Statistical Circuit Design: Large Change Sensitivities for Statistical Design

Abstract

A Monte Carlo study is an analysis in the sense that for specified tolerances, correlations, etc., empiric distributions of measures of performance are obtained. An approach is presented which addresses itself to the inverse problem, that of determining the tolerances, correlations, etc., necessary to realize acceptable performance distributions. The approach is based on the concept of large change sensitivities which are proposed as a measure of sensitivity for statistical design. The approach specifically addresses design problems such as specifying tolerances, desensitizing a nominal design, recognizing the possibilities for and specifying tuning and/or matching procedures, and verifying that a design is consistent with expected statistical correlation between parameters. We present an example illustrating several of these applications.

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