Anaconda: Simulation-based synthesis of analog circuits via stochastic pattern search

Analog synthesis tools have traditionally traded quality for speed, substit uting simplified circuit evaluation methods for full simulation in order to accelerate the numerical search for solution candidates. As a result, thes e tools have failed to migrate into mainstream use primarily because of dif ficulties in reconciling the simplified models required for synthesis with the industrial-strength simulation environments required for validation. We argue that for synthesis to be practical, it is essential to synthesize a circuit using the same simulation environment created to validate the circu it. In this paper, we develop a new numerical search algorithm efficient en ough to allow full circuit simulation of each circuit candidate, and robust enough to find good solutions for difficult circuits. The method combines the population-of-solutions ideas from evolutionary algorithms with a novel variant of pattern search, and supports transparent network parallelism. C omparison of several synthesized cell-level circuits against manual industr ial designs demonstrates the utility of the approach.