| 1 | Modeling and simulation of enhancement mode p-GaN Gate AlGaN/GaN HEMT for RF circuit switch applications显示文摘An enhancement mode p-GaN gate AlGaN/GaN HEMT is proposed and a physics based virtual source charge model with Landauer approach for electron transport has been developed using Verilog-A and simulated using Cadence Spectre,in order to predict device characteristics such as threshold voltage,drain current and gate capacitance.The drain current model incorporates important physical effects such as velocity saturation,short channel effects like DIBL(drain induced barrier lowering),channel length modulation(CLM),and mobility degradation due to self-heating.The predicted I_d–V_(ds),I_d–V_(gs),and C–V characteristics show an excellent agreement with the experimental data for both drain current and capacitance which validate the model.The developed model was then utilized to design and simulate a single-pole single-throw(SPST)RF switch. | D.K.Panda T.R.Lenka | 2017 | Journal of Semiconductors2017，38，6： | 5 |
| 2 | Small-signal model parameter extraction of E-mode N-polar GaN MOS-HEMT using optimization algorithms and its comparison显示文摘An improved small-signal parameter extraction technique for short channel enhancement-mode N-polar GaN MOS-HEMT is proposed, which is a combination of a conventional analytical method and optimization techniques. The extrinsic parameters such as parasitic capacitance, inductance and resistance are extracted under the pinch-off condition. The intrinsic parameters of the small-signal equivalent circuit(SSEC) have been extracted including gate forward and backward conductance. Different optimization algorithms such as PSO, Quasi Newton and Firefly optimization algorithm is applied to the extracted parameters to minimize the error between modeled and measured S-parameters. The different optimized SSEC models have been validated by comparing the S-parameters and unity current-gain with TCAD simulations and available experimental data from the literature. It is observed that the Firefly algorithm based optimization approach accurately extracts the small-signal model parameters as compared to other optimization algorithm techniques with a minimum error percentage of 1.3%. | d.k.panda g.amarnath t.r.lenka | 2018 | Journal of Semiconductors2018，39，7： | 2 |
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