A Self-Biased High Performance Folded Cascode CMOS Op-Amp
VLSID '97 Proceedings of the Tenth International Conference on VLSI Design: VLSI in Multimedia Applications
Alternative techniques to solve hard multi-objective optimization problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Analog circuit optimization system based on hybrid evolutionary algorithms
Integration, the VLSI Journal
Design of Analog CMOS Integrated Circuits
Design of Analog CMOS Integrated Circuits
Analog circuit design optimization through the particle swarm optimization technique
Analog Integrated Circuits and Signal Processing
Analog circuit design by nonconvex polynomial optimization: Two design examples
International Journal of Circuit Theory and Applications
Constraint multi-objective automated synthesis for CMOS operational amplifier
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Rank-density-based multiobjective genetic algorithm and benchmark test function study
IEEE Transactions on Evolutionary Computation
CMOS op-amp sizing using a geometric programming formulation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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A multi-objective evolution algorithm (MOEA) is presented to automatically determine the parameters in Op-Amp synthesis where the cost functions (e.g., minimizing the power dissipation and the chip area) and the constraint functions (e.g., the user-defined specifications) can be modeled as polynomials of the design variables. The proposed algorithm is based on MOEA which does not use weighting coefficients in converting multiple objectives into single objective. A constraint handling strategy without penalty parameters is proposed to avoid the difficulty of penalty parameter selection. Moreover, an elitist maintaining scheme is utilized to keep the evenness of the Pareto front. Simulations over several benchmark functions validate the efficiency of the proposed algorithm for the evenness of population distribution and the convergence to the Pareto front. Numerical experiments of a Miller compensated two-stage Op-Amp show that the proposed MOEA is able to achieve better performance than NSGA-II+PCH, GA+SPF and GA+PCH.