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1 -This paper presents a new design automation tool based on a modified genetic algorithm kernel, in order to increase efficiency on the analog circuit and system design cycle. It combines a robust optimization with corners, machine learning modeling and distributed processing capability able to deal with multi-objective and highly constrained optimization problems. The resulting optimization tool, simulation capabilities, and extensible architecture are presented and the improvement in design productivity is demonstrated for the design of robust CMOS operational amplifiers.

An efficient use of macromodeling techniques is pointed out as an effective approach to improve the convergence and speed of the optimization process. The methodology presented in this paper is based on a learning scheme using Support Vector Machines (SVMs) that together with and an evolutionary strategy is used to create efficient models to estimate and optimize the performance parameters of analog and mixed-signal ICs. The SVM is used to identify the feasible design space regions while at the same time the evolutionary techniques are looking for the global optimum. Finally, the proposed optimization based methodology is demonstrated for the design of a well known class of CMOS operational amplifier topologies. The efficiency of the proposed approach is compared with standard and modified genetic algorithm kernels.