Authors: M. Murakawa, Y. Oda, H. Amakawa, S. Baba, T. Higuchi and K. Nishi
Affilation: AIST, Japan
Pages: 60 - 63
Keywords: model calibration, genetic algorithm, parallel computation, B implantation, dual-Pearson profile
In this paper, we show, for the first time, GA application to process model calibration. We propose a distributed GA based calibration technique combined with the traditional local optimization algorithm, which reduces time for calibration considerably. Experimental results show calibration of 144 parameters can be completed with a few minutes, whereas it typically takes a human expert a few days. Our algorithm can be easily implemented on a coarse-grain parallel computer such as a PC cluster system or a multi processor workstation. GA, thus, can be a practical and robust tool for process/device calibration.
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