Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Performance trade-off analysis of analog circuits by normal-boundary intersection
Proceedings of the 40th annual Design Automation Conference
A Methodology for System-Level Analog Design Space Exploration
Proceedings of the conference on Design, automation and test in Europe - Volume 1
HOLMES: Capturing the Yield-Optimized Design Space Boundaries of Analog and RF Integrated Circuits
DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
An evolutionary approach to automatic synthesis of high-performance analog integrated circuits
IEEE Transactions on Evolutionary Computation
WATSON: design space boundary exploration and model generation for analog and RFIC design
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Deterministic approaches to analog performance space exploration (PSE)
Proceedings of the 42nd annual Design Automation Conference
Proceedings of the conference on Design, automation and test in Europe
Computation of yield-optimized Pareto fronts for analog integrated circuit specifications
Proceedings of the Conference on Design, Automation and Test in Europe
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The knowledge of optimal design space boundaries of component circuits can be extremely useful in making good subsystem-level design decisions which are aware of the parasitics and other second-order circuit-level details. However, direct application of popular Multi-objective genetic optimization algorithms were found to produce Pareto fronts with poor diversity for analog circuits problems. This work proposes a novel approach to control the diversity of solutions by paritioning the solution space, using Local Competition to promote diversity and Global competition for convergence, and by controlling the proportion of these two mechanisms by a Simulated Annealing based formulation. The algorithm was applied to extract numerical results on analog switched capacitor integrator circuits with a wide range of tight specifications. The results were found to be significantly better than traditional GA based uncontrolled optimization methods.