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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. > |
