility transistors under large drain voltage using channel analysis

This article analyzes the bias dependence of gate‐drain capacitance (C_gd) and gate‐source capacitance (C_gs) in the AlGaN/GaN high electron mobility transistors under a high drain‐to‐source voltage (V_ds) from the perspective of channel shape variation, and further simplifies C_gd and C_gs to be gate‐to‐source voltage (V_gs) dependent only at high V_ds. This method can significantly reduce the number of parameters to be fitted in C_gd and C_gs and therefore lower the difficulty of model development. The Angelov capacitance models are chosen for verifying the effectiveness of simplification. Good agreement between simulated and measured small‐signal S‐parameters, large‐signal power sweep, and power contours comprehensively proves the accuracy of this simplification method.     

AlGaN/GaN C-HEMT structures for dynamic stress detection

In our work, we investigated the possibility of dynamic stress detection based on the piezoelectric polarization using AlGaN/GaN circular high electron mobility transistors (C-HEMTs). In our knowledge, stress sensors in that account are introduced for the first time. The sensor structures exhibit good linearity in the piezoelectric response under dynamic stress conditions. The measurements reveal excellent stress detection sensitivity that is independent on the measured frequency range. The sensitivity of the devices can be easily increased by increase of the area of the Schottky gate ring electrode. The further increase of the sensitivity can be tuned by an optimal selection of the DC drain and gate bias.     

Charge density and bare surface barrier height in GaN/AlGaN/GaN heterostructures: A modeling and simulation study

In this article, we present a physics‐based model to explain the effect of the GaN cap layers on the 2D electron gas density and the bare surface barrier height in AlGaN/GaN heterostructures. We consider that the 2DEG originates from the surface donor states present on the GaN cap top surface. The influence of a 2D hole gas, formed when the valence band crosses the Fermi energy level, has also been considered. This model agrees well with the published experimental results and TCAD simulations, and can easily be incorporated into the modeling of GaN/AlGaN/GaN‐based HEMT devices.     

Ultrathin GaN/AlN/GaN solution-gate field effect transistor with enhanced resolution at low source-gate voltage

The pH response of a GaN/AlN/GaN solution-gate field effect transistor (SGFET), with a GaN/AlN barrier of 7.5 nm thick, is analyzed and compared with standard GaN/AlGaN/GaN SGFETs with total barrier thicknesses of 19 and 23 nm. While all types of SGFETs show a similar surface sensitivity to H⁺ ions, a significant improvement in the transducive sensitivity of the SGFET source-drain current under pH changes is found when decreasing the barrier thickness, due to the increased transconductance of the FET structure. Resolution better than 0.005 pH can be estimated in the case of the ultrathin SGFET. Moreover, the maximum transconductance value shifts to gate-drain voltage close to 0 V, which eventually involves no need of reference electrode in less demanding applications, simplifying the final design of the device and making AlN barrier-based SGFETs highly recommended in the broad field of chemical sensors.     

Impact of ZnO gate interfacial layer on piezoelectric response of AlGaN/GaN C-HEMT based ring gate capacitor

We report on a piezoelectric response investigation of AlGaN/GaN circular high electron mobility transistor (C-HEMT) based ring gate capacitor as a new stress sensor device to be potentially applied for dynamic high-pressure sensing. A ring gate capacitor of C-HEMT with an additional ZnO gate interfacial layer was used to measure the changes in the piezoelectric charge induced directly by the variation of piezoelectric polarization of both gate piezoelectric layers (AlGaN, ZnO) for harmonic loading at different excitation frequences. Our experimental results show that about 10 nm thick piezoelectric ZnO layer grown on ring gate/AlGaN interface of C-HEMT can yield almost a 60% increase in the piezoelectric detection sensitivity of the device due to its higher piezoelectric coefficient. A three-dimensional CoventorWare simulation is carried out to confirm the increase in the measured piezoelectric response of ZnO based ring gate capacitor of C-HEMT.     

AlGaN/GaN HEMTs on SiC and Si substrates: A review from the small‐signal‐modeling's perspective

In this article, small‐signal modeling approaches for GaN HEMTs on SiC and Si substrates have been developed. The main advantage of these approaches is their accuracy, reliability, and dependency on only cold S‐parameter measurements to extract the parasitic elements of the device. The proposed equivalent circuit model for GaN on Si HEMT considers extra effects due to parasitic conduction through substrate or buffer layers. S‐parameter measurements at different bias conditions in addition to physical based analysis have been used to validate the accuracy and reliability of the developed modeling methods. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:389–400, 2014.     

Design of data feature-driven 1D/2D convolutional neural networks classifier for recycling black plastic wastes through laser spectroscopy

In this study, two types of convolutional neural network (CNN) classifiers are designed to handle the problem of classifying black plastic wastes. In particular, the black plastic wastes have the property of absorbing laser light coming from spectrometer. Therefore, the classification of black plastic wastes remains still a challenging problem compared to classifying other colored plastic wastes using existing spectroscopy (i.e., NIR). When it comes the classification problem of black plastic wastes, effective classification techniques by the laser spectroscopy of Fourier Transform-Infrared Radiation (FT-IR) with Attenuated Total Reflectance (ATR) and Raman to analyze the classification problem of black plastic wastes are introduced. Due to the strong ability of extracting spatial features and remarkable performance in image classification, 1D and 2D CNN through data features are designed as classifiers. The technique of chemical peak points selection is considered to reduce data redundancy. Furthermore, through the selection of data features based on the extracted 1D data with peak points is introduced. Experimental results demonstrate that 2DCNN classifier designed with the help of 2D data feature selection as well as 1DCNN classifier shows the best performance compared with other reported methods for classifying black plastic wastes.