Design of pipeline analog-to-digital converters via geometric programming
Proceedings of the 2002 IEEE/ACM international conference on Computer-aided design
Convex Optimization
OPERA: optimization with ellipsoidal uncertainty for robust analog IC design
Proceedings of the 42nd annual Design Automation Conference
Proceedings of the 43rd annual Design Automation Conference
Fast, non-Monte-Carlo estimation of transient performance variation due to device mismatch
Proceedings of the 44th annual Design Automation Conference
Optimal design of a CMOS op-amp via geometric programming
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Asymptotic Probability Extraction for Nonnormal Performance Distributions
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Worst-case analysis and optimization of VLSI circuit performances
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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This paper proposes an equation-based multi-scenario iterative robust optimization methodology for analog/mixed-signal circuits. We show that due to local circuit performance monotonicity in random variations constraint maximization can be used to efficiently find critical constraints and worst-case scenarios of random process variations and populate them into a multi-scenario optimization. This algorithm scales gracefully with circuit size and is tested on both two-stage and fully differential folded-cascode operational amplifiers with a 90 nm predictive model. The improving yield-trends are confirmed across process and random variations with Hspice Monte-Carlo simulations.