Modeling and computer-aided design (CAD) techniques are essential for microwave design, especially with our drive towards first-pass design success. In the past few decades, tremendous progress in microwave CAD has led to a large variety of microwave models for passive and active devices and circuit components. The high quality and the availability of these models have enabled us to design circuits efficiently. These models have also allowed us to design larger and more complicated circuits than ever before.At the same time, new technologies and materials, emerging and non-traditional devices continue to evolve. Although the existing models are good for modeling mature technologies and existing devices, they are often inadequate or unsuitable when new devices are needed in system design. Conventional approaches to create or modify models are heavily based on slow trial-and-error processes.As new technologies and devices continue to evolve, we need not only new models, but also computeraided modeling algorithms such that model development becomes fast and systematic.At high frequencies, equivalent circuit models often lack fidelity. Detailed electromagnetic (EM) based simulations become essential to achieve design accuracy. However, EM simulations are computationally expensive especially when physical or geometrical parameters have to be repeatedly adjusted during design cycle. With the increasing design complexities, coupled with tighter component tolerances and shorter design cycles, there is a demand for design methodologies that are both accurate and fast at the same time. These are contradictory requirements and difficult to satisfy with conventional CAD techniques. The problem becomes even more severe in yield optimization and statistical validation where process variations and manufacturing tolerances of components are required to be taken into account. In addition, accurate parametric modeling techniques have become increasingly necessary, where we strive to describe not only the behavior of the microwave device, but also the change of the behavior against physical or geometrical parameters of the device.In recent years, neural network (NN) or artificial neural network (ANN) techniques have been recognized as a useful alternative to conventional approaches in microwave modeling [1]- [2]. Artificial neural networks can be used to develop new models or to enhance the accuracy of existing models. Neural networks learn device data through an automated training process, and the trained neural networks are then used as fast and accurate models for efficient high-level circuit and system design. These models have the ability to capture multi-dimensional arbitrary nonlinear relationships. The theoretical basis of neural network is based on the universal approximation theory [3], which states that a neural network with at least one hidden layer can approximate any nonlinear continuous multidimensional function to any desired accuracy. This makes neural networks a useful choice for device modeling where a mathemati...

This 2007 book is a comprehensive exposition of FET modeling, and is a must-have resource for seasoned professionals and new graduates in the RF and microwave power amplifier design and modeling community. In it, you will find descriptions of characterization and measurement techniques, analysis methods, and the simulator implementation, model verification and validation procedures that are needed to produce a transistor model that can be used with confidence by the circuit designer. Written by semiconductor industry professionals with many years' device modeling experience in LDMOS and III-V technologies, this was the first book to address the modeling requirements specific to high-power RF transistors. A technology-independent approach is described, addressing thermal effects, scaling issues, nonlinear modeling, and in-package matching networks. These are illustrated using the current market-leading high-power RF technology, LDMOS, as well as with III-V power devices.

In this paper, we present an envelope-domain behavioral model of a high-power RF amplifier. In this modeling approach, we use the signal envelope information, and the behavioral model is generated using an established nonlinear time-series approach to create a time-domain model that operates in the envelope or signal domain. We have generated a model of a 200-W Doherty amplifier from measured IQ data taken using a wideband code-division multiple-access excitation; the amplifier was driven from the linear regime into saturation. The time-series model was created using a time-delay embedding identified from auto-mutual information analysis, and an artificial neural network was used to fit the multivariate transfer function. The model has been validated using measured and simulated data, and it has been used in the development of a system-level design of a digital pre-distorter. © 2006 IEEE

The use of substrate integrated waveguides (SIW) for microwave and millimeter wave integrated components has increased dramatically over the last decade. They mimic the performance of conventional metallic waveguides and they are fabricated using printed circuit boards using the top and bottom metallization with two rows of vias forming the side walls. This creates a low profile, compact, and light weight alternative to conventional metallic waveguides, and they allow a direct interconnection with printed circuit boards and active components. This article reviews the fundamental theory, documents the research that has been performed over the past decade, and summarizes progress up to the recent state-of-the-art including novel SIW structures for passive circuits and antennas as well as new applications for reconfigurable and printed circuits using SIW technology.

Abstract-A new nonlinear, charge-conservative, scalable, dynamic electro-thermal compact model for LDMOS RF power transistors is described in this paper. The transistor is characterized using pulsed I-V and S-parameter measurements, to ensure isothermal conditions. A new extrinsic network and extrinsic parameter extraction methodology is developed for high power RF LDMOS transistor modeling, using manifold de-embedding by electromagnetic simulation, and optimization of the extrinsic network parameter values over a broad frequency range. The intrinsic model comprises controlled charge and current sources that have been implemented using artificial neural networks (ANNs), designed to permit accurate extrapolation of the transistor's performance outside of the measured data domain. A thermal sub-circuit is coupled to the nonlinear model. Largesignal validation of this new model shows a very good agreement with measurements at 2.14 GHz.