A Noise-Aware Hybrid Method for SDD Pattern Grading and Selection

摘要：

Testing for small-delay defects (SDDs) is necessary for ensuring product quality in smaller technology nodes. Current tools such as transition-delay fault (TI)F) ATPGs and timing-aware ATPGs are either inefficient in detecting SDDs or suffering from large pattern count and CPU runtime. Furthermore, none of these methodologies take into account the impact of pattern-induced noises, e.g., power supply noise (PSN) and crosstalk, which are potential sources of SDDs. In this paper, we present a hybrid method considering the impacts of pattern-induced noises to grade and select the most effective patterns for detecting SDDs. The grading procedure is performed on a large repository of patterns generated by n-detect TDF ATPG. Top-off ATPG is performed after pattern selection to achieve the same fault coverage as that for timing-aware ATPG. The experimental results demonstrate the efficiency of our proposed method; it results in a pattern count close to 1-detect ATPG while sensitizes similar or greater number of long paths than the commercial tuning-aware ATPG pattern set.

关键词： Delay test small-delay defects pattern selection power supply noise crosstalk

作者: Ke Peng Mahmut Yilmaz Krishnendu Chakrabarty Mohammad Tehranipoor

作者单位: ECE Department, University of Connecticut Advanced Micro Devices, Inc., Sunnyvale, California ECE Department, Duke University

会议类型: 国际会议

会议名称: 2010 19th IEEE Asian Test Symposium(第19届IEEE亚洲测试技术学术会议 ATS 2010)

会议地点: 上海

会议语种:英文

页码: 331-336

在线出版日期: 2010-12-01（万方平台首次上网日期，不代表论文的发表时间）