研究目的
To develop a nonlinear model for simulating the temperature-dependent I–V characteristics of submicron GaN HEMTs, incorporating self- and ambient heating effects to accurately predict both negative and positive conductance in the saturation region.
研究成果
The proposed nonlinear model accurately simulates the DC characteristics of submicron GaN HEMTs, including the effects of self-heating and ambient temperature. It outperforms existing models in predicting both positive and negative conductance in the saturation region, with RMSE improvements ranging from 17 to 50%. The model is suitable for integration into CAD software for power circuit design involving GaN HEMTs.
研究不足
The model's accuracy is dependent on the optimization of parameters using the PSO algorithm, which may not capture all physical phenomena in GaN HEMTs. Additionally, the model's validation is limited to the specified range of gate lengths and temperatures.
1:Experimental Design and Method Selection:
The study employs a nonlinear model to simulate the I–V characteristics of GaN HEMTs, incorporating self-heating and ambient heating effects. The model is validated against experimental data for devices with gate lengths ranging from 0.12 to 0.7 μm across temperatures from 298 to 773 K.
2:12 to 7 μm across temperatures from 298 to 773 K.
Sample Selection and Data Sources:
2. Sample Selection and Data Sources: GaN HEMTs with varying gate lengths and widths are selected for validation. Experimental data from previous studies are used as references.
3:List of Experimental Equipment and Materials:
The study utilizes MATLAB for model optimization and simulation. Specific device parameters are detailed in the paper.
4:Experimental Procedures and Operational Workflow:
The model parameters are optimized using the particle swarm optimization (PSO) algorithm. The model's accuracy is assessed by comparing simulated and experimental I–V characteristics.
5:Data Analysis Methods:
The root-mean-square error (RMSE) is used to evaluate the model's performance against experimental data.
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