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...

Abstract-This paper presents a new advance in Neuro-space mapping (Neuro-SM) techniques for modeling nonlinear microwave devices. Suppose that existing device models (namely, coarse models) cannot match the behavior of a new device (referred to as the fine model). By neural network mapping of the voltage and current signals from the coarse to the fine models, Neuro-SM can modify the behavior of the coarse model to match that of the fine model. However, the efficiency of mapping depends on both the mapping structure and the coarse model. In this paper, a structural optimization technique is presented to achieve optimal combinations of mapping structure and coarse model. An aggressive optimization formulation exploring detailed structural variations in both the mapping and the coarse model is proposed, where the internal branches of coarse models and separate mappings for the voltage and current at gate and drain are used as basic topology variables. The formulation of such a structural optimization by an evolutionary optimization algorithm is proposed. Numerical examples of metal-semiconductor field-effect transistor and high electron-mobility transistor modeling demonstrate that, by using the proposed algorithm, optimal combinations of space mapping and coarse model structures can be achieved leading to the best modeling accuracy with the simplest mapping function.Index Terms-Genetic algorithms, knowledge-based modeling, nonlinear device modeling, space mapping.

This paper develops an analytical framework for ramp metering, under which various ramp control strategies can be viewed as ramifications of the same most-efficient control logic with different threshold values, control methods, and equity considerations. The most-efficient control logic only meters the entrance ramps nearest critical freeway mainline sections so as to eliminate freeway internal queues, which is derived from a new formulation of the optimal ramp control problem. Instead of assuming the availability of real-time origin-destination information, the new formulation takes advantages of the stability and predictability of off-ramp exit percentages. Those properties of the off-ramp exit percentages are supported by empirical data, and allow us to formulate the optimal ramp control problem as a linear program whose input variables are all directly measurable by detectors in real-time. The solution is also tested on a real-world freeway section in a microscopic traffic simulator for demonstration. Time-dependent origindestination tables and off-ramp exit percentages are compared as two alternative ways to represent the true real-time demand patterns that are important to freeway ramp metering.

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