High-Dimensional and Multiple-Failure-Region Importance Sampling for SRAM Yield Analysis
High-Dimensional and Multiple-Failure-Region Importance Sampling for SRAM Yield Analysis
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用于 SRAM 良率分析的高维和多故障区域重要性采样
DOI：
10.1109/tvlsi.2016.2601606
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发表时间：
2017-03
影响因子：
2.8
通讯作者：
Xuan Zeng
中科院分区：
文献类型：
--
作者：
Mengshuo Wang;Changhao Yan;Xin Li;Dian Zhou;Xuan Zeng
静态RAM（SRAM）单元的故障率被限制为极低，以确保整个芯片的足够高的成品率。此外，多种性能的兴趣和外围设备的影响，使SRAM的故障率估计的高维多故障区域的问题。本文提出了一种新的方法，具有多起点序列二次规划（SQP）框架，以扩展最小范数重要性抽样（IS）来解决这个问题。首先通过低偏差抽样序列在变异空间中找到失效区域。然后，在所有识别的故障区域中生成起点，并从这些起点调用基于SQP的局部优化，以搜索最优移位向量（OSV）。基于OSV，构造了IS的高斯混合畸变分布。为了进一步降低IS的计算成本，同时充分考虑增加的维度的影响，提出了一种自适应的模型训练框架，以保持低维和高维问题的高效率。实验结果表明，该方法不仅能在低维情况下高效准确地逼近失效率，而且在高维情况下也能保持失效率特征。
The failure rate of static RAM (SRAM) cells is restricted to be extremely low to ensure sufficient high yield for the entire chip. In addition, multiple performances of interest and influences from peripherals make SRAM failure rate estimation a high-dimensional multiple-failure-region problem. This paper proposes a new method featuring a multistart-point sequential quadratic programming (SQP) framework to extend minimized norm importance sampling (IS) to address this problem. Failure regions in the variation space are first found by the low-discrepancy sampling sequence. Afterward, start points are generated in all identified failure regions and local optimizations based on SQP are invoked from these start points searching for the optimal shift vectors (OSVs). Based on the OSVs, a Gaussian mixture distorted distribution is constructed for IS. To further reduce the computational cost of IS while fully considering the influence of increasing dimensionality, an adaptive model training framework is proposed to keep high efficiency for both low- and high-dimensional problems. The experimental results show that the proposed method can not only approximate failure rate with high accuracy and efficiency in low-dimensional cases but also keep these features in high-dimensional ones.
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DOI：
10.1109/tcad.2015.2404895
发表时间：
2013-11
期刊：
2013 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)
影响因子：
--
作者：
Shupeng Sun;Xin Li;Hongzhou Liu;Kangsheng Luo;Ben Gu
通讯作者：
Shupeng Sun;Xin Li;Hongzhou Liu;Kangsheng Luo;Ben Gu
DOI：
10.1145/1837274.1837372
发表时间：
2010-06
期刊：
Design Automation Conference
影响因子：
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作者：
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Fang Gong;Hao Yu;Yiyu Shi;D. Kim;Junyan Ren;Lei He
DOI：
10.1109/date.2010.5456940
发表时间：
2010-03
期刊：
2010 Design, Automation & Test in Europe Conference & Exhibition (DATE 2010)
影响因子：
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作者：
Masood Qazi;M. Tikekar;L. Dolecek;Devavrat Shah;A. Chandrakasan
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Masood Qazi;M. Tikekar;L. Dolecek;Devavrat Shah;A. Chandrakasan
DOI：
10.1109/iccad.2008.4681593
发表时间：
2008-11
期刊：
2008 IEEE/ACM International Conference on Computer-Aided Design
影响因子：
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作者：
L. Dolecek;Masood Qazi;Devavrat Shah;A. Chandrakasan
通讯作者：
L. Dolecek;Masood Qazi;Devavrat Shah;A. Chandrakasan
DOI：
--
发表时间：
2005-10
期刊：
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影响因子：
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作者：
G. S. Fishman
通讯作者：
G. S. Fishman