基于输入感知统计时序分析的延迟测试模式生成

Bao Liu, Lu Wang
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引用次数: 1

摘要

延迟测试模式的生成已成为高性能VLSI设计中日益重要的问题。现有的技术通过STA或SSTA找到定时关键路径,然后应用传统的ATPG算法找到测试模式。本文提出了一种新的延迟测试模式生成方法,该方法通过更精确的输入感知统计时序分析找到时序关键路径,通过反向跟踪获得输入模式,并通过逻辑仿真验证输入模式下估计的时序关键路径。基于9个ISCAS’89基准电路的实验结果表明,在测试规模为50、100和200的情况下,最先进的SSTA-TQM-BnB技术的延迟故障覆盖率平均为57.83%、54.50%和69.91%,而SPSTA-DTPG技术的延迟故障覆盖率平均为67.83%、71.39%和77.53%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Input-aware statistical timing analysis-based delay test pattern generation
Delay test pattern generation has emerged as an increasingly critical problem in high performance VLSI designs. Existing techniques find timing critical paths by STA or SSTA, apply a traditional ATPG algorithm subsequently and find the test patterns. In this paper, we propose a new delay test pattern generation method, which finds timing critical paths by more accurate input-aware statistical timing analysis, achieves input patterns by back-tracing, and verifies the estimated timing critical paths under the input patterns by logic simulation. Our experimental results based on 9 ISCAS'89 benchmark circuits show that the state-of-the-art SSTA-TQM-BnB technique achieves an average of 57.83%, 54.50%, and 69.91% delay fault coverage, while our SPSTA-DTPG technique achieves an average of 67.83%, 71.39%, and 77.53% delay fault coverage for a test size of 50, 100, and 200, respectively.
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