NDLI: Statistical regression for efficient high-dimensional modeling of analog and mixed-signal performance variations

Content Provider	IEEE Xplore Digital Library
Author	Xin Li Hongzhou Liu
Copyright Year	2008
Description	Author affiliation: Dept. of ECE, Carnegie Mellon Univ., Pittsburgh, PA (Xin Li)
Abstract	The continuous technology scaling brings about high-dimensional performance variations that cannot be easily captured by the traditional response surface modeling. In this paper we propose a new statistical regression (STAR) technique that applies a novel strategy to address this high dimensionality issue. Unlike most traditional response surface modeling techniques that solve model coefficients from over-determined linear equations, STAR determines all unknown coefficients by moment matching. As such, a large number of (e.g., $10^{3}~10^{5})$ model coefficients can be extracted from a small number of (e.g., $10^{2}~10^{3})$ sampling points without over-fitting. In addition, a novel recursive estimator is proposed to accurately and efficiently predict the moment values. The proposed recursive estimator is facilitated by exploiting the interaction between different moment estimators and formulating the moment estimation problem into a special form that can be iteratively solved. Several circuit examples designed in commercial CMOS processes demonstrate that STAR achieves more than 20times runtime speedup compared with the traditional response surface modeling.
Starting Page	38
Ending Page	43
File Size	451300
Page Count	6
File Format	PDF
ISBN	9781605581156
ISSN	0738100X
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Association for Computing Machinery, Inc. (ACM)
Subject Keyword	Response surface methodology Integrated circuit modeling Semiconductor device modeling Recursive estimation Space technology Predictive models Semiconductor process modeling CMOS process Random variables Manufacturing processes Circuits Response Surface Modeling Process Variations
Content Type	Text
Resource Type	Article

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