NDLI: An error-driven adaptive grid refinement algorithm for automatic generation of analog circuit performance macromodels

Content Provider	IEEE Xplore Digital Library
Author	Mengmeng Ding Wolfe, G. Vemuri, R.
Copyright Year	2005
Description	Author affiliation: Cincinnati Univ., OH, USA (Mengmeng Ding; Wolfe, G.; Vemuri, R.)
Abstract	In this paper, we present an error-driven adaptive sampling algorithm called adaptive grid refinement (AGR) algorithm to automatically generate performance macromodels for analog circuits. Starting from samples on a coarse grid, the AGR algorithm builds a global model and validates its accuracy on an independent validation data set sampled within this grid. If this model is not accurate enough on the validation data, the grid is split into equal sized smaller grids. On each of these grids, a local model is built using samples on this grid and its neighboring and validated similarly. A grid will not be further refined only if the corresponding local model is accurate on its validation data set. The algorithm will stop when all the local models are accurate on their corresponding validation data set. We build six performance macromodels of a CMOS opamp using the AGR algorithm and compare it with the competing techniques. The strengths and weaknesses of the proposed algorithm are discussed.
Sponsorship	Chinese Inst. of Electron
Starting Page	477
Ending Page	482
File Size	1064989
Page Count	6
File Format	PDF
ISBN	0780387368
DOI	10.1109/ASPDAC.2005.1466210
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-01-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mesh generation Analog circuits Circuit simulation Semiconductor device modeling Circuit topology Hypercubes Force sensors Sampling methods Spline Operational amplifiers
Content Type	Text
Resource Type	Article

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