We present a methodology for automated sizing of analog cells using statistical optimization in a simulation based approach. This methodology enables to design complex analog cells from scratch within reasonable CPU time. Three different specification types are covered: strong constraints on the electrical performance of the cells, weak constraints on this performance, and design objectives. A mathematical cost function is proposed and a bunch of heuristics is given to increase accuracy and reduce CPU time to minimize the cost function. A technique is also presented to yield designs with reduced variability in the performance parameters, under random variations of the transistor technological parameters. Several CMOS analog cells with complexity levels up to 48 transistors are designed for illustration. Measurements from fabricated prototypes demonstrate the suitability of the proposed methodology.
We present a methodology for automated sizing of analog cells using statistical optimization in a simulation based approach. This methodology enables to design complex analog cells from scratch within reasonable CPU time. Three different specification types are covered: strong constraints on the electrical performance of the cells, weak constraints on this performance, and design objectives. A mathematical cost function is proposed and a bunch of heuris
A tool is presented that evaluates statistical deviations in performance characteristics of analog circuits, starting from statistical deviations in the technological parameters of MOS transistors. Performance is demonstrated via the analysis of a Miller OTA in two different configurations and a linearized CMOS transconductor.The CPU time is reduced by a factor of 25 to 90 with respect to conventional Monte Carlo simulation, while maintaining similar accuracy in the computations.
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