Paper Info
Title
Determination of optimal polynomial regression function to decompose on-die systematic and random variations
Abstract
A procedure that decomposes measured parametric device variation into systematic and random components is studied by considering the decomposition process as selecting the most suitable model for describing on-die spatial variation trend. In order to maximize model predictability, the log-likelihood estimate called corrected Akaike information criterion is adopted. Depending on on-die contours of underlying systematic variation, necessary and sufficient complexity of the systematic regression model is objectively and adaptively determined. The proposed procedure is applied to 90-nm threshold voltage data and found the low order polynomials describe systematic variaiation very well. Designing cost-effective variation monitoring circuits as well as appropriate model determination of on-die variation are hence facilitated.
Year
DOI
Venue
2008
10.1109/ASPDAC.2008.4484006
ASP-DAC
Keywords
Field
DocType
random variation,on-die variation,suitable model,cost-effective variation monitoring circuit,parametric device variation,optimal polynomial regression function,systematic regression model,on-die spatial variation trend,systematic variation,model predictability,systematic variaiation,appropriate model determination,polynomials,data mining,polynomial regression,predictive models,cost effectiveness,fabrication,regression model,threshold voltage,formal verification,spatial variation,akaike information criterion,algorithm,regression analysis,cmos,cmos integrated circuits
Predictability,Akaike information criterion,Polynomial,Regression analysis,Computer science,Polynomial regression,Electronic engineering,Parametric statistics,Spatial variability,Formal verification
Conference
ISSN
ISBN
Citations 
2153-6961
978-1-4244-1922-7
5
PageRank 
References 
Authors
0.43
6
4
Name
Order
Citations
PageRank
Takashi Sato1131.63
Hiroyuki Ueyama2161.68
Noriaki Nakayama3308.95
Kazuya Masu412036.37