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Timing yield estimation of digital circuits using a control variate technique

机译:使用控制变化技术时序产量估计数字电路

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The Monte-Carlo (MC) technique is a traditional solution for reliable yield analysis, and in contrast to probabilistic methods it can account for any complicated timing and process variation models. However, a precise analysis that involves a traditional MC-based technique requires many simulation iterations, especially for the extreme quantile values. In this paper, a new yield estimator is developed for the timing yield of digital circuits based on an auxiliary (control) variable which is formed by extracting the delay equation of the nominally critical path. The superiority of the proposed technique is studied and verified against the crude-MC and the advanced sampling techniques, Latin Hypercube Sampling and Quasi-MC. The technique shows significant (2X to 30X) runtime improvement over the crude-MC by reducing the required number of iterations to achieve a maximum confidence interval.
机译:Monte-Carlo(MC)技术是一种用于可靠收益率分析的传统解决方案,与概率方法相比,它可以考虑任何复杂的定时和过程变化模型。然而,涉及传统的基于MC技术的精确分析需要许多仿真迭代,特别是对于极端定量位值。在本文中,基于辅助(控制)变量的数字电路的定时产量开发了一种新的产量估计器,该变量是通过提取标称关键路径的延迟方程而形成的。研究并验证了提出的技术的优越性,并针对粗磨MC和先进的采样技术,拉丁超立体采样和准MC。通过减少所需的迭代次数以实现最大置信区间,该技术通过粗糙-cm显示出显着的(2倍至30倍)运行时改进。

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