Recursive Statistical Blockade: An Enhanced Technique for Rare Event Simulation with Application to SRAM Circuit Design
Recursive Statistical Blockade: An Enhanced Technique for Rare Event Simulation with Application to SRAM Circuit Design
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DOI：
10.1109/vlsi.2008.54
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发表时间：
2008-01
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影响因子：
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通讯作者：
Amith Singhee;Jiajing Wang;B. Calhoun;Rob A. Rutenbar
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作者：
Amith Singhee;Jiajing Wang;B. Calhoun;Rob A. Rutenbar
在统计工艺变化下的电路可靠性是一个日益受到关注的领域。对于SRAM和触发器等高度重复的电路，一个电路的罕见统计事件可能会导致不那么罕见的系统故障。统计封锁是作为一种蒙特卡罗技术提出的，它使我们能够有效地过滤到封锁我们所寻求的尾部分布中不够罕见的不需要的样本。然而，该技术存在重大的实际问题。在这项工作中，我们展示了SRAM设计中的常见场景，这些问题使统计封锁无效。然后，我们提出了显着的扩展，使统计封锁实际上可用于这些常见的场景。对于采用工业90纳米技术的SRAM单元，我们显示比标准统计封锁的加速比为102+，比标准蒙特卡罗的加速比为104+。
Circuit reliability under statistical process variation is an area of growing concern. For highly replicated circuits such as SRAMs and flip flops, a rare statistical event for one circuit may induce a not-so-rare system failure. The Statistical Blockade was proposed as a Monte Carlo technique that allows us to efficiently filter-to block-unwanted samples insufficiently rare in the tail distributions we seek. However, there are significant practical problems with the technique. In this work, we show common scenarios in SRAM design where these problems render Statistical Blockade ineffective. We then propose significant extensions to make Statistical Blockade practically usable in these common scenarios. We show speedups of 102+ over standard Statistical Blockade and 104+ over standard Monte Carlo, for an SRAM cell in an industrial 90 nm technology.