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Content Provider | IEEE Xplore Digital Library |
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Author | Chieh-Yuan Chao Milor, L.S. |
Copyright Year | 1988 |
Abstract | Circuit designers need to be able to predict variations in circuit performance as a function of variations in process parameters. Often the relation between process parameters and circuit performances is highly nonlinear, and the process is described by a large number of independent variables. Traditional approaches to modeling, like, polynomial regression, are not very accurate for such problems. In order to build accurate nonlinear models for high-dimensional problems, an algorithm has been implemented based on additive regression splines. The model building process is fully automated. The algorithm is used to build a model to predict the offset voltage of a parallel filter bank. This example demonstrates that very accurate nonlinear models can be constructed very efficiently.< |
Sponsorship | IEEE Electron Devices Society IEEE Components, Packaging, and Manufacturing Technology Society IEEE Reliability Society IEEE Solid-State Circuits Society |
Starting Page | 239 |
Ending Page | 251 |
Page Count | 13 |
File Size | 1314750 |
File Format | |
ISSN | 08946507 |
Volume Number | 8 |
Issue Number | 3 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 1995-08-01 |
Publisher Place | U.S.A. |
Access Restriction | One Nation One Subscription (ONOS) |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Polynomials Circuit simulation Circuit optimization Manufacturing processes Analytical models Monte Carlo methods Performance analysis Equations Chaos Buildings |
Content Type | Text |
Resource Type | Article |
Subject | Industrial and Manufacturing Engineering Condensed Matter Physics Electronic, Optical and Magnetic Materials Electrical and Electronic Engineering |
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