A scalable and multibias parameter extraction method for a small‐signal GaN HEMT model

Abstract

In this paper, a scalable and multibias parameter extraction method for gallium nitride high electron mobility transistor small‐signal model has been proposed. The main advantage of this method is that the temperature‐dependent access resistances and nonlinear thermal resistance have been taken into account, which makes the extracted access resistances and intrinsic elements more physical and reliable at different bias conditions for various device dimensions. The new modeling method has been validated by comparing the simulated small‐signal S‐parameters with the measured data over a wide range of frequency.

A reliable and efficient small‐signal parameter extraction method for GaN HEMTs

“…Compared with the determined values of R g and R s in Wen, Xu, Wang, Zhao, and Xu's work, 26 the ones obtained in this work are more reasonable. Moreover, the values of R d and R s obtained in Table 2 are scalable with gate-to-source and gate-to-drain spacing and device gate width, 9,30 which is another clear improvement compared with Wen, Xu, Wang, Zhao, and Xu's work. 26 According to the physical definition, R d and R s can be expressed as 9…”

“…The physical meaning of the equivalent circuit is straightforward. 9 In the extrinsic part of this SSECM, C pg and C pd represent parasitic capacitances because of pad and air-bridge connection; L g , L d , and L s represent the effect of metallization inductances; R g consists mainly of the gate metallization resistance, while R d and R s consist of two parts, ie, the ohmic-contact resistance and the access resistance, respectively. In the intrinsic part, charging and discharging process for the depletion region under the gate is described by C gs , R i , C gd , and R gd .…”

“…7,8 Furthermore, the small signal parameters can provide an essential feedback for device manufactures. 9 Thus, small-signal modeling is of great importance to the development and application of GaN technology.…”

A review on the artificial neural network applications for small‐signal modeling of microwave FETs

“…Therefore, ANNs are widely used also for modeling and predicting the scattering (S-) parameters of microwave field-effect transistors (FETs). 7,8,11,13,18,[40][41][42][43][44][45][46][47][48][49] The main advantage of an ANN-based FET model is that, because of the "black-box" nature of ANN models, the S-parameters can be straightforwardly and accurately reproduced without requiring the extraction of an equivalent-circuit model [50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68] or a detailed knowledge of the FET physics. [69][70][71][72][73] As a matter of fact, by exploiting ANNs, it is possible mathematically describe the observable inputoutput relationships.…”

Small signal behavioral modeling technique of GaN high electron mobility transistor using artificial neural network: An accurate, fast, and reliable approach

“…[5][6][7][8][9][10] In this context, empirical modeling approach is very common for the development of small signal model using cold pinch off condition, deembedding structures, and analytical techniques. [11][12][13][14][15][16][17][18][19] It is imperative to note that an accurate and reliable small signal equivalent model will ensure the development of reliable large signal and noise models. [20][21][22][23][24][25][26] The design of circuits generally requires behavior of the transistor and in this context, the analytical techniques have been found to be too involved due to technology dependency, and complex parameter extraction procedure.…”