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Experience in critical path selection for deep sub-micron delay test and timing validation
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Source Asia and South Pacific Design Automation Conference archive
Proceedings of the 2003 Asia and South Pacific Design Automation Conference table of contents
Kitakyushu, Japan
SESSION: Test issues for deep sub-micron design table of contents
Pages: 751 - 756  
Year of Publication: 2003
ISBN:0-7803-7660-9
Authors
Jing-Jia Liou  University of California at Santa Barbara
Li-C. Wang  University of California at Santa Barbara
Angela Krstic  University of California at Santa Barbara
Kwang-Ting Cheng  University of California at Santa Barbara
Sponsors
SIGDA: ACM Special Interest Group on Design Automation
IPSJ : Information Processing Society of Japan
IEICE : Institute of Electronics, Information and Communication Engineers
: IEEE Circuits and Systems Society
Publisher
ACM  New York, NY, USA
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ABSTRACT

Critical path selection is an indispensable step for AC delay test and timing validation. Traditionally, this step relies on the construction of a set of worse-case paths based upon discrete timing models. However, the assumption of discrete timing models can be invalidated by timing defects and process variation in the deep sub-micron domain, which are often continuous in nature. As a result, critical paths defined in a traditional timing analysis approach may not be truly critical in reality. In this paper, we propose using a statistical delay evaluation framework for estimating the quality of a path set. Based upon the new framework, we demonstrate how the traditional definition of a critical path set may deviate from the true critical path set in the deep sub-micron domain. To remedy the problem, we discuss improvements to the existing path selection strategies by including new objectives. We then compare statistical approaches with traditional approaches based upon experimental analysis of both defect-free and defect-injected cases.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Jing-Jia Liou: colleagues
Li-C. Wang: colleagues
Angela Krstic: colleagues
Kwang-Ting Cheng: colleagues