A Method to Model Statistical Path Delays for Accurate Defect Coverage

被引：0
|
作者：
Javvaji, Pavan Kumar [1 ]
Tragoudas, Spyros [1 ]
机构：
[1] Southern Illinois Univ, Dept Elect & Comp Engn, Carbondale, IL 62901 USA
来源：
2018 IEEE INTERNATIONAL SYMPOSIUM ON DEFECT AND FAULT TOLERANCE IN VLSI AND NANOTECHNOLOGY SYSTEMS (DFT) | 2018年
关键词：
delay modeling; path delay faults; critical path selection;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The statistical delay of a path is traditionally modeled as a Gaussian random variable assuming that the path is always sensitized by a test pattern. Its sensitization in various circuit instances varies among its test patterns and the pattern induced delay is non-Gaussian. It is modeled using probability mass functions. The defect coverage is improved by test pattern selection using machine learning. Experimental results demonstrate accuracy in defect coverage when comparing to existing methods.
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页数：6
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