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This textbook is designed for graduate-level courses, and for self-study, in analog and sampled-data, including switched-capacitor, circuit theory and design for ongoing, or active electrical engineers, needing to become proficient in analog circuit design on a system, rather than on a device, level. After decades of experience in industry and teaching this material in academic settings, the author has extracted many of the most important and useful features of analog circuit theory and design and presented them in a manner that is easy to digest and utilize. The methodology and analysis techniques presented can be applied to areas well beyond those specifically addressed in this book. This book is meant to enable readers to gain a 'general knowledge' of one aspect of analog engineering (e.g., that of network theory, filter design, system theory and sampled-data signal processing). The presentation is self-contained and should be accessible to anyone with a first degree in electrical engineering.
Thisbook contains revised contributions byt he speakers oft he 1s t IEEE Workshop on Wireless Communication Circuits andSystems, held in Lucerne, Switzerland, from June 22-24, 1998. The aim oft he workshop was to apply the vast expertise oft he CAS Society in the area of circuit and systemdesign to therapidly growing field of wireless communications. Theworkshop combined presentations by invited experts from academia and industry with panel and informal discussions. The following topics were covered: RFSystemInteg ration (single chip systems, CM OS RF circuits), RFFront End Circuits (CMOS RF oscillators, broadband design techniques), Wideband Conversion for Software Radio (A/D conversion issues, wideband sub sampling, low spurious A/D conversion), Process Technologies for Future RF Systems (Si, SiGe, GaAs, CMOS, packaging technologies), DSPforWireless Communications (DSP algorithms, fixed point systems, DSP for baseband applications), Blind Channel Equalization (adaptive interference suppression, design techniques, channel estimation). The workshop was a great success, with over 130 participants from 19 countries, from the U. S. to Europe and Asia, including a large contingent of participants from industry (60% ). Feedback from the participants showed that the carefully selected combination oftutorial like lectures with lectures onspecialized and advancedtopics was a feature oft he workshop that was particularly a ppreciated. Duet o therelatively strong involvemento findustry - both in theform of lecturers and listeners - a high level ofd i scussion was attained in bothpanel sessions and informal gatherings.
Cellular Neural Networks (CNNs) constitute a class of nonlinear, recurrent and locally coupled arrays of identical dynamical cells that operate in parallel. ANALOG chips are being developed for use in applications where sophisticated signal processing at low power consumption is required. Signal processing via CNNs only becomes efficient if the network is implemented in analog hardware. In view of the physical limitations that analog implementations entail, robust operation of a CNN chip with respect to parameter variations has to be insured. By far not all mathematically possible CNN tasks can be carried out reliably on an analog chip; some of them are inherently too sensitive. This book defines a robustness measure to quantify the degree of robustness and proposes an exact and direct analytical design method for the synthesis of optimally robust network parameters. The method is based on a design centering technique which is generally applicable where linear constraints have to be satisfied in an optimum way. Processing speed is always crucial when discussing signal-processing devices. In the case of the CNN, it is shown that the setting time can be specified in closed analytical expressions, which permits, on the one hand, parameter optimization with respect to speed and, on the other hand, efficient numerical integration of CNNs. Interdependence between robustness and speed issues are also addressed. Another goal pursued is the unification of the theory of continuous-time and discrete-time systems. By means of a delta-operator approach, it is proven that the same network parameters can be used for both of these classes, even if their nonlinear output functions differ. More complex CNN optimization problems that cannot be solved analytically necessitate resorting to numerical methods. Among these, stochastic optimization techniques such as genetic algorithms prove their usefulness, for example in image classification problems. Since the inception of the CNN, the problem of finding the network parameters for a desired task has been regarded as a learning or training problem, and computationally expensive methods derived from standard neural networks have been applied. Furthermore, numerous useful parameter sets have been derived by intuition. In this book, a direct and exact analytical design method for the network parameters is presented. The approach yields solutions which are optimum with respect to robustness, an aspect which is crucial for successful implementation of the analog CNN hardware that has often been neglected. `This beautifully rounded work provides many interesting and useful results, for both CNN theorists and circuit designers.' Leon O. Chua
As the frequency of communication systems increases and the
dimensions of transistors are reduced, more and more stringent
performance requirements are placed on analog circuits. This is a
trend that is bound to continue for the foreseeable future and
while it does, understanding performance trade-offs will constitute
a vital part of the analog design process. It is the insight and
intuition obtained from a fundamental understanding of performance
conflicts and trade-offs, that ultimately provides the designer
with the basic tools necessary for effective and creative analog
design.
Cellular Neural Networks (CNNs) constitute a class of nonlinear, recurrent and locally coupled arrays of identical dynamical cells that operate in parallel. ANALOG chips are being developed for use in applications where sophisticated signal processing at low power consumption is required. Signal processing via CNNs only becomes efficient if the network is implemented in analog hardware. In view of the physical limitations that analog implementations entail, robust operation of a CNN chip with respect to parameter variations has to be insured. By far not all mathematically possible CNN tasks can be carried out reliably on an analog chip; some of them are inherently too sensitive. This book defines a robustness measure to quantify the degree of robustness and proposes an exact and direct analytical design method for the synthesis of optimally robust network parameters. The method is based on a design centering technique which is generally applicable where linear constraints have to be satisfied in an optimum way. Processing speed is always crucial when discussing signal-processing devices. In the case of the CNN, it is shown that the setting time can be specified in closed analytical expressions, which permits, on the one hand, parameter optimization with respect to speed and, on the other hand, efficient numerical integration of CNNs. Interdependence between robustness and speed issues are also addressed. Another goal pursued is the unification of the theory of continuous-time and discrete-time systems. By means of a delta-operator approach, it is proven that the same network parameters can be used for both of these classes, even if their nonlinear output functions differ. More complex CNN optimization problems that cannot be solved analytically necessitate resorting to numerical methods. Among these, stochastic optimization techniques such as genetic algorithms prove their usefulness, for example in image classification problems. Since the inception of the CNN, the problem of finding the network parameters for a desired task has been regarded as a learning or training problem, and computationally expensive methods derived from standard neural networks have been applied. Furthermore, numerous useful parameter sets have been derived by intuition. In this book, a direct and exact analytical design method for the network parameters is presented. The approach yields solutions which are optimum with respect to robustness, an aspect which is crucial for successful implementation of the analog CNN hardware that has often been neglected. This beautifully rounded work provides many interesting and useful results, for both CNN theorists and circuit designers.' Leon O. Chua
Thisbook contains revised contributions byt he speakers oft he 1s t IEEE Workshop on Wireless Communication Circuits andSystems, held in Lucerne, Switzerland, from June 22-24, 1998. The aim oft he workshop was to apply the vast expertise oft he CAS Society in the area of circuit and systemdesign to therapidly growing field of wireless communications. Theworkshop combined presentations by invited experts from academia and industry with panel and informal discussions. The following topics were covered: RFSystemInteg ration (single chip systems, CM OS RF circuits), RFFront End Circuits (CMOS RF oscillators, broadband design techniques), Wideband Conversion for Software Radio (A/D conversion issues, wideband sub sampling, low spurious A/D conversion), Process Technologies for Future RF Systems (Si, SiGe, GaAs, CMOS, packaging technologies), DSPforWireless Communications (DSP algorithms, fixed point systems, DSP for baseband applications), Blind Channel Equalization (adaptive interference suppression, design techniques, channel estimation). The workshop was a great success, with over 130 participants from 19 countries, from the U. S. to Europe and Asia, including a large contingent of participants from industry (60% ). Feedback from the participants showed that the carefully selected combination oftutorial like lectures with lectures onspecialized and advancedtopics was a feature oft he workshop that was particularly a ppreciated. Duet o therelatively strong involvemento findustry - both in theform of lecturers and listeners - a high level ofd i scussion was attained in bothpanel sessions and informal gatherings.
As the frequency of communication systems increases and the dimensions of transistors are reduced, more and more stringent performance requirements are placed on analog circuits. This is a trend that is bound to continue for the foreseeable future and while it does, understanding performance trade-offs will constitute a vital part of the analog design process. It is the insight and intuition obtained from a fundamental understanding of performance conflicts and trade-offs, that ultimately provides the designer with the basic tools necessary for effective and creative analog design. Trade-offs in Analog Circuit Design, which is devoted to the understanding of trade-offs in analog design, is quite unique in that it draws together fundamental material from, and identifies interrelationships within, a number of key analog circuits. The book covers ten subject areas: Design methodology, Technology, General Performance, Filters, Switched Circuits, Oscillators, Data Converters, Transceivers, Neural Processing, and Analog CAD. Within these subject areas it deals with a wide diversity of trade-offs ranging from frequency-dynamic range and power, gain-bandwidth, speed-dynamic range and phase noise, to tradeoffs in design for manufacture and IC layout. The book has by far transcended its original scope and has become both a designer's companion as well as a graduate textbook. An important feature of this book is that it promotes an intuitive approach to understanding analog circuits by explaining fundamental relationships and, in many cases, providing practical illustrative examples to demonstrate the inherent basic interrelationships and trade-offs. Trade-offs in Analog Circuit Designdraws together 34 contributions from some of the world's most eminent analog circuits-and-systems designers to provide, for the first time, a comprehensive text devoted to a very important and timely approach to analog circuit design.
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