Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/DFT.2018.8602962 | 2018 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT) |
Keywords | Field | DocType |
delay modeling,path delay faults,critical path selection | Probability mass function,Random variable,Logic gate,Normal distribution,Pattern selection,Computer science,Algorithm,Digital signature,Electronic engineering | Conference |
ISSN | ISBN | Citations |
1550-5774 | 978-1-5386-8399-6 | 0 |
PageRank | References | Authors |
0.34 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pavan Kumar Javvaji | 1 | 0 | 1.35 |
| Spyros Tragoudas | 2 | 625 | 88.87 |