| Citation: |
Tian Zhixin, Liu Yongpan, Yang Huazhong. Combined Novel Gate Level Model and Critical Primary Input Sharing for Genetic Algorithm Based Maximum Power Supply Noise Estimation[J]. Journal of Semiconductors, 2007, In Press.
Tian Z X, Liu Y P, Yang H Z. Combined Novel Gate Level Model and Critical Primary Input Sharing for Genetic Algorithm Based Maximum Power Supply Noise Estimation[J]. Chin. J. Semicond., 2007, 28(9): 1375.
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Combined Novel Gate Level Model and Critical Primary Input Sharing for Genetic Algorithm Based Maximum Power Supply Noise Estimation
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Abstract
A gate level maximum power supply noise (PSN) model is defined that captures both IR drop and di/dt noise effects.Experimental results show that this model improves PSN estimation by 5.3% on average and reduces computation time by 10.7% compared with previous methods.Furthermore,a primary input critical factor model that captures the extent of primary inputs’ PSN contribution is formulated.Based on these models,a novel niche genetic algorithm is proposed to estimate PSN more effectively.Compared with general genetic algorithms,this novel method can achieve up to 19.0% improvement on PSN estimation with a much higher convergence speed. -
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