A parallel optimal statistical design method based on genetic algorithm

Author

Wu, K.Y. ; Shen, Y. ; Chen, R.M.M. ; Wu, A.

Author_Institution

Dept. of Electron. Eng., City Univ. of Hong Kong, Hong Kong

Volume

4

fYear

1996

Firstpage

477

Abstract

Genetic Algorithms (GA), together with a boundary sampling strategy have been identified as a novel approach for optimal statistical design to achieve better performance and higher yield at a minimum cost. Due to the reduced number of circuit simulations, the proposed combination can provide a satisfactory model representation at improved computation speed for the selection of the response surface model function. In this paper, a number of possible approaches for parallelizing the GA operations is identified, and studied. The parallel GA was implemented on a parallel machine constructed from a cluster of networked workstations

Keywords

circuit CAD; circuit analysis computing; genetic algorithms; integrated circuit design; integrated circuit yield; parallel algorithms; statistical analysis; boundary sampling strategy; circuit simulation; computation speed; genetic algorithm; networked workstations; parallel algorithm; response surface model function; statistical design method; yield; Algorithm design and analysis; Circuit simulation; Design engineering; Design methodology; Genetic algorithms; Genetic engineering; Monte Carlo methods; Polynomials; Response surface methodology; Sampling methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1996. ISCAS '96., Connecting the World., 1996 IEEE International Symposium on

Conference_Location

Atlanta, GA

Print_ISBN

0-7803-3073-0

Type

conf

DOI

10.1109/ISCAS.1996.542022

Filename

542022