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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This last chapter presents the work conclusions and discusses the future work issues. [ABSTRACT FROM AUTHOR]