Hierarchical constraint transformation based on genetic optimization for analog system synthesis

In a top-down analog system design methodology, the task of translating high-level performance specifications and constraints into component level parameters is termed as constraint transformation. In this paper, we presented a genetic optimization based approach to constraint transformation. The salient features of this are a search space profiling technique and a hierarchical two-level genetic optimization engine. Performance estimation for profiling and hierarchical optimization uses both the performance equations embedded in an analog performance estimation module as well as detailed SPICE simulation. We described the constraint transformation method, and each of its constituents. The effectiveness of the two-level hierarchical approach was established by comparing it against a flat non-hierarchical method. Application of the constraint transformation method in the synthesis of design examples was also presented in the paper.

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