Sequential importance sampling for low-probability and high-dimensional SRAM yield analysis
Sequential importance sampling for low-probability and high-dimensional SRAM yield analysis
复制标题
DOI：
10.1109/iccad.2010.5654259
复制
发表时间：
2010-11
期刊：
影响因子：
--
通讯作者：
Kentarou Katayama;Shiho Hagiwara;Hiroshi Tsutsui;H. Ochi;Takashi Sato
中科院分区：
文献类型：
--
作者：
Kentarou Katayama;Shiho Hagiwara;Hiroshi Tsutsui;H. Ochi;Takashi Sato
在本文中，一个显着的加速估计低故障率在高维SRAM的成品率分析实现使用序贯重要性抽样。所提出的方法系统地，自主地，自适应地探索感兴趣的故障区域，而以前的工作需要诉诸暴力搜索。暴力搜索和自适应试验分布的消除显着提高了迄今未解决的高维情况下，其中包括阈值电压，沟道长度，载流子迁移率等的变化源，同时考虑故障率估计的效率。该方法适用于大范围的高维稀有事件蒙特卡罗模拟分析。在SRAM成品率估计示例中，我们在6维问题中实现了106倍于标准蒙特卡罗模拟的加速，故障概率为3 × 10−9。文中还给出了其它方法无效的24维分析实例。
In this paper, a significant acceleration of estimating low-failure rate in a high-dimensional SRAM yield analysis is achieved using sequential importance sampling. The proposed method systematically, autonomously, and adaptively explores failure region of interest, whereas all previous works needed to resort to brute-force search. Elimination of brute-force search and adaptive trial distribution significantly improves the efficiency of failure-rate estimation of hitherto unsolved high-dimensional cases wherein a lot of variation sources including threshold voltages, channel-length, carrier mobility, etc. are simultaneously considered. The proposed method is applicable to wide range of Monte Carlo simulation analyses dealing with high-dimensional problem of rare events. In SRAM yield estimation example, we achieved 106 times acceleration compared to a standard Monte Carlo simulation for a failure probability of 3 × 10−9 in a six-dimensional problem. The example of 24-dimensional analysis on which other methods are ineffective is also presented.