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Evolution of a LowDistortion, LowBias 60 Decibel Op Amp with Good Frequency Generalization using Genetic
 Programming, , presented at the International Conference on Evolvable System: From Biology to Hardware (ICES96
, 1996
"... Genetic programming was used to evolve both the topology and the sizing (numerical values) for each component of a lowdistortion, lowbias 60 decibel (1000to1) amplifier circuit with good frequency generalization. The evolved circuit was composed of two types of transistors (active elements) as we ..."
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Cited by 4 (1 self)
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Genetic programming was used to evolve both the topology and the sizing (numerical values) for each component of a lowdistortion, lowbias 60 decibel (1000to1) amplifier circuit with good frequency generalization. The evolved circuit was composed of two types of transistors (active elements) as well as resistors and capacitors. 1.
Design of a 96 Decibel Operational Amplifier and Other Problems for Which a Computer Program Evolved by Genetic Programming is Competitive with
 Human Performance, presented at the 1996 JapanChina Joint International Workshop on Information Systems at the Ashikaga: Ashikaga Institute of Technology
"... It would be desirable if computers could solve problems without the need for a human to write the detailed programmatic steps. That is, it would be desirable to have a domainindependent automatic programming technique in which "What You Want Is What You Get " ("WYWIWYG " – pron ..."
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Cited by 3 (1 self)
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It would be desirable if computers could solve problems without the need for a human to write the detailed programmatic steps. That is, it would be desirable to have a domainindependent automatic programming technique in which "What You Want Is What You Get " ("WYWIWYG " – pronounced "woweeewig"). Genetic programming is such a technique. This paper surveys three recent examples of problems (one from the field of cellular automata and two from the fields of molecular biology) in which genetic programming evolved a computer program that produced results that were slightly better than human performance for the same problem. This paper then discusses a fourth problem in greater detail and demonstrates that a design for a lowdistortion 96 decibel op amp (including both topology and component sizing) can be evolved using genetic programming. The information that the user must supply to genetic programming consists of the parts bin (transistors, resistors, and capacitors) and the fitness measure for the major operating characteristics of an op amp. 1.
Automatic synthesis of electrical circuits containing a free variable using genetic programming
 Proceedings of the Genetic and Evolutionary Computation Conference, 477–484. Las Vegas
, 2000
"... A mathematical formula containing one or more free variables is "general " in the sense that it represents the solution to all instances of a problem (instead of just the solution of a single instance of the problem). For example, the familiar formula for solving a quadratic equation conta ..."
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A mathematical formula containing one or more free variables is "general " in the sense that it represents the solution to all instances of a problem (instead of just the solution of a single instance of the problem). For example, the familiar formula for solving a quadratic equation contains free variables representing the coefficients of the tobesolved equation. This paper demonstrates, using an illustrative problem, that genetic programming can automatically create the design for both the topology and component values for an analog electrical circuit in which the value of each component in the evolved circuit is specified by a mathematical expression containing a free variable. That is, genetic programming is used to evolve a general parameterized circuit that satisfies the problem's highlevel requirements. The evolved circuit has been crossvalidated on unseen values of the free variable. 1
Optimal RF design using smart evolutionary algorithms
 In Proc. Design Automation Conference, 2000
, 2000
"... This paper presents an optimization algorithm that is able to significantly increase the speed of RF circuit optimizations. The algorithm consists of a series of consecutive evolutionary optimizations of the circuit itself and of a modeled version thereof. The speed increase arises from the differen ..."
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Cited by 2 (1 self)
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This paper presents an optimization algorithm that is able to significantly increase the speed of RF circuit optimizations. The algorithm consists of a series of consecutive evolutionary optimizations of the circuit itself and of a modeled version thereof. The speed increase arises from the difference in evaluation time between the real simulation and the fit evaluation. As circuit approximation, behavioral models are used instead of polynomial expressions, allowing to put some “design knowledge ” into the optimization. gaRFeeld is a tool implementing this smart evolutionary algorithm for RF circuits. Finally some experiments performed with gaRFeeld are illustrated for the optimization of a Low Noise Amplifier. 1
Automatic Synthesis of the Topology and Sizing for Analog Electrical Circuits using Genetic Programming
, 1999
"... Design is a major activity of practicing engineers. The design process entails creation of a complex structure to satisfy userdefined requirements. Since the design process typically entails tradeoffs between competing considerations, the end product of ..."
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Design is a major activity of practicing engineers. The design process entails creation of a complex structure to satisfy userdefined requirements. Since the design process typically entails tradeoffs between competing considerations, the end product of
Application of the Univariate Marginal Distribution Algorithm to Analog Circuit Design
"... The approach to computer aided analog circuit design on the base of univariate algorithms was derived by the analysing the mathematical principles behind recombination. A Bayesian prior using for the estimations of the probability distribution is equivalent to having mutation for the genetic algorit ..."
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The approach to computer aided analog circuit design on the base of univariate algorithms was derived by the analysing the mathematical principles behind recombination. A Bayesian prior using for the estimations of the probability distribution is equivalent to having mutation for the genetic algorithms. In this paper the relation between a success rate and a mutation one is considered for analog circuit design. Practical illustration of the use of this approach is demonstrated for filter design. Experiments indicate that mutation and elitism increase the performance of the algorithms and decrease the dependence of the correct choice of the population size. 1.
Optimal RF design using Smart Evolutionary Algorithms
 In Proc. Design Automation Conference, 2000
, 2000
"... This paper presents an optimization algorithm that is able to significantly increase the speed of RF circuit optimizations. The algorithm consists of a series of consecutive evolutionary optimizations of the circuit itself and of a modeled version thereof. The speed increase arises from the differen ..."
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Cited by 1 (0 self)
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This paper presents an optimization algorithm that is able to significantly increase the speed of RF circuit optimizations. The algorithm consists of a series of consecutive evolutionary optimizations of the circuit itself and of a modeled version thereof. The speed increase arises from the difference in evaluation time between the real simulation and the fit evaluation. As circuit approximation, behavioral models are used instead of polynomial expressions, allowing to put some "design knowledge" into the optimization. gaRFeeld is a tool implementing this smart evolutionary algorithm for RF circuits. Finally some experiments performed with gaRFeeld are illustrated for the optimization of a Low Noise Amplifier.
Automation in MixedSignal Design: Challenges and Solutions in the Wake of the Nano Era
"... The use of CMOS nanometer technologies at 65 nm and below will pose serious challenges on the design of mixedsignal integrated systems in the very near future. Rising design complexities, tightening timetomarket constraints, leakage power, increasing technology tolerances, and reducing supply vol ..."
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The use of CMOS nanometer technologies at 65 nm and below will pose serious challenges on the design of mixedsignal integrated systems in the very near future. Rising design complexities, tightening timetomarket constraints, leakage power, increasing technology tolerances, and reducing supply voltages are key challenges that designers face. Novel types of devices, new process materials and new reliability issues are next on the horizon. We discuss new design methodologies and EDA tools that are being or need to be developed to address the problems of designing such mixedsignal integrated systems.
High Speed Performance and Yield Optimization Technique for Analog Integrated Circuits
"... Abstract — This paper presents a novel predictive modeling technique for yield and performance of analog integrated circuits. Tradeoffs between performance functions can be explored through the use of a multiobjective evolutionary algorithm and Monte Carlo simulations. When compared to conventiona ..."
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Abstract — This paper presents a novel predictive modeling technique for yield and performance of analog integrated circuits. Tradeoffs between performance functions can be explored through the use of a multiobjective evolutionary algorithm and Monte Carlo simulations. When compared to conventional simulation based approaches, the results show a significant improvement in overall simulation time and efficiency without a corresponding drop in accuracy. The behavioral model has been developed in VerilogA and tested extensively with practical designs using the Spectre simulator. Two OTA topologies are used to demonstrate the proposed algorithm and their behavior has been verified through transistor level simulations. The examples have demonstrated that accurate performance and yield prediction can be achieved using the proposed method in a fraction of the time taken by conventional simulation based methods. Keywords—Analog circuit design, Behavioral modeling, circuit synthesis, design automation, multiobjective optimization, yield optimization. 1