DDD-based symbolic sensitivity analysis of active filters

Abstract

Sensitivity analysis helps circuit designers to determine boundaries to predict the variations that a particular design variable will generate in a target specification, if it differs from what is previously assumed. It is quite important in designing active filters, but the major drawback is the need of generating the symbolic transfer function, from which the normalized sensitivity formula is usually applied. In this paper we show the usefulness of applying determinant decision diagrams (DDDs), to compute sensitivities without generating the symbolic transfer function, previously. Basically, we propose replacing all symbols in the DDD graph by their numerical values, except one, i.e. the variable of interest. Therefore, all sensitivities are computed by applying Cramer's rule and by constructing one DDD for each symbol. We demonstrate the computing time gain compared to generating the fully symbolic transfer function and then applying the normalized sensitivity formula.