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Performance modeling of analog circuits using additive regression splines

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2 Author(s)
Chao, C.-Y. ; Dept. of Electr. Eng., Maryland Univ., College Park, MD, USA ; Milor, L.

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 SC filter bank. This example demonstrates that very accurate nonlinear models can be constructed very efficiently

Published in:

Custom Integrated Circuits Conference, 1994., Proceedings of the IEEE 1994

Date of Conference:

1-4 May 1994

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