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IEEE Expert Now Educational Courses
IEEE Expert Now selects the best IEEE educational courses and tutorials from conferences and workshops around the world. Delivered in a series of engaging and highly interactive, online learning courses, these courses have been developed by recognized experts in a wide range of engineering and research technologies.
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AEROSPACE
“Cooperative Control of Multiagent Systems: Synthesis and Experimentation” by Camille-Alain Rabbath, sponsored by the IEEE Systems, Man & Cybernetics Society The Cooperative Control of Multiagent Systems course will illustrate the various attributes needed in such systems and the complexity inherent to the design. Cooperative systems are currently limited in capacity and in availability, partly due to this so-called complexity and to the multifaceted nature of design and analysis. This course will focus on the well-known problem of multiagent path planning, with brief discussions of advanced techniques for formation flight health management. The optimization problem and its solution will be cast in the framework of dynamic programming and Markov decision processes, typical of problems of optimization under uncertainty. A discussion of the results of numerical simulations, integrating decision-making with closed-loop dynamics of the air vehicles, for both formation flight and path planning, will conclude the course. After completing you should be able to develop an understanding of:
C.A. Rabbath is currently Defence Scientist at Defence Research and Development Canada - Valcartier. He also holds adjunct professorship positions at Concordia University and McGill University, Montreal, Canada. Dr. Rabbath received the PhD degree in 1999 from McGill University. He then worked in industry from 1999 to 2002 in control systems design, and in modeling and simulation of aerospace and robotic systems.
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"Real-Time Embedded Computing for Signal & Image Processing" by David Martinez, sponsored by the IEEE Aerospace & Electronic Systems Society This course presents an overview of the current developments in the field of embedded computing drawing from signal and image processing applications. Computing complexity drivers will be reviewed as well as implementation approaches in hardware and software. The course concludes with a discussion on recent trends. After completing this course you should be able to develop an understanding of:
David Martinez is Associate Division Head of the Sensor Systems Division at MIT Lincoln Laboratory.
IEEE
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"Software Safety for Aerospace Applications" by Alan Tribble, sponsored by the IEEE Aerospace & Electronic Systems Society This course provides an overview of software safety as it relates to the safety of the overall computing system. In particular, learners will gain an understanding of the various software safety standards used in the aircraft industry, traditional safety analysis techniques, and current research and development efforts in the field. After completing this course you should be able to develop an understanding of:
Alan Tribble has over sixteen years of industrial experience and is currently with Rockwell Collins, Advanced Technology Center.
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"Space-Time Adaptive Processing for Radar" by Michael L. Picciolo and Scott Goldstein, sponsored by the IEEE Aerospace and Electronic Systems Society Space-Time Adaptive Processing (STAP) is an advanced signal processing methodology for the Ground Moving Target Indication (GMTI) mode of airborne and spaceborne surveillance radar systems. It is used to mitigate motion-induced spread-Doppler clutter that interferes with the echo from ground targets. This course will develop and clearly illustrate the GMTI problem from first principles, showing the need for STAP processing. Traditional STAP processing solutions will be derived from a detection probabilistic perspective - the most pertinent metric for radar. After completing you should be able to develop an understanding of:
Dr. Picciolo is an Adaptive Signal Processing Analyst at SAIC in Chantilly, VA. He works in the areas of Space-Time Adaptive Processing (STAP) algorithms, SAR / GMTI radar, Geolocation algorithms, and Image Processing. Dr. Goldstein is a Vice President at SAIC and has over 20 years of experience in the fields of radar, sonar, communications, navigation, and imaging sensors. He has performed fundamental research and development in the technical areas that support C3I and ISR functions.
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Coming 4Q 2008: “Design of Electrically Small Antennas” by Steven Best, sponsored by the IEEE Antennas and Propagation Society As today’s ubiquitous wireless devices decrease in size, there is an increasing demand for physically smaller antennas, yet the performance requirements are rarely relaxed. Optimizing the performance properties of electrically small antennas represents a significant design challenge for the antenna engineer. This course provides a discussion on the fundamental theory, challenges and performance trade-offs associated with the design of electrically small antennas. The course begins with a brief overview of the basic theory and concepts associated with electrically small antennas. This segment of the presentation provides an understanding of antenna performance limitations in terms of impedance, radiation patterns, bandwidth, efficiency, and quality factor. Techniques used to design self-resonant electrically small antennas are described and compared. These include dielectric loading, linear loading (increasing wire length), top-loading, and “folded” configurations. The relationship between the antenna’s performance characteristics and its physical properties is discussed. Issues such as the significance of antenna geometry are considered. The performance of the small antenna on small finite ground planes is considered with a particular emphasis on how the antenna’s location on the ground plane affects impedance, pattern and polarization properties. The course concludes with a discussion on recent advances made in the design of low profile, conformal and integrated device antennas. After completing you should be able to develop an understanding of:
Steven R. Best received the B.Sc.Eng and the Ph.D. degrees in Electrical Engineering from the University of New Brunswick, Canada in 1983 and 1988, respectively. He has over 20 years of experience in business management and antenna design engineering in both military and commercial markets. He is currently a Principal Sensor Systems Engineer with the MITRE Corporation in Bedford, MA where he is involved in supporting a number of government programs. Dr. Best is an Adjunct Professor at Northeastern University, Tuft’s University and UMass-Lowell. He is the author or co-author of over 100 papers in various journal, conference and industry publications. He was the 2004 and 2005 recipient of the AFRL Sensors Directorate Chief Scientist Award. He was formerly a Distinguished Lecturer for the IEEE Antennas and Propagation Society and an Associate Editor for the IEEE Antennas and Wireless Propagation Letters. Dr. Best is a frequent reviewer for several IEE and IEEE journals. He is an Associate Editor of the IEEE Transactions on Antennas and Propagation, a member of the IEEE APS AdCom and Junior Past Chair of the IEEE Boston Section.
Coming 4Q 2008: "A Primer on Cluster Analysis: Models and Alogrithms" by James C. Bezdek sponsored by the IEEE Computational Intelligence Society from the IEEE World Congress on Computational Intelligence (WCCI) This course- the first in a series of three - provides a foundation for understanding the field of cluster analysis in unlabeled data. The target audience for this course comprises undergraduate and graduate students majoring in engineering and science, as well as practicing engineers and scientists interested in either research about or applications of clustering to real world problems such as data mining, image analysis and bioinformatics. The subject matter is widely available in a number of standard textbooks given in the references below. The course begins with a discussion of the general nature of clustering. Three problems are identified: tendency assessment, partitioning and validation. Two types of data are discussed: object vector data, and pair wise objects relational data. Next, I develop the mathematical structure needed to carry clustering algorithms, discussing the notions of similarity, label vectors, partition matrices (U) and point prototypes (V). The second part of the course contains a description (and pseudo code) for one algorithm each from the four major categories of clustering methods. Specifically, I discuss and illustrate with a numerical example: (i) the U only model for single linkage clustering; (ii) the V only model for clustering with Kohonen's self-organizing map; (iii) the (U,V) model for clustering with the hard and fuzzy c-means models; and (iv) the (U,V,+) model for clustering using the expectation-maximization algorithm for Gaussian mixture decomposition. After completing this course you should be able to develop an understanding of:
Jim received the BS in Civil Engineering from U. of Nevada (Reno, 1969); and the PhD in Applied Mathematics Cornell University, 1973. Jim's interests include woodworking, optimization, motorcycles, pattern recognition, fine cigars, fishing, image processing, computational neural networks, blues music, and clustering in very large data sets. Jim is past president of NAFIPS, IFSA and the IEEE NNC (aka CIS), is the founding editor of the Int'l. Jo. Approximate Reasoning and the IEEE Transactions on Fuzzy Systems, is a fellow of the IEEE and IFSA, and is a recipient of the IEEE 3rd Millenium , IEEE Fuzzy Systems Pioneer and IEEE Frank Rosenblatt medals.
"Computational Intelligence: Natural Information Processing" by Leonid Perlovsky, sponsored by the IEEE Computational Intelligence Society This course covers the rapidly evolving field of Computational Intelligence and focuses on the current understanding of the fundamental principles of working the mind, their computational implementations, and practical applications. This course covers mind mechanisms, including concepts, emotions, instincts, behavior, language, cognition, understanding, thinking, intuitions, conscious and unconscious, abilities for formation of symbols and aesthetic feelings. Computational techniques are given for these mechanisms and abilities. The goal of this course is to provide a basic mathematical understanding of the working of the mind. Its secondary goal is to demonstrate practical applications of these mechanisms for pattern recognition, tracking, fusion, search engines, and for integrated systems combining sensor signals and communication data. Lastly, this course will outline future research directions. Historical and current difficulties in developing intelligent systems (IS) and applications will be discussed along with how the mind and new computational techniques overcome these difficulties. By the end of this course, learners will be familiar with several general applications addressed by IS, computational difficulties encountered over fifty years, and basic novel approaches to overcoming these difficulties. After completing this course you should be able to develop an understanding of:
Dr. Leonid Perlovsky is Principal Research Physicist and Technical Advisor at the Air Force Research Laboratory/SNHE.
IEEE
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“Cooperative Control of Multiagent Systems: Synthesis and Experimentation” by Camille-Alain Rabbath, sponsored by the IEEE Systems, Man & Cybernetics Society The Cooperative Control of Multiagent Systems course will illustrate the various attributes needed in such systems and the complexity inherent to the design. Cooperative systems are currently limited in capacity and in availability, partly due to this so-called complexity and to the multifaceted nature of design and analysis. This course will focus on the well-known problem of multiagent path planning, with brief discussions of advanced techniques for formation flight health management. The optimization problem and its solution will be cast in the framework of dynamic programming and Markov decision processes, typical of problems of optimization under uncertainty. A discussion of the results of numerical simulations, integrating decision-making with closed-loop dynamics of the air vehicles, for both formation flight and path planning, will conclude the course. After completing you should be able to develop an understanding of:
C.A. Rabbath is currently Defence Scientist at Defence Research and Development Canada - Valcartier. He also holds adjunct professorship positions at Concordia University and McGill University, Montreal, Canada. Dr. Rabbath received the PhD degree in 1999 from McGill University. He then worked in industry from 1999 to 2002 in control systems design, and in modeling and simulation of aerospace and robotic systems.
IEEE
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Coming 4Q 2008: "Fuzzy Cluster Analysis in Very Large Scale Data Sets" by James C. Bezdek sponsored by the IEEE Computational Intelligence Society from the IEEE International Joint Conference on Neural Networks This last module in the series discusses just one approach to the interesting and important problem of clustering in very large (VL) data. The target audience is graduate students majoring in engineering and science, and practicing engineers and scientists interested in either research about or applications of clustering applied to very large real world problems that occur in data mining, image analysis and bioinformatics. Almost none of the subject matter in this course is available in textbooks; almost all of it is the object of (my own) current research, and as such, it reflects my own bias, prejudices, background and interests. I have supplied references that contain pointers to many nice papers on these topics that use related or competitive methods that have been proposed and studied by others. I begin with a characterization of VL data. For me, this means any data set that you cannot load into your computer. Not an objective definition, but a definition that is easy to understand and practical, because there is a data set too big for any computer you use, and hence, VL for you. There are two main approaches to clustering in VL data; distributed clustering, and progressing sampling followed by extension. I discuss the first approach briefly, but it seems much more difficult to me than the second approach. Next, I define progressive sampling followed by (non-iterative) extension. This idea is pretty general: it can accelerate most (but not all) iterative algorithms that estimate parameters with loadable data (this is true for both clustering and classifier design!), and, it provides a means for approximating the outputs of many algorithms for unloadable data. So, one of the main points of this third course is to establish the basic ideas of progressive sampling and extension. The method of clustering in VL data by (sampling + extension) is developed and illustrated with four clustering algorithms: (i) extended fast fuzzy c-means (eFFCM) for segmentation of VL images; generalized fast fuzzy c-means (geFFCM) for clustering in VL object data (VL sets of feature vectors in p dimensions); (iii) generalized fast expectation maximization (geFEM) for clustering by Gaussian mixture decomposition in VL object data; and (iv), extended non-Euclidean relational fuzzy c-means (eNERF) for clustering in VL (square) relational data. These four methods are presented in the spirit of active research - i.e., parts of them clearly need improvement and more testing, and I expect much of this material to be replaced by better approaches as our understanding of clustering using this approach matures. After completing this course you should be able to develop an understanding of:
Jim received the BS in Civil Engineering from U. of Nevada (Reno, 1969); and the PhD in Applied Mathematics Cornell University, 1973. Jim's interests include woodworking, optimization, motorcycles, pattern recognition, fine cigars, fishing, image processing, computational neural networks, blues music, and clustering in very large data sets. Jim is past president of NAFIPS, IFSA and the IEEE NNC (aka CIS), is the founding editor of the Int'l. Jo. Approximate Reasoning and the IEEE Transactions on Fuzzy Systems, is a fellow of the IEEE and IFSA, and is a recipient of the IEEE 3rd Millenium , IEEE Fuzzy Systems Pioneer and IEEE Frank Rosenblatt medals.
"Implementations of Computational Intelligence Techniques" by Vincenzo Piuri and Fabio Scotti sponsored by the IEEE Computational Intelligence Society Computational Intelligence techniques are a powerful and adaptable approach to tackle problems and cases for which the conventional technologies have not been proved sufficiently effective. These results are achieved by mimicking some aspects of the knowledge representation and processing performed by the brain. The computational efforts implied by these approaches are usually quite relevant. Practical use of computational Intelligence technologies is constrained by limits (e.g., on performance, cost, accuracy) imposed by the envisioned application. The choice of the most appropriate implementation approach becomes therefore fundamental in order to find best balance among the computational intelligence characteristics and the application constraints. A methodological perspective becomes also useful in order to tackle the design of a dedicated system encompassing computational Intelligence components as most efficiently as possible. After completing this course you should be able to develop an understanding of:
Vincenzo PIURI obtained the Ph.D. in Computer Engineering in 1989, at Politecnico di Milano, Italy. From 1992 to September 2000, he was Associate Professor in Operating Systems at Politecnico di Milano. Since October 2000 he is Full Professor in Computer Engineering at the University of Milano, Italy. He was Visiting Professor at the University of Texas at Austin during the summers from 1993 to 1999. Fabio Scotti received the Dr. Ing. degree in electronic engineering in 1998 from Politecnico di Milano, Milano, Italy, where he is currently pursuing the Ph.D. degree. His research interests include signal and image processing, neural technologies for image processing, and optical technologies.
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"Information Theoretic Learning" by Jose C. Principe, sponsored by the IEEE Computational Intelligence Society This course examines Information Theory and our efforts to develop an information-theoretic criterion which can be utilized in adaptive filtering and neurocomputing. The main aim of our research is to develop new signal processing techniques by going beyond the basic assumptions of Linearity, Gaussianity and Stationarity. By capturing higher order statistics of data using Information Theory, we solve a variety of problems in Biomedical Signal Processing, Communications and Machine Learning. In the context of Information Theory, “information” is a precise, fully characterized mathematical quantity. The core process toward learning from examples in both biological and artificial systems is extracting information directly from data. The concept of learning from examples begins with a data set which globally conveys information about a real-world event, with the goal of capturing the information in the parameters of a learning machine. The information exists in a “distributed” mode in the data set, and, after successful training, is “condensed” in the parameters of the learning machine. Here, we develop information-theoretic criteria which can train directly from samples of linear or nonlinear mappers, either for entropy or mutual information maximization or minimization. After completing this course you should be able to develop an understanding of:
Jose C. Principe is Distinguished Professor of Electrical and Biomedical Engineering at the University of Florida, Gainesville, where he teaches advanced signal processing and artificial neural networks (ANNs) modeling. He is BellSouth Professor and Founder and Director of the University of Florida Computational NeuroEngineering Laboratory (CNEL).
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“Introduction to Type-2 Fuzzy Sets and Systems” by Jerry Mendel, sponsored by the IEEE Computational Intelligence Society This course will provide an introduction to, and an overview of, type-2 fuzzy sets (T2 FSs) and systems. It will locate type-2 fuzzy sets and systems in an educational taxonomy, so that the learner will appreciate from the onset the importance of studying such fuzzy sets; explain what a T2 FS is, how it is different from a type-1 FS, and why it is needed. The course will also provide careful definitions and pictures of the new terminology of T2 FSs and explain the importance of interval type-2 fuzzy sets over more general T2 FSs. The course will also explain important representations for a T2 FS (one is very good for computing, and another is very good for quickly developing the structure of the solution to a new theoretical problem) After completing you should be able to develop an understanding of:
Jerry M. Mendel received the Ph.D. degree in electrical engineering from the Polytechnic Institute of Brooklyn, Brooklyn, NY. Currently he is Professor of Electrical Engineering and Systems Architecting Engineering at the University of Southern California in Los Angeles, where he has been since 1974. He has published over 470 technical papers and is author and/or editor of eight books.
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day access)
Coming 4Q 2008: “Methods & Models of Collaborative Computational Intelligence” by Witold Pedryca sponsored by the IEEE Computational Intelligence Society and the IEEE Systems, Man and Cybernetics Society There are
rapidly emerging needs to deal with distributed
sources of data (sensors and sensor networks,
web sites, databases). While recognizing their
limited accessibility at a global level (associated
with technical constraints and/or privacy
issues) and fully acknowledging benefits of
collaborative processing, we propose a concept of
Collaborative Computational Intelligence (CI),
and collaborative fuzzy models, in particular.
The variety of possible mechanisms of interaction is
organized into a setting of the C3
interaction paradigm (communication –
collaboration – consensus). This helps
us offer a coherent taxonomy of various schemes of
interaction which in the sequel implies a
certain development of a suite of algorithms.
In this setting, the role granular information in
the establishing of the mechanisms of
interaction plays a pivotal role. After completing you should be able to develop an understanding of:
His main research interests encompass fundamentals of Computational Intelligence, Granular Computing, fuzzy modeling, knowledge discovery and data mining, fuzzy control including fuzzy controllers, pattern recognition, knowledge-based neural networks, relational computing, and Software Engineering. He has published vigorously in these areas. He is an author of 11 research monographs and over 250 journal papers published in highly reputable journals. His research is highly cited and he is also on the list Highly cited researcher on ISI HighlyCited.com. Witold Pedrycz has been a member of numerous program committees of IEEE conferences in the area of Computational Intelligence, Granular Computing, fuzzy sets and neurocomputing. He was a Program Chair of the 2007 Int. Conf on Machine Learning and Cybernetics, August 19-22, 2007, Hong Kong. He was also a General Chair of NAFIPS 2004, June 24-26, 2004, Banff, Alberta- a flagship conference of the NAFIPS Society. He currently serves as Editor-in-Chief of IEEE Transactions on Systems Man and Cybernetics-part A. He is an Associate Editor of Transactions on Fuzzy Systems. He is also on editorial boards of over 10 international journals. Dr Pedrycz is also an Editor-in-Chief of Information Sciences. NEXT Dr. Pedrycz is the past president of IFSA and the past president of NAFIPS. Dr. Pedrycz is a recipient of the prestigious Norbert Wiener Award which is one of the two highest awards of the IEEE Systems, Man, and Cybernetics Society. He is also a recipient of the K.S. Fu from NAFIPS. Coming 4Q 2008: "Tendency Assessment and Cluster Validity" by James C. Bezdek sponsored by the IEEE Computational Intelligence Society from the IEEE International Joint Conference on Neural Networks This course - the second in a series of three - discusses several approaches to the first and third problems of clustering identified in module I - viz., pre-clustering tendency assessment and post-clustering cluster validation. The target audience comprises advanced undergraduate and graduate students majoring in engineering and science, and practicing engineers and scientists interested in either research about or applications of clustering to real world problems such as data mining, image analysis and bioinformatics. Some of subject matter in this course is available in textbooks (most notably some of the material about cluster validity functionals), and some of the subject matter is the object of (my) current research. The references contain pointers to some excellent papers on these topics, and on a number of related or competitive methods that have been proposed and studied by others. I begin with a simple numerical example that establishes the necessity for both assessment and validity. Then, I discuss the visual assessment of tendency family of algorithms (VAT, sVAT and coVAT). These algorithms produce images that enable a user to make useful guesses about the number of clusters to seek in relational data before proceeding with a partitioning method for finding the clusters. Since object data can always be converted to relational form by computing pair wise distances, these methods are well defined for all types of unlabeled numerical data. The coVAT algorithm provides a means for estimating the number of clusters in each of the four problems associated with rectangular relational data: row clusters, column clusters, joint (pure) clusters, and mixed co-clusters. The second half of this course presents some examples of cluster validation using scalar measures or indices of cluster validity. Several examples from each of the three major categories (crisp, fuzzy and probabilistic) of indices are presented. This course concludes with a numerical example that compares 23 indices of all three types on clusters in 12 sets of data drawn from mixtures of Gaussian distributions having either 3 or 6 components. (SOME) indices of all three types do pretty well in this example, while others do very badly. I don't think this problem has a general "solution", but since we use clustering in many, many applications, we keep trying to find good indices to validate algorithmic outputs. After completing this course you should be able to develop an understanding of:
Jim received the BS in Civil Engineering from U. of Nevada (Reno, 1969); and the PhD in Applied Mathematics Cornell University, 1973. Jim's interests include woodworking, optimization, motorcycles, pattern recognition, fine cigars, fishing, image processing, computational neural networks, blues music, and clustering in very large data sets. Jim is past president of NAFIPS, IFSA and the IEEE NNC (aka CIS), is the founding editor of the Int'l. Jo. Approximate Reasoning and the IEEE Transactions on Fuzzy Systems, is a fellow of the IEEE and IFSA, and is a recipient of the IEEE 3rd Millenium , IEEE Fuzzy Systems Pioneer and IEEE Frank Rosenblatt medals.
Coming 4Q 2008: “Type-2 Fuzzy Logic Controllers: Towards a New Approach for Handling Uncertainties in Real World Environments” by Hani Hagras, sponsored by the IEEE Computational Intelligence Society This course will have a large impact on a large audience as handling uncertainties will be a very important challenge to any real world application that operate in real world changing and dynamic environments. The course will present the theoretical aspects of type-2 FLCs and how to build a type-2 FLC. The course will also present many applications in different areas ranging from Control of Marine Diesel Engines, Autonomous Outdoor mobile Robots as well as Embedded Agents and Ambient Intelligent Environments which deals with how we can embed very efficient computational intelligence and type-2 techniques in small computing and memory platforms. After completing you should be able to develop an understanding of:
Prof. Hani Hagras is a Professor of Computer Science, Director of the Centre for Computational Intelligence and leader of the Fuzzy Systems Research Group in the University of Essex, UK.
“Biometrics: Solutions for Security and Authentication” by Kostas Plataniotis, sponsored by the IEEE Educational Activities Board This course will provide an overview of the study of methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits. I will present the fundamentals of biometrics and biometric systems. The course will delve into why biometrics is a solution for security and authentication. Face, gait and ECG based biometrics will be covered. Biometrics and encryption will also be discussed, and the course will conclude with a discussion of future steps. After completing you should be able to develop an understanding of:
Konstantinos N. (Kostas) Plataniotis received his B. Eng. degree in Computer Engineering & Informatics from University of Patras, Greece in 1988 and his M.S. and Ph.D. degrees in Electrical Engineering from Florida Institute of Technology (Florida Tech), Melbourne, Florida, in 1992 and 1994 respectively. He was an Assistant Professor with the Computer Science Department at Ryerson University, Ontario, Canada from July 1997 to June 1999. Dr. Plataniotis is currently an Associate Professor with The Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto in Toronto, Ontario, Canada. He is also an Adjunct Professor with the School of Computer Science at Ryerson University, Toronto, Ontario, Canada.
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Coming 4Q 2008: “Cellular Wave Computers--Via Million Processing” by Tamas Roska, sponsored by the IEEE Circuits and Systems Society The cellular wave computer architecture, based on the CNN universal machine principle, has been implemented recently in many different physical forms. The mixed mode CMOS, the emulated digital (cell wise or as aggregated arrays), FPGA, DSP, as well as optical implementations are the main examples. In many cases, the sensory array is integrated as well. This course will begin with an introduction which will provide a historical overview, mind inspired and brain inspired computing models, the role of spatial address of a processor, new directions and products in computing The technology scenario. After completing you should be able to develop an understanding of:
Dr. Roska is a co-inventor of the CNN Universal Machine (with Leon O. Chua) and the analogic CNN Bionic Eye (with Frank S. Werblin and Leon O.Chua), US patents of UC Berkeley. During the last 15 years he has received two NSF grants, four ONR grants, two EU Grants and several Hungarian Grants. He has been a founding member of two spin/off companies, one in Berkeley and one in Budapest.
"Challenges Near the Limit of CMOS Scaling" by Yuan Taur, sponsored by the IEEE Electron Devices Society Beginning with a brief review of CMOS scaling trends, this course examines the fundamental factors that will ultimately limit CMOS scaling and considers the design issues near the limit of scaling. The fundamental limiting factors are electron thermal energy, tunneling leakage through gate oxide, and 2D electrostatic scale length. To extend CMOS scaling to the shortest channel length possible while still gaining significant performance benefit, an optimized, vertically and laterally nonuniform doping design (superhalo) is presented. It is projected that room-temperature CMOS will be scaled to 20-nm channel length with the superhalo profile. Low-temperature CMOS allows additional design space to further extend CMOS scaling to near 10 nm. After completing this course you should be able to develop an understanding of:
Yuan Taur is a professor in the Department of Electrical and Computer Engineering, University of California, San Diego.
IEEE
Member Individual Purchase ($69.95--30 day access) Coming 4Q 2008: “Dealing with Issues in VLSI Interconnect Scaling” by Ron Ho, sponsored by the IEEE Solid-State Circuits Society Designers have recognized for many years that on-chip wires can limit system performance, and as technologies continue to scale, the problems posed by on-chip wires continue to worsen. This course discusses on-chip wires, how to model them, what their problems (and their advantages) are and some solutions. After completing you should be able to develop an understanding of:
Ron Ho is a Senior Research Scientist at Sun Microsystems Laboratories in Menlo Park, CA, where he worries about the future of wires. He received his Ph.D. in electrical engineering from Stanford University. From 1993 to 2003, he was at Intel in Santa Clara, CA, where he worked on processors ranging from the 80486 to the 3rd-generation Itanium. In 2003, he joined Sun Labs, where he is currently researching high-performance and low-energy communication technologies, both on a single chip and between two chips. In 2005, he was also a Lecturer at Stanford University, where he taught a graduate class on circuit design.
"Design of Active-RC Filters for the Analog Front End (of Integrated Circuit System Chips)" by George S. Moschytz, sponsored by the IEEE Circuits & Systems Society The concept of the 'Analog Front End' (AFE), which is the interface to the real world in most IC-system chips, is first introduced. The course then focuses on active-RC filters which constitute an essential part of most AFEs. The formulation of filter specifications and the basic ideas associated with classical filter approximation theory are then briefly reviewed. Some key points of classical network theory, as needed for the understanding of inductorless filter design, are then briefly recalled. This is followed by some basic concepts of signal-flow graph theory, which permit the transition from transfer function (resulting from approximation theory) to circuit topology. After the review of these introductory and basic concepts, the stage is set to consider some of the most important and established active-RC filter-design techniques. Examples are given for the conversion of classical LCR filter structures into inductorless active-RC filter circuits that are amenable to IC chip design. The examples are taken from typical modern communication systems. Finally, filters designed using the design techniques covered in the course are compared in terms of practical performance criteria such as thermal output noise, sensitivity to component tolerances, and tunablity. After completing you should be able to develop an understanding of:
George Moschytz is head of the School of Engineering at Bar-Ilan University, Israel.
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Coming 4Q 2008: "Design of Active-RC Filters for the Analog Front End (of Integrated Circuit System Chips) Part 2" by George S. Moschytz, sponsored by the IEEE Circuits & Systems Society In this course, we shall introduce analytical tools, which are useful for the formulation of criteria with which we can decide which inductorless active-RC filters of the kind presented in the first course (or of other similar kinds) are most suitable for the front end of mixed-mode integrated-circuit system chips. These tools will enable us to select and characterize optimum filter realizations for the analog front end. With the filters selected according to these criteria, we shall then go through the detailed steps necessary for their design. They represent typical, well-proven circuits for the IC design of active RC filters. We shall check the performance of these designed filters by computer simulation (e.g. PSpice). We shall then look at some special situations in communications systems that require (i), novel topologies in filter design and (ii), special attention in integrated-circuit design for higher frequency applications and higher bit-rate communications. At the end of the course, the student should be well prepared to embark on the design and performance evaluation of inductorless filters for the front end of typical and emerging communication systems on a chip. After completing you should be able to develop an understanding of:
George Moschytz is head of the School of Engineering at Bar-Ilan University, Israel.
Coming 4Q 2008: “Design of Phase Locked Loops” by Lama Dayaratna, sponsored by the IEEE Microwave Theory and Techniques Society The objective of this course is to provide a state of the art review of phase locked loop circuits and applications from a design and development perspective. Intended for RF and Microwave Engineers, the course details out the design and development of phase locked loop circuits. Topics include PLL basics, VCOs, phase detectors, open and close loop characterization, loop filter design, and phase noise concepts. Examples will be given to a variety of problems relevant to the design of phase locked loops. After completing you should be able to develop an understanding of:
Dr. Dayaratna holds a Ph.D. and has over 20-years of extensive experience in the theory and design of phase locked loop circuits with emphasis on low noise frequency synthesis techniques. Analyzed, designed, developed, and engineered Frequency Generation Architectures for communications payloads. Conceptualized, designed, built and led the first frequency generation architecture for LMCSS’ first mobile payload, which served as the cornerstone of LMCSS’s all subsequent Frequency Generation Units. As a Principal Engineer in the RF/Microwave Products area, Dr. Dayaratna is responsible for the design, development of RF/Microwave payload components such as receivers, transmitters, modulators, demodulators, synthesizers, and frequency generation equipment for all commercial satellite programs. Dr. Dayaratna also has over five years of teaching experience at graduate and undergraduate level.
"Effects of Reliability Mechanisms on VLSI Circuit Functionality" by Wayne Ellis, co-sponsored by the IEEE Electron Devices Society and IEEE Reliability Society This course provides examples of reliability mechanisms and how these can affect the normal operation of selected VLSI circuits. Large circuit-count ASIC chips use standard digital and analog circuits such as Logic gates, eSRAM, eDRAM and I/O circuits which must function properly under various voltage and thermal environments. These chips are subjected to Reliability Screens such as Burn In to activate latent defects and screen out those chips that cannot meet product specifications for performance, power and operating margins. The advent of degraded VLSI circuit operating margins due to the activated defects as well as reliability mechanisms such as negative bias temperature instability (NBTI), hot carrier injection (HCI), and others will be discussed. How these failing circuits can then manifest themselves in observed product failures will also be discussed. After completing this course you should be able to develop an understanding of:
Wayne Ellis has worked from 1977 to the present at the IBM Microelectronics division labs in Essex Junction Vermont.
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Coming 4Q 2008: “Interconnect Technology for 32 NM and Beyond” by Jeff Gambino, sponsored by the IEEE Electron Devices Society This course will provide an overview of advanced interconnect technologies, including dielectric materials, patterning, metallization, CMP, and packaging. New processes will be discussed, such as ultra-low K dielectrics, air-gap structures, low-damage patterning methods, thin barrier and seed layers, refractory metal capping layers, and novel CMP techniques. The effect of these processes on performance and reliability will be briefly described. After completing you should be able to develop an understanding of:
Jeff Gambino received the B.S. degree in materials science from Cornell University, Ithaca, NY, in 1979, and the Ph.D. degree in materials science from the Massachusetts Institute of Technology, Cambridge, MA, in 1984. He joined IBM, Hopewell Junction, NY, in 1984, where he worked on silicide processes for Bipolar and CMOS devices. In 1992, he joined the DRAM development alliance at IBM’s Advanced Semiconductor Technology Center, Hopewell Junction, NY. In 1999, he joined IBM’s manufacturing organization in Essex Junction, VT, where he has worked on copper interconnect processes for CMOS logic and CMOS imager technology. He has published over 90 technical papers and holds over 100 patents.
"Introduction to Developing Embedded Systems" by Kim Fowler, sponsored by the IEEE Instrumentation & Measurement Society This course introduces important issues in preparing, designing, and developing a product covering areas such as:
After completing this course you should be able to develop an understanding of:
Kim Fowler has spent over 22 years in the design, development, and project management of medical, military, and satellite equipment. He developed many different kinds of embedded systems at The Johns Hopkins University Applied Physics Laboratory; he currently manages technical programs there.
IEEE
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"Introduction to Statistical Variation and Techniques for Design Optimization" by Norman Rohrer sponsored by the IEEE Solid State Circuits Society Variability is a reality in nanometer semiconductor processes. This course will cover the sources of systematic and random variations of transistors and their surrounding interconnects. Included in the variability discussion will be withinchip variability, across-wafer variability, across-device variability, and device mismatch. The resulting impact upon an individual circuit’s functionality and timing will be explored. Analytical approaches will be shown for examining the variability’s impact upon leakage power, dynamic power, and circuit functionality of static and dynamic circuits, SRAM arrays, and PLLs. Techniques will include Monte-Carlo analysis, vector analysis, and statistical timing analysis. After completing this course you should be able to develop an understanding of:
Norman Rohrer is a Distinguished Engineer in the Power PC Microprocessor Group within the System-and-Technology Group of IBM, located in Essex Junction, VT.
IEEE
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"Molecular Electronics Part 1: Potential and Applications" by Curt Richter, sponsored by the IEEE Electron Devices Society and the IEEE Reliability Society This course will begin by outlining approaching limits of conventional CMOS technology. New architectural requirements and paradigms for future nanoelectronics will be described. ‘Top-down’ and ‘bottom-up’ manufacturing paradigms, particularly self-assembly of organic monolayers will be discussed. Theoretical and experimental realizations of molecular-scale electronic switches will be described. This course will also show nanoscale memory and logic circuits built with these materials and methods and will discuss potential nanoscale chemical and biological sensors built with these materials and methods After completing this course you should be able to develop an understanding of:
Curt A. Richter, Ph.D. has worked in the Semiconductor Electronics Division of the National Institute of Standards and Technology, Gaithersburg, MD since 1993. He is currently Project Leader of the Nanoelectronic Device Metrology Project.
IEEE
Member Individual Purchase ($69.95--30 day access)
"Molecular Electronics Part 2: Molecular Elect |