Compact Modeling of Static and Transient Effects of Buffer Traps in GaN HEMTs
Ajay Shanbhag, M. P. Sruthi, Anjan Chakravorty, Nandita DasGupta, Amitava DasGupta
Abstract
We propose a physics-based analytical model that accurately captures the effects of buffer traps on dc characteristics of gallium nitride (GaN)-based high-electron-mobility transistors (HEMTs). The model is then semi-analytically extended to additionally include the transient behavior. Analytical formulations for the shift in the threshold voltage <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${(}{V}_{\text {OFF}}{)}$ </tex-math></inline-formula> and two-dimensional electron gas (2-DEG) density due to the presence of buffer traps in the steady state are presented. In pulsed operation, technology computer-aided design (TCAD) simulations indicate that a time-dependent negative potential (NP) is developed under the gate, resulting in a modified <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${V}_{\text {OFF}}$ </tex-math></inline-formula> and current collapse (CC). An expression for the modified <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${V}_{\text {off}}$ </tex-math></inline-formula> helps capture the pulsed current–voltage characteristics. The model captures the dependence of bias, time, temperature, trap concentration, capture cross section area, and activation energy of traps on the steady-state and transient characteristics. The model is implemented in Verilog-A in an existing compact model framework using a diode and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RC</i> sub-circuit and validated using measured data and TCAD simulations. The modeling results are in excellent agreement with the experimental data and TCAD simulations. Since the model is physics-based, it requires fewer number of parameters compared to that in the existing models.