Name
Title | ||
|---|---|---|
Device-parameter estimation with on-chip variation sensors considering random variability |
Abstract | ||
|---|---|---|
Device-parameter monitoring sensors inside a chip are gaining its importance as the post-fabrication tuning is becoming of a practical use. In estimation of variational parameters using on-chip sensors, it is often assumed that the outputs of variation sensors are not affected by random variations. However, random variations can deteriorate the accuracy of the estimation result. In this paper, we propose a device-parameter estimation method with on-chip variation sensors explicitly considering random variability. The proposed method derives the global variation parameters and the standard deviation of the random variability using the maximum likelihood estimation. We experimentally verified that the proposed method can accurately estimate variations, whereas the estimation result deteriorates when neglecting random variations. We also demonstrate an application result of the proposed method to test chips fabricated in a 65-nm process technology.
|
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ASPDAC.2011.5722274 | ASP-DAC |
Keywords | Field | DocType |
variation sensor,random variation,device-parameter estimation,maximum likelihood estimation,random variability,estimation result,application result,device-parameter estimation method,on-chip variation sensor,global variation parameter,accuracy,standard deviation,sensors,maximum likelihood estimate,chip,parameter estimation,silicon,random variable,sensitivity,random processes | Computer science,Maximum likelihood,Stochastic process,Electronic engineering,Chip,Estimation theory,Process variability,Standard deviation | Conference |
ISSN | ISBN | Citations |
2153-6961 | 978-1-4244-7516-2 | 3 |
PageRank | References | Authors |
0.53 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ken'ichi Shinkai | 1 | 14 | 3.48 |
| Masanori Hashimoto | 2 | 462 | 79.39 |