An efficient macromodeling approach for statistical IC process design

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 device design centering example.<>