2022
DOI: 10.1007/s10973-022-11371-y
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Modeling of access resistances and channel temperature estimation for GaN HEMT

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Cited by 4 publications
(4 citation statements)
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“…From the T max model expression, it can be found that the thermal resistance is mainly inversely proportional to the thermal conductivity of the material, and its proportional coefficient is mainly determined by the device's structure parameters. [ 51,55 ] Thus, T eq can be modeled asTeq=T0+(Rsub +RGaN)Pdiss$$T_{\text{eq}} = T_{0} + \left(\right. R_{\text{sub }} + R_{\text{GaN}} \left.\right) P_{\text{diss}}$$where P diss is the power dissipation, and R GaN and R sub are the thermal resistance of the GaN buffer and substrate layers, respectively, which can be modeled asRGaN=αGaN 1κGaN (T)$$R_{\text{GaN}} = \left(\alpha\right)_{\text{GaN }} \frac{1}{\left(\kappa\right)_{\text{GaN }} \left(\right.…”
Section: Model Descriptionmentioning
confidence: 99%
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“…From the T max model expression, it can be found that the thermal resistance is mainly inversely proportional to the thermal conductivity of the material, and its proportional coefficient is mainly determined by the device's structure parameters. [ 51,55 ] Thus, T eq can be modeled asTeq=T0+(Rsub +RGaN)Pdiss$$T_{\text{eq}} = T_{0} + \left(\right. R_{\text{sub }} + R_{\text{GaN}} \left.\right) P_{\text{diss}}$$where P diss is the power dissipation, and R GaN and R sub are the thermal resistance of the GaN buffer and substrate layers, respectively, which can be modeled asRGaN=αGaN 1κGaN (T)$$R_{\text{GaN}} = \left(\alpha\right)_{\text{GaN }} \frac{1}{\left(\kappa\right)_{\text{GaN }} \left(\right.…”
Section: Model Descriptionmentioning
confidence: 99%
“…Therefore, there is usually a certain distance from gate to source/drain (access region), which produces a nonlinear resistance. The resistance depends on the current I ds flowing through the access region, defined as [ 18,55 ] Rd/s=Rd0/s0+Lgd/sQacc,d/sμacc,d/s[ 1(IdsIds,sat)δ ]1δ$$R_{\text{d/s}} = R_{\text{d0/s0}} + \frac{L_{\text{gd/s}}}{Q_{\text{acc,d/s}} \left(\mu\right)_{\text{acc,d/s}} \left(\left[\right. 1 - \left(\left(\right.…”
Section: Model Descriptionmentioning
confidence: 99%
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