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Hard and Soft Constraints for Multi-objective Analog IC Sizing Optimization

Lourenço, N. ; Moutaye, E. ; Martins, R. M. ; Canelas, A. ; Póvoa , R. P. ; Horta, N.

Hard and Soft Constraints for Multi-objective Analog IC Sizing Optimization, Proc International Conf. on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design - SMACD, Lausanne, Switzerland, Vol. , pp. - , July, 2019.

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Abstract
In this paper, the constraint handling of the nondominated
sorting genetic algorithm II (NSGA-II) is modified to
accommodate for both soft and hard constraints, by introducing the concept of soft-feasible solutions. In this context, soft-feasible solutions are design points that fail to meet the hard constraints (original target specifications) but meet the soft constraints (acceptable relaxation for some of the hard constraints). This soft/hard constraint definition responds to a real-world need since not all constraints have the same
relevance and it can be hard to predict reasonable values beforehand.
Since analog IC sizing optimization is done on highly constrained
search spaces, the proposed methodology increases the capability to
retain meaningful soft-feasible elements, hence augmenting diversity
when hard-feasibility is difficult to achieve. The proposed
methodology was implemented and tested on two circuit topologies,
showing improvements of up to 31% on the average dominated
hypervolume for difficult but existent target specifications. Moreover,
when the target specifications are set to values impossible to be met by
the topology, the proposed technique can obtain meaningful
performance tradeoffs over the soft-feasible solutions.