Preface

Today, addressing the technical challenges posed by system complexities requires a broad range of innovative, multidisciplinary, physics-based, problem-matched analytical and computational skills that are not adequately covered in conventional electrical-electronics (EE) engineering curricula. A great many higher educational institutions are now actively engaged in efforts to define “what makes a modern engineer” and to design curricula for teaching the necessary skills to a computer-weaned generation of students, with access to the internet and consequent globalization of information. The explosive growth of computer capabilities has revolutionized communication and the analysis of complex systems, and has made interdisciplinary exposure necessary in modern EE engineering. Physics-based modeling, observation-based parameterization, computer-based simulations, and code calibration against canonical problems (i.e., problems that have mathematical exactness and numerically computable forms) are the key issues of these challenges.

As phrased by Einstein, “in the matter of physics, first lessons should contain nothing but what is experimental and interesting to see—experimentation and hands-on training are the key issues in engineering education at least at undergraduate level.” On the other hand, with the development of new computer technologies, interactive multimedia programming languages, and the Internet, it is now possible to simulate engineering and science laboratory projects of all sorts on a computer all around the world. Experiment-oriented problems can be offered without the overhead incurred when maintaining a full laboratory. At this point the question arises: should an intelligent balance be established between real and virtual experimentations and how? Another similar problem is the balance to be maintained between teaching essentials (theory) and cranking the gear (blind computer applications). It is a general observation that the motto “I did it, it works” is widespread among the youth of today, without really grasping the general principles and boundaries of validity of the underlying phenomena and what is even worse with a false sense of satisfaction.

Engineering, as given in the American Society for Engineering Education web site (www.asee.org), is “the art of applying scientific and mathematical principles, experience, judgment, and common sense to make things that benefit people.” That is, it is the process of producing a technical product or system to meet a specific need in a society. Engineering education is a university education, where knowledge of mathematics and natural sciences are gained, followed up by lifetime self-education where experience is piled up with practice. Therefore, the four keywords mathematics, physics, experience, and practice are the untouchables of engineering education.

Many applications in science and technology rely increasingly on field theory and circuit theory computations in either man-made or natural complex structures. Wireless communication systems, for example, pose challenging problems with respect to field propagation prediction, microwave hardware design, compatibility issues, biological hazards, and so on. Nanotechnologies, on the other hand, have challenges of locating multimillion circuit elements and subsystems on a few square centimeter chips, with very low emissions and immune to environmental interference. Moreover, need to and use of these theories are not limited to EE applications only; they are exploited in a very wide spectrum ranging from biomedical to geophysical applications. Since different problems have their own combination of geometrical features and scales, frequency ranges, material properties, and so on, no single method or approach is best suited for handling all possible cases; instead, a combination of methods, “hybridization,” is needed to attain the greatest flexibility and efficiency in engineering. Relations between field theory and network (circuit) theory play an important role in this respect.

The necessity for hybrid methods has already been recognized in the past: for example, in scattering and antenna problems, techniques have been devised that combine the method of moments (MoM) and the geometrical theory of diffraction (GTD) or physical theory of diffraction (PTD). Similarly, numerical methods such as finite elements (FE) or finite differences (FD) have been considered in conjunction with MoM, with integral equations, with boundary integrals, with modal techniques, with multipole methods, and so on. Combinations of other methods, for example, boundary contour and mode matching or hybrid electric field integral equations (EFIE) and magnetic field integral equations (MFIE) denoted as HEM, have also been proposed.

Physics-based modeling and observable-based parameterization are very important in EE engineering education. The models that are established via well-known Maxwell equations (field theory) and transmission line equations (circuit theory) in both time and frequency domains parameterize a complex physical problem well defined that guarantees existence, uniqueness, and convergence. Field and circuit theories are dual; that is, any field problem (e.g., antenna radiation) can be transformed into a circuit theory problem and solved there (or vice versa). Starting after World War II, circuit formulations of field problems have also been employed extensively in the design of microwave, optical, and other closed and open waveguiding and radiating systems.

Another very important occurrence of hybridization is the increase in integrated circuit (IC) performance being exponential in time at rates of more than 100/decade, with the critical device dimensions shrinking and the interconnects between devices becoming smaller and more closely spaced, interconnect delays (ID) started to dominate over gate delays (GD); the ratio GD/ID of the order of 7–8 in favor of interconnects in early nineties is expected to be of the order of nearly 1/20 in favor of gates within a couple of years. As the count of active devices exceeds several tens of millions and the number of interconnects among these devices grows superlinearly with this count, efficient evaluation of time delays and signal integrity becomes more difficult and important. Devices with operating frequencies exceeding a hundred gigahertz have already appeared and today's circuits contain millions of transistors per unit area as opposed to 1970s SPICE targeted for circuits with a few hundred transistors. Hence the need arises for a new generation of simulators with improved numerical methods using, if possible, analytic solution techniques to handle very large circuits.

Engineering as defined above is based on practice. The minima of this practice should be given during the EE education. This has become more and more comprehensive and expensive parallel to high-technology devices developed and presented to societies: computers and other microprocessor-based devices make EE engineering education not only very complex but also interdisciplinary as well. The cost of building undergraduate labs in EE may vary from 1 unit to 105 units; for example, a spectrum or a network analyzer may cost few 104 units, whereas a simple software of 1 unit with or without the addition of specific cards costing 102 units may turn a regular personal computer (PC) into a virtual lab. The key question therefore is to establish a balance between virtual and real labs, so as to optimize cost problems, while graduating sophisticated engineers with enough practice.

Doing numerical simulations in EE engineering has become as easy (as well as difficult) as doing measurements. It is easy because one can purchase commercial codes that do almost everything, such as supplying computer-controlled devices for measurements. The simulation packages are user friendly, have self-checking routines for control, and all can be calibrated, like most of high-tech measurement devices. On the other hand all the efforts of simulation can be in vain if one does not know how to interpret the resulting numbers. In addition, they are capable of doing only what has already been planned and included by the developer. Moreover, important concepts such as accuracy, precision, and resolution, in short the underlying theory, should be well understood by engineers.

This book aims to introduce simple, easy-to-use, but effective short codes as well as virtual tools that can be used in broad range of EE engineering lectures. The book itself may serve as a textbook for several lectures such as electromagnetic modeling and simulation, computational electromagnetics, transmission line theory, guided wave theory, diffraction theory, and others. Almost all of the virtual tools are coded in MATLAB; therefore, the reader is strongly advised to get used to working with MATLAB. The book contains 16 chapters. Roughly speaking, the first five chapters are introductory, the next five chapters are for analytical modeling, and the last six chapters are for numerical modeling and simulation.

People had to simplify problems as much as possible a century ago in order to get the feeling on the results. This is why we have had excellent canonical problems with simple analytical models. Today, we very often revisit these problems for (i) teaching electromagnetics and (ii) validation, verification, and calibration of numerical models. That is why we included many canonical problems in this book (although not very often, we also use numerical models in validation and verification of analytical...