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Christian Fager, Microwave Electronics Laboratory, Chalmers University of Technology,
Kristoffer Andersson, Microwave Electronics Laboratory, Chalmers University of Technology,
Matthias Ferndahl, Microwave Electronics Laboratory, Chalmers University of Technology
Almost all computer-aided RF and microwave circuit design work relies on small-signal equivalent circuit models  (see Chapters 3 and 5). These models are used to replicate, as accurately as possible, the linearized electrical characteristics of the device under test. However, despite all efforts spent on deriving physically correct model topologies, surprisingly little research has been reported on how to extract the parameters of these models accurately.
There are several sources of uncertainty in the modeling process that contribute to inaccuracies in the models and their parameter estimates. By using a stochastic approach that acknowledges the fact that microwave and RF measurements are, indeed, associated with uncertainties and incorporating this approach with existing model extraction methods, it becomes possible to quantify the uncertainties in the models obtained. The statistical approach adopted in this chapter will be used as a framework for the application of statistical parameter estimation methods that are often used in other applications but not in the context of microwave and RF small-signal modeling. As a consequence, the accuracy of the models obtained is significantly improved when compared to traditionally used methods.
Different simple modeling examples will be used to illustrate how measurement uncertainties propagate to uncertainties in the models and their parameters when different extraction methods are applied. The application to small-signal transistor models will be of particular focus, since they form the basis for large signal transistor modeling and thus indirectly influence the performance when designing complex active circuits (see Chapter 5).
Designing microwave circuits today means relying on numerical circuit simulation. While not a substitute for one's own skills, knowledge, and experience, a designer must be able to count on the adequacy of circuit simulation tools to accurately simulate the circuit performance. Circuit simulators themselves are generally up to the challenge. However, there is a perpetual quest for good transistor models to use with the simulator, because models are usually the limiting factor in the accuracy of a simulated design. This is due to the continuous evolution of transistor technology, requiring the models to keep up, and also to the increasing demands placed on the models to perform with respect to wider classes of signals, operating conditions (e.g., temperature), and statistical variation. Circuit designers therefore often face the challenge of adapting the models that are provided with simulators to better describe the actual transistor that is being used in the design. This is achieved by characterizing the transistor, mainly by measurement, but also by electromagnetic and/or thermal simulation. Finally, model parameter values must be extracted from this data before the model can be used at all in a design.
As transistor modeling is a key to circuit design, many publications are available on the models for any type of transistor, ranging from model documentation in simulator products, to application notes and scientific papers in technical conferences and journals; but it seems that much less is published on how the respective model parameters can be determined.
Achieve accurate and reliable parameter extraction using this complete survey of state-of-the-art techniques and methods. A team of experts from industry and academia provides you with insights into a range of key topics, including parasitics, intrinsic extraction, statistics, extraction uncertainty, nonlinear and DC parameters, self-heating and traps, noise, and package effects. Learn how similar approaches to parameter extraction can be applied to different technologies. A variety of real-world industrial examples and measurement results show you how the theories and methods presented can be used in practice. Whether you use transistor models for evaluation of device processing and you need to understand the methods behind the models you use, or you want to develop models for existing and new device types, this is your complete guide to parameter extraction.
This paper presents the design and implementation of an inverse class-F power amplifier (PA) using a high power gallium nitride high electron mobility transistor (GaN HEMT). For a 3.5 GHz continuous wave signal, the measurement results show state-of-the-art power-added efficiency (PAE) of 78%, a drain efficiency of 82%, a gain of 12 dB, and an output power of 12 W. Moreover, over a 300 MHz bandwidth, the PAE and output power are maintained at 60% and 10 W, respectively. Linearized modulated measurements using 20 MHz bandwidth long-term evolution (LTE) signal with 11.5 dB peak-to-average ratio show that −42 dBc adjacent channel power ratio (ACLR) is achieved, with an average PAE of 30%, −47 dBc ACLR with an average PAE of 40% are obtained when using a WCDMA signal with 6.6 dB peak-to-average ratio (PAR).
In this paper, the main nonidealities appearing in polar transmitters will be addressed, together with several implementation considerations. Special attention will be paid to the role of AM modulation nonlinearity and parasitic AM-to-PM conversion, once architecture mechanisms such as time-delay mismatch between branches or limited bandwidth in the amplitude path are controlled. The device limiting factors for a highly efficient switched mode operation and a linear amplitude modulation will be identified. Some circuit design and implementation guidelines for the RF modulating stage and the envelope amplifier will be discussed, to finish with system-level analysis considerations under two-tone and real communication signal excitations.
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