Large-Signal Time-Domain Waveform-Based Transistor Modeling
Book chapter, 2013

Nonlinear models of microwave transistors are essential for the design of high-frequency nonlinear circuits, such as power amplifiers or mixers. Among the existing modeling techniques, measurement-based approaches have gained huge attention from researchers in the last decades. Especially, nonlinear measurements-driven model extraction is preferred for transistors exploited in the design of power amplifiers and mixers. This chapter mainly deals with the generation of empirical transistor models starting from large-signal time-domain waveforms. Specifically, a widely used model available in commercial CAD tools is adopted, and the extraction procedure of the model parameters is outlined in detail. Moreover the advantage of using time-domain waveforms at different frequencies is highlighted. More specifically, by making use of time-domain waveforms at frequencies in the kHz-MHz range, one can separately model the behavior of the transistor output current generator, which is more prone to low-frequency dispersive effects. In fact at low frequencies the effect of the nonlinear transistor capacitance is significantly reduced and, therefore, already "de-embedded" from the measured time-domain waveforms. Once the model of the output current generator is available, one can use high-frequency measurements to determine the nonlinear capacitances (or charges). Several modeling examples of different transistor technologies, such as gallium-arsenide and gallium-nitride, are reported. © 2014 Elsevier Ltd. All rights reserved.

Large-signal modeling

Large-signal measurements

I-V functions

Microwave transistors

Q-V functions

Empirical models

Author

Iltcho Angelov

Chalmers, Microtechnology and Nanoscience (MC2), Microwave Electronics

G. Avolio

KU Leuven

Dmmp Schreurs

KU Leuven

Microwave De-embedding: From Theory to Applications

189-223
9780124017009 (ISBN)

Subject Categories

Signal Processing

DOI

10.1016/B978-0-12-401700-9.00005-7

ISBN

9780124017009

More information

Latest update

5/29/2018