Fast statistical analysis of rare circuit failure events via scaled-sigma sampling for high-dimensional variation space
Fast statistical analysis of rare circuit failure events via scaled-sigma sampling for high-dimensional variation space
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DOI：
10.1109/tcad.2015.2404895
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发表时间：
2013-11
期刊：
影响因子：
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通讯作者：
Shupeng Sun;Xin Li;Hongzhou Liu;Kangsheng Luo;Ben Gu
中科院分区：
文献类型：
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作者：
Shupeng Sun;Xin Li;Hongzhou Liu;Kangsheng Luo;Ben Gu
准确估计纳米级电路块的罕见故障率（例如，SRAM、DFF等）是一个具有挑战性的任务，特别是当变化空间是高维的。在本文中，我们提出了一种新的缩放西格玛采样（SSS）的方法来解决这一技术挑战。SSS的关键思想是从失真的分布中生成随机样本，其中标准偏差（即，σ）被放大。其次，利用“软最大值”定理建立的分析模型，从这些比例随机样本中精确地估计失效率。几个纳米级电路设计的例子表明，所提出的SSS方法达到上级精度比传统的重要性采样技术时，变化空间的维数是超过几百。
Accurately estimating the rare failure rates for nanoscale circuit blocks (e.g., SRAM, DFF, etc.) is a challenging task, especially when the variation space is high-dimensional. In this paper, we propose a novel scaled-sigma sampling (SSS) method to address this technical challenge. The key idea of SSS is to generate random samples from a distorted distribution for which the standard deviation (i.e., sigma) is scaled up. Next, the failure rate is accurately estimated from these scaled random samples by using an analytical model derived from the theorem of “soft maximum”. Several circuit examples designed in nanoscale technologies demonstrate that the proposed SSS method achieves superior accuracy over the traditional importance sampling technique when the dimensionality of the variation space is more than a few hundred.