Nominal-Yield-Area Tradeoff in Automatic Synthesis of Analog Circuits: A Genetic Programming Approach using Immune-Inspired Operators

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Conca:2009:AHS,

• author = "Piero Conca and Giuseppe Nicosia and Giovanni Stracquadanio and Jon Timmis",
• title = "Nominal-Yield-Area Tradeoff in Automatic Synthesis of Analog Circuits: A Genetic Programming Approach using Immune-Inspired Operators",
• booktitle = "NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2009)",
• year = "2009",
• editor = "Tughrul Arslan and Didier Keymeulen",
• pages = "399--406",
• address = "San Francisco, California, USA",
• month = jul # " 29-" # aug # " 1",
• keywords = "genetic algorithms, genetic programming, AIS, ElP, Pareto Front, analog circuit automatic synthesis, analog circuit design, circuit reliability, elitist immune programming, evolutionary algorithm, frequency response, genetic programming approach, immune-inspired operators, industrial components series, low-pass filter synthesis, nominal-yield-area tradeoff, Pareto optimisation, analogue circuits, circuit CAD, circuit reliability, frequency response, low-pass filters",
• DOI = "doi:10.1109/AHS.2009.32",
• abstract = "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).",
• notes = "Co-located with Design Automation Conference (DAC-2009) http://www.see.ed.ac.uk/~ahs2009/ Also known as \cite{5325428}",

}

Genetic Programming entries for Piero Conca Giuseppe Nicosia Giovanni Stracquadanio Jon Timmis

Citations