Design centering is the term used for a procedure of obtaining enhanced parametric yield of a circuit despite the variations in device and design parameters. The process variability in nanometer regimes manifest into variations in these devices and design parameters. During design space exploration of analog circuits, a methodology to find design-instances with better yield is necessitated; this would ensure that the circuit will function as per specifications after fabrication, even with impact of statistical variations. We need to evaluate circuit performance for a given instance of a circuit-design identified by possessing a set of nominal values of device-design parameters. A lot of instances need be searched, having different sizes for a given circuit topology. HSPICE is very compute intensive. Instead, we employ macromodeling approach for analog circuits based on support vector machine (SVM), which enables efficient evaluation of performance of such circuits of different sizing during yield optimization loops. These performance macromodels are found to be as accurate as SPICE and at the same time, time-efficient for use in sizing of analog circuits with optimal yield. Process variability aware SVM macromodels are first trained and then used inside the Genetic algorithm loops for design centering of different circuits, subsequently resulting into sized-circuit instances having optimal yield. Post design centering, the sized circuits will be able to provide functions as per specifications upon fabrication. The application of this design centering approach as process variability analysis tool is illustrated on various circuits e.g. two stage op amp, voltage controlled oscillator and mixer circuit with layouts drawn into 90 nm UMC technology (Euro-practice).
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