Optimization of analog circuits by applying evolutionary algorithms
IVICK GUERRA GOMEZ
Unlike automated digital design, analog circuit designers require experience to develop skills, and to avoid spending a lot of time understanding all the aspects involved around a specific design such as nonlinearities, parasitics, performances, trade-offs, etc. The continuing size reduction of electronic devices along with their shorter life cycle imposes design challenges to discover or to optimize the performances of modern electronic systems; such as: wireless services, telecom, mobile computing and media applications. Fortunately, those design challenges can be overcome through the development of Electronic Design Automation (EDA) tools. In the analog domain, circuit optimization tools have demonstrated their usefulness in addressing design problems taking into account downscaling technological aspects. However, those EDA tools still have the lack of taking into account some design constraints when applied to a multiple objective design problem. On the one hand, Evolutionary Algorithms (EAs) have demonstrated their suitability in solving nonlinear multi-objective design problems with multiple constraints, providing a set of feasible design solutions from which several insights and trade-offs among the circuit performance objectives can be deduced. On the other hand, still the application of EAs in optimizing the biases and sizes of analog circuits, have hard shortcomings to be improved, for instance: guarantee of convergence, run-time and incorporation of variation aware techniques. This Thesis is focused on the application of multi-objective EAs in the optimization of analog circuits including nanometer technology. The EAs have been programmed to work with different genetic operators over different kinds of circuit objectives, variables and design technologies.
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