Combined Novel Gate Level Model and Critical Primary Input Sharing for Genetic Algorithm Based Maximum Power Supply Noise Estimation

基于最大电源噪声门级模型的遗传算法电源噪声估计(英文)

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.

提出一种能够综合考虑IRdrop和di/dt噪声的门级电路模型.实验表明,利用这种模型进行电源噪声估计,可以比传统模型提高5.3 %的精度,同时运算时间降低10.7 %.根据输入信号对最大电源噪声的影响,还提出了关键输入信号模型.实验表明,在进行电源噪声估计中,基于这些模型的遗传算法,能够比传统的遗传算法提高最多19.0 %的精度,并且收敛更加迅速.