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Dynamic Statistical-Timing-Analysis-Based VLSI Path Delay Test Pattern Generation

机译:基于动态统计时间分析的VLSI路径延迟测试模式生成

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Nanoscale VLSI systems are subject to increasingly significant performance variability. Accurate timing analysis and effective silicon-based performance verification techniques are critical to successful nanoscale VLSI design. The state-of-the-art statistical static timing analysis (SSTA) techniques cannot capture performance variability due to primary inputs and sequential element states which, however, is critical to path delay test generation. In this paper, we present the first dynamic statistical timing analysis-based VLSI path delay test pattern generation technique. We observe that VLSI timing analysis and power estimation target the same signal toggling activity. By leveraging the existing power estimation techniques, we have developed signal-probability-based statistical timing analysis (SPSTA), and SPSTA-based VLSI delay test pattern generation (SPSTA-DTPG) techniques. Our experimental results based on ISCAS’89 benchmark circuits show that the state-of-the-art statistical static timing analysis-based delay test pattern generation (SSTA-DTPG) achieves an average of 47.32%, 45.14%, and 57.98%, SPSTA-DTPG achieves an average of 57.41%, 61.43%, and 68.05%, while signal probability-based statistical timing analysis-based delay test pattern generation with (test pattern) compaction (SPSTA-DTPG-C) achieves an average of 83.09%, 87.48% and 90.30% coverage of the top 50, 100, and 200 timing-critical paths, respectively.
机译:纳米级VLSI系统的性能可变性越来越大。准确的时序分析和有效的基于硅的性能验证技术对于成功的纳米级VLSI设计至关重要。最新的统计静态时序分析(SSTA)技术由于主要输入和顺序元素状态而无法捕获性能可变性,然而,这对于路径延迟测试的生成至关重要。在本文中,我们提出了第一个基于动态统计时序分析的VLSI路径延迟测试模式生成技术。我们观察到,VLSI时序分析和功率估计针对相同的信号切换活动。通过利用现有的功率估计技术,我们开发了基于信号概率的统计时序分析(SPSTA)和基于SPSTA的VLSI延迟测试模式生成(SPSTA-DTPG)技术。我们基于ISCAS'89基准电路的实验结果表明,基于统计静态时序分析的最新技术的延迟测试码型生成(SSTA-DTPG)的SPSTA平均达到47.32%,45.14%和57.98% -DTPG的平均值达到57.41%,61.43%和68.05%,而基于信号概率的基于统计时序分析的延迟测试图案生成(测试图案)压缩(SPSTA-DTPG-C)的平均值达到83.09%,前50条,100条和200条时序关键路径的覆盖率分别为87.48%和90.30%。

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