Paper Info
Title
Beyond low-order statistical response surfaces: latent variable regression for efficient, highly nonlinear fitting
Abstract
The number and magnitude of process variation sources are increasing as we scale further into the nano regime. Today's most successful response surface methods limit us to low-order forms -- linear, quadratic -- to make the fitting tractable. Unfortunately, not all variation-al scenarios are well modeled with low-order surfaces. We show how to exploit latent variable regression ideas to support efficient extraction of arbitrarily nonlinear statistical response surfaces. An implementation of these ideas called SiLVR, applied to a range of analog and digital circuits, in technologies from 90 to 45nm, shows significant improvements in prediction, with errors reduced by up to 21X, with very reasonable runtime costs.
Year
DOI
Venue
2007
10.1145/1278480.1278542
design automation conference
Keywords
Field
DocType
nanotechnology,network analysis,regression analysis,response surface methodology,SiLVR,efficient highly nonlinear fitting,latent variable regression,low-order statistical response surfaces,Algorithms,DFM,Design,Dimensionality reduction,Regression,Response Surface
Mathematical optimization,Nonlinear system,Dimensionality reduction,Regression analysis,Computer science,Algorithm,Quadratic equation,Electronic engineering,Latent variable,Process variation,Network analysis,Artificial neural network
Conference
ISSN
Citations 
PageRank 
0738-100X
21
1.38
References 
Authors
12
2
Name
Order
Citations
PageRank
Amith Singhee134722.94
Rob A. Rutenbar22283280.48