An Efficient Non-Gaussian Sampling Method for High Sigma SRAM Yield Analysis
An Efficient Non-Gaussian Sampling Method for High Sigma SRAM Yield Analysis
复制标题
用于高西格玛 SRAM 良率分析的高效非高斯采样方法
DOI：
10.1145/3174866
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发表时间：
2018
影响因子：
1.4
通讯作者：
Zeng Xuan
中科院分区：
文献类型：
--
作者：
Zhai Jinyuan;Yan Changhao;Wang Sheng-Guo;Zhou Dian;Zhou Hai;Zeng Xuan
SRAM的产量分析是一个具有挑战性的问题，因为SRAM单元的故障率非常小。本文提出了一种高效的非高斯抽样交叉熵优化方法，用于估计高西格玛SRAM成像率。采用非高斯分布，即一维广义Pareto分布和（n-1）维高斯分布的联合函数族作为实际分布的函数族，代替了现有方法中高斯分布的抽样，证明了在极端失效事件下，非高斯分布更适合拟合理想分布。为了使实际分布与理想分布之间的交叉熵最小化，采用多起点策略的顺序二次规划求解器计算实际分布的最优参数。实验结果表明，所提出的非高斯采样比高斯采样的速度提高了2.2—4.1倍，总体上比目前最先进的低维和高维情况下的方法加快了1.6—2.3倍，且精度没有损失
Yield1 analysis of SRAM is a challenging issue, because the failure rates of SRAM cells are extremely small. In this article, an efficient non-Gaussian sampling method of cross entropy optimization is proposed for estimating the high sigma SRAM yield. Instead of sampling with the Gaussian distribution in existing methods, a non-Gaussian distribution, i.e., a joint one-dimensional generalized Pareto distribution and (n-1)-dimensional Gaussian distribution, is taken as the function family of practical distribution, which is proved to be more suitable to fit the ideal distribution in the view of extreme failure event. To minimize the cross entropy between practical and ideal distributions, a sequential quadratic programing solver with multiple starting points strategy is applied for calculating the optimal parameters of practical distributions. Experimental results show that the proposed non-Gaussian sampling is a 2.2--4.1× speedup over the Gaussian sampling, on the whole, it is about a 1.6--2.3× speedup over state-of-the-art methods with low- and high-dimensional cases without loss of accuracy