Multi-objective optimization of doping profile in semiconductor design
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Fault detection in analog circuits using a fuzzy dendritic cell algorithm
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Open-ended evolution to discover analogue circuits for beyond conventional applications
Genetic Programming and Evolvable Machines
PAGE: parallel agile genetic exploration towards utmost performance for analog circuit design
Proceedings of the Conference on Design, Automation and Test in Europe
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The synthesis of analog circuits is a complex and expensive task; whilst there are various approaches for the synthesis of digital circuits, analog design is intrinsically more difficult since analog circuits process voltages in a continuous range. In the field of analog circuit design, the genetic programming approach has received great attention, affording the possibility to design and optimize a circuit at the same time. However, these algorithms have limited industrial relevance, since they work with ideal components. Starting from the well known results of Koza and co-authors, we introduce a new evolutionary algorithm, called elitist Immune Programming (EIP), that is able to synthesize an analog circuit using industrial components series in order to produce reliable and low cost circuits. The algorithm has been used for the synthesis of low-pass filters; the results were compared with the genetic programming, and the analysis shows that EIP is able to design better circuits in terms of frequency response and number of components. In addition we conduct a complete yield analysis of the discovered circuits, and discover that EIP circuits attain a higher yield than the circuits generated via a genetic programming approach, and, in particular, the algorithm discovers a Pareto Front which respects nominal performance (sizing), number of components (area) and yield (robustness).