Input-aware statistical timing analysis-based delay test pattern generation

Bao Liu, Lu Wang
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引用次数: 1

Abstract

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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基于输入感知统计时序分析的延迟测试模式生成
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%。
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