Title :
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
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;
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
DOI :
10.1109/ISCAS.1996.542022
