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Statistical leakage modeling for accurate yield analysis the CDF matching method and its alternatives

机译:统计泄漏建模,用于精确的产量分析CDF匹配方法及其替代方法

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We study the impact of statistical leakage modeling on the yield of memory designs. We critically evaluate different closed form models from a rare fail event perspective and propose CDF matching as a comprehensive and effective approach for accurate statistical leakage modeling. While Schwartz-Yeh method is found to match the body and left tail of the distribution, the Fenton-Wilkinson method aims more at matching the right tail of the distribution. The latter is more critical for purposes of yield estimation in the presence of leaky bitlines devices, as the right tail region is more crucial. However, for practical applications, it is shown that even Fenton-Wilkinson method leads to reduced accuracy compared to the CDF matching method. The error in estimating the probability of a false-read is shown to range from 10x–147x and is expected to increase with technology scaling.
机译:我们研究统计泄漏建模对内存设计产量的影响。我们从罕见的失败事件角度严格评估不同的闭合形式模型,并提出CDF匹配作为准确的统计泄漏建模的一种全面而有效的方法。尽管发现Schwartz-Yeh方法与分布的身体和左尾匹配,但Fenton-Wilkinson方法的目标更多是与分布的右尾匹配。在存在漏电位线器件的情况下,后者对于成品率估算而言更为关键,因为右尾部区域更为关键。但是,对于实际应用,结果表明,与CDF匹配方法相比,即使Fenton-Wilkinson方法也会导致精度降低。估计错误读取概率的误差范围为10x-147x,并且预计会随着技术规模的扩大而增加。

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