Abstract
An efficient macromodeling approach for statistical IC process design based on experimental design and regression analysis is described. Automatic selection of the input variables is done as part of the model-building procedure to reduce the problem dimension to a manageable size. The resulting macromodels are simple analytical expressions describing the device characteristics in terms of the fundamental process variables. The validity and efficiency of the macromodels obtained by the approach are illustrated through their use in an IC process design centering example. Although the approach has only been applied to the IC fabrication process level, the underlying methodology can also be used to obtain circuit-level macromodels.

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