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A Generic Data-Driven Nonparametric Framework for Variability Analysis of Integrated Circuits in Nanometer Technologies.
- Source :
-
IEEE Transactions on Computer-Aided Design of Integrated Circuits & Systems . Jul2009, Vol. 28 Issue 7, p1038-1046. 9p. 1 Diagram, 1 Chart, 7 Graphs. - Publication Year :
- 2009
-
Abstract
- We present a generic data-driven nonparametric analyzer (GDNA) to estimate the impact of process variations device properties and circuit functionalities in nanometer technologies. The mathematical framework of GDNA uses a kernel estimator that eliminates the need for a priori assumption of nature of variation (i.e., no a priori choice is required for the density of a random variable). Furthermore, a generic tail probability estimator is developed that uses the kernel estimator to predict low occurrence probabilities using a small set of observed samples. Verifications using statistical simulations show that GDNA can reliably predict variability in device/circuit properties and hence facilitate technology development and circuit design under process variation. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02780070
- Volume :
- 28
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Computer-Aided Design of Integrated Circuits & Systems
- Publication Type :
- Academic Journal
- Accession number :
- 43049310
- Full Text :
- https://doi.org/10.1109/TCAD.2009.2017429