Model-building optimization

Computer-aided design and analysis of circuits and semiconductor – Integrated circuit design processing – Optimization

Reexamination Certificate

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C716S100000, C716S103000, C716S106000

Reexamination Certificate

active

08006220

ABSTRACT:
A method and system for performing multi-objective optimization of a multi-parameter design having several variables and performance metrics. The optimization objectives include the performance values of surrogate models of the performance metrics and the uncertainty in the surrogate models. The uncertainty is always maximized while the performance metrics can be maximized or minimized in accordance with the definitions of the respective performance metrics. Alternatively, one of the optimization objectives can be the value of a user-defined cost function of the multi-parameter design, the cost function depending from the performance metrics and/or the variables. In this case, the other objective is the uncertainty of the cost function, which is maximized. The multi-parameter designs include electrical circuit designs such as analog, mixed-signal, and custom digital circuits.

REFERENCES:
patent: 2008/0234979 (2008-09-01), Costiner et al.
patent: 2009/0228846 (2009-09-01), McConaghy et al.
Jones et al., “Efficient Global Optimization”, J. Global Optimization, vol. 13, 1998, pp. 455-592.
Knowles, “ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems” , IEEE Transactions on Evolutionary Computation, vol. 10, No. 1, Feb. 2005, pp. 50-66.
Deb et al., “A fast and elitist multi-objective genetic algorithm: NSGA-II”. IEEE Transaction on Evolutionary Computation, 6(2), 2002, 181-197.

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