Nonlinear modeling of LDMOS transistors for high‐power FM transmitters

Summary A simple small‐signal equivalent circuit based on the physical structure of silicon‐on‐insulator metal–oxide–semiconductor field‐effect transistor varactor for substrate loss effects is presented in this paper. The model includes a BSIMSOI charge formulation and physical geometry and process parameter based on parasitic modeling. Key device performances of capacitance and quality factor are validated over voltage, frequency, and geometry. The model, implemented in Verilog‐A, provides robust and accurate radiofrequency simulation of metal–oxide–semiconductor varactor. Copyright © 2016 John Wiley & Sons, Ltd.

Section: Methods Of Model Parameter Extractionmentioning

confidence: 99%

An improved model for substrate in RF SOI MOSFET varactor

Chen

Liu

2016

“…C GSP (fF) C GS0 (fF) C GDP (fF) C GD0 (fF) P 11 = P 41 (V À1 ) P 10 = P 40 P 21 = P 31 (V À1 ) P 20 = P 30 I DS , and the resistive parasitic elements. If the gate Schottky junction is not in forward or reverse conduction, R G and I GS can be neglected.…”

Section: A Fitting Of the IV Characteristicsmentioning

confidence: 99%

“…Typically, microwave transistor nonlinear models are described in terms of nonlinear currents and charges, which account for carrier transport and charge variations within the semiconductor materials. A great deal of works aimed at extraction and identification of nonlinear models of microwave transistors can be found in literature, including [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. A largely used FET model topology is reported in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

A procedure for the extraction of a nonlinear microwave GaN FET model

Avolio

Vadalà

Angelov

et al. 2016

Self Cite

In this paper, we describe an extraction procedure of nonlinear models for microwave field-effect transistor (FET). We use a nonlinear model available in commercial CAD tools, and we extract the parameters by combining direct extraction and numerical optimization. We determine a first estimate of the model parameters by few DC and S-parameter measurements. Next, we refine the parameters by optimization against low-frequency and highfrequency vector-calibrated large-signal measurements gathered with a Large-Signal-Network Analyzer (LSNA). As case study we consider a 0.25 × 200 μm 2 GaN FET on SiC for power amplifier applications. Ultimately, we want to show that a good accuracy level can be achieved while minimizing the extraction effort and that an accurate model can be built and suitably tailored depending on the final application.

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Marinković

Crupi

Caddemi

et al. 2019

Self Cite

The purpose of this paper is to provide a comprehensive overview of the fieldeffect transistor (FET) small-signal modeling using artificial neural networks (ANNs). To gain an in-depth insight into how to effectively develop an ANN model, we present a comparative study on the application of the ANNs for modeling the scattering (S-) parameters of a variety of FET technologies versus bias point, ambient temperature, and geometrical dimensions. As will be shown, the main challenge consists of identifying the most appropriate ANN model for the specific case under study. This is because the performance of an ANN-based model can vary significantly, depending especially on the choice of the model structure and the size and parameters of the chosen ANN. In addition, the choice of the model is related directly to the behavior of the FET characteristics, which might greatly depend on the selected device technology and operating conditions. The analysis of the present comparative study allows understanding how to properly construct ANN models to perform at their best for a successful FET modeling.