QuickYield: An efficient global-search based parametric yield estimation with performance constraints
QuickYield: An efficient global-search based parametric yield estimation with performance constraints
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DOI：
10.1145/1837274.1837372
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发表时间：
2010-06
期刊：
影响因子：
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通讯作者：
Fang Gong;Hao Yu;Yiyu Shi;D. Kim;Junyan Ren;Lei He
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文献类型：
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作者：
Fang Gong;Hao Yu;Yiyu Shi;D. Kim;Junyan Ren;Lei He
随着工艺规模缩小到90 nm及以下，已经提出了许多成品率驱动的设计和优化方法来科普显著的工艺变化并提高成品率。影响这些方法效率的一个关键问题是在给定设计参数变化的情况下估计成品率。现有的方法要么在性能域中使用蒙特卡罗方法，其中需要数千次模拟，要么在参数域中使用局部搜索，其中需要一些模拟来表征由性能约束定义的屈服边界上的点。为了提高效率，在本文中，我们提出了QuickYield，屈服面边界确定的表面点发现和全球搜索。在许多不同电路上的实验表明，对于相同的精度，QuickYield比Monte Carlo方法快519倍，比文献中报道的最快方法YENSS快4.7倍。
With technology scaling down to 90nm and below, many yield-driven design and optimization methodologies have been proposed to cope with the prominent process variation and to increase the yield. A critical issue that affects the efficiency of those methods is to estimate the yield when given design parameters under variations. Existing methods either use Monte Carlo method in performance domain where thousands of simulations are required, or use local search in parameter domain where a number of simulations are required to characterize the point on the yield boundary defined by performance constraints. To improve efficiency, in this paper we propose QuickYield, a yield surface boundary determination by surface-point finding and global-search. Experiments on a number of different circuits show that for the same accuracy, QuickYield is up to 519X faster compared with the Monte Carlo approach, and up to 4.7X faster compared with YENSS, the fastest approach reported in literature.