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Analog Circuits and Systems Optimization based on Evolutionary Computation Techniques

Authors: Manuel F. M. Barros, Jorge M. C. Guilherme, Nuno C. G. Horta

Publisher: Springer Berlin Heidelberg

Book Series : Studies in Computational Intelligence

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

The microelectronics market, with special emphasis to the production of complex mixed-signal systems-on-chip (SoC), is driven by three main dynamics, time-- market, productivity and managing complexity. Pushed by the progress in na- meter technology, the design teams are facing a curve of complexity that grows exponentially, thereby slowing down the productivity design rate. Analog design automation tools are not developing at the same pace of technology, once custom design, characterized by decisions taken at each step of the analog design flow, - lies most of the time on designer knowledge and expertise. Actually, the use of - sign management platforms, like the Cadences Virtuoso platform, with a set of - tegrated CAD tools and database facilities to deal with the design transformations from the system level to the physical implementation, can significantly speed-up the design process and enhance the productivity of analog/mixed-signal integrated circuit (IC) design teams. These design management platforms are a valuable help in analog IC design but they are still far behind the development stage of design automation tools already available for digital design. Therefore, the development of new CAD tools and design methodologies for analog and mixed-signal ICs is ess- tial to increase the designer’s productivity and reduce design productivitygap. The work presented in this book describes a new design automation approach to the problem of sizing analog ICs.

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Table of Contents

Frontmatter

Introduction

Abstract

This chapter presents the motivation to the research work in the area of analog integrated circuit (IC) design automation, i.e., outlines the market and technological evolution, characterizes the analog IC design, discusses the available CAD solutions and, finally, describes goals for the this work.

Manuel F. M. Barros, Jorge M. C. Guilherme, Nuno C. G. Horta

State-of-the-Art on Analog Design Automation

Abstract

This chapter presents the State-of-the-Art (SOA) in analog circuit design automation. First, the analog design flow is reviewed and the fundamental trends in design automation are discussed. Then, the existing approaches to circuit sizing are presented, outlining in each case their advantages and limitations. Next, a detailed discussion over the existing tools approaches is provided. Finally, conclusions concerning the specification and design of a new analog design automation methodology implementation will be drawn.

Manuel F. M. Barros, Jorge M. C. Guilherme, Nuno C. G. Horta

Evolutionary Analog IC Design Optimization

Abstract

This chapter starts with an overview on computation techniques aiming to solve nonlinear optimization problems with emphasis on evolutionary optimization algorithms and discusses their relevance to analog design problem. The main virtues and weaknesses, as well as, the design issues of evolutionary algorithms are discussed with a description of the recent developments in this field. This chapter also introduces a new optimization kernel based on genetic algorithms applied to analog circuit optimization. It includes a detailed description of the coding schemes, the fitness function, the genetic operators and other design strategy criteria. Finally, a robust IC design methodology supported by the optimization kernel is presented in the end of the chapter.

Manuel F. M. Barros, Jorge M. C. Guilherme, Nuno C. G. Horta

Enhanced Techniques for Analog Circuits Design Using SVM Models

Abstract

In order to improve the relatively slow convergence of GA, in the presence of large search spaces, and reduce the high consuming time of evaluation functions in analog circuit design applications, this chapter will discuss the use of learning algorithms. These algorithms explore the successive generation of solutions, learn the tendency of the best optimization variables and will use this knowledge to predict future values. In other words, these techniques employ data mining theory, used to manage large databases and huge amount of internet information, to discover complex relationships among various factors and extract meaningful knowledge to improve the efficiency and quality of decision making. In this chapter a new hybrid optimization algorithm is presented together with a design methodology, which increases the efficiency on the analog circuit design cycle. This new algorithm combines an enhanced GA kernel with an automatic learning machine based on SVM model (GA-SVM) which efficiently guides the selection operator of the GA algorithm avoiding time-consuming SPICE evaluations of non-promising solutions. The SVM model is here defined as a classification model used to predict the feasibility region in the presence of large, non-linear and constraints search spaces that characterize analog design problems. The SVM modeling attempts to constraint the search space in order to accelerate the search towards the feasible region ensuring a proper operation of the circuit.

Manuel F. M. Barros, Jorge M. C. Guilherme, Nuno C. G. Horta

Analog IC Design Environment Architecture

Abstract

This chapter describes the implementation of an innovative design automation tool, GENOM which explores the potentials of evolutionary computation techniques and state-of-the-art modeling techniques presented in the previous chapters. The main design options of the proposed approach will be here described and justified. First, an overview of the design architecture main building blocks will be provided. Then, the optimization algorithm kernel, as well as, the implemented functionalities are described. Finally, the design options are described in detail using experimental results on a few test cases.

Manuel F. M. Barros, Jorge M. C. Guilherme, Nuno C. G. Horta

Optimization of Analog Circuits and Systems - Applications

Abstract

In the previous chapters there was a description of the optimization methodology and the supporting tool that simplifies the design tasks of analog integrated circuits. The developed design optimization tool, GENOM, based on evolutionary computation techniques and incorporating heuristic knowledge on the automatic control mechanism was combined efficiently with a learning strategy based on SVM to improve the convergence speed of the optimization algorithm. This chapter demonstrates the capabilities and performances of the implemented design optimization methodology when applied to several analog synthesis experiments and provides some insight into factors that affect the synthesis process. Several state of the art circuit blocks will be introduced and optimized for performance and efficiency. Particularly, the performance and effectiveness of GENOM optimizer will be compared with one important reference tool.

Manuel F. M. Barros, Jorge M. C. Guilherme, Nuno C. G. Horta

Conclusions

Abstract

This last chapter presents the work conclusions and discusses the future work issues.

Manuel F. M. Barros, Jorge M. C. Guilherme, Nuno C. G. Horta

Backmatter

Title: Analog Circuits and Systems Optimization based on Evolutionary Computation Techniques
Authors: Manuel F. M. Barros
Jorge M. C. Guilherme
Nuno C. G. Horta
Copyright Year: 2010
Publisher: Springer Berlin Heidelberg
Electronic ISBN: 978-3-642-12346-7
Print ISBN: 978-3-642-12345-0
DOI: https://doi.org/10.1007/978-3-642-12346-7