04262nam 22006735 450 991036661070332120200702064043.03-030-35743-010.1007/978-3-030-35743-6(CKB)5280000000190096(MiAaPQ)EBC5995823(DE-He213)978-3-030-35743-6(PPN)24281932X(EXLCZ)99528000000019009620191211d2020 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierUsing Artificial Neural Networks for Analog Integrated Circuit Design Automation /by João P. S. Rosa, Daniel J. D. Guerra, Nuno C. G. Horta, Ricardo M. F. Martins, Nuno C. C. Lourenço1st ed. 2020.Cham :Springer International Publishing :Imprint: Springer,2020.1 online resource (117 pages)SpringerBriefs in Applied Sciences and Technology,2191-530X3-030-35742-2 Introduction -- Related Work -- Overview of Artificial Neural Networks (ANNs) -- On the Exploration of Promising Analog IC Designs via ANNs -- ANNs as an Alternative for Automatic Analog IC Placement -- Conclusions. .This book addresses the automatic sizing and layout of analog integrated circuits (ICs) using deep learning (DL) and artificial neural networks (ANN). It explores an innovative approach to automatic circuit sizing where ANNs learn patterns from previously optimized design solutions. In opposition to classical optimization-based sizing strategies, where computational intelligence techniques are used to iterate over the map from devices’ sizes to circuits’ performances provided by design equations or circuit simulations, ANNs are shown to be capable of solving analog IC sizing as a direct map from specifications to the devices’ sizes. Two separate ANN architectures are proposed: a Regression-only model and a Classification and Regression model. The goal of the Regression-only model is to learn design patterns from the studied circuits, using circuit’s performances as input features and devices’ sizes as target outputs. This model can size a circuit given its specifications for a single topology. The Classification and Regression model has the same capabilities of the previous model, but it can also select the most appropriate circuit topology and its respective sizing given the target specification. The proposed methodology was implemented and tested on two analog circuit topologies. .SpringerBriefs in Applied Sciences and Technology,2191-530XElectronic circuitsSignal processingImage processingSpeech processing systemsComputational intelligenceCircuits and Systemshttps://scigraph.springernature.com/ontologies/product-market-codes/T24068Signal, Image and Speech Processinghttps://scigraph.springernature.com/ontologies/product-market-codes/T24051Computational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Electronic circuits.Signal processing.Image processing.Speech processing systems.Computational intelligence.Circuits and Systems.Signal, Image and Speech Processing.Computational Intelligence.621.3815621.3815Rosa João P. Sauthttp://id.loc.gov/vocabulary/relators/aut1061375Guerra Daniel J. Dauthttp://id.loc.gov/vocabulary/relators/autHorta Nuno C. Gauthttp://id.loc.gov/vocabulary/relators/autMartins Ricardo M. Fauthttp://id.loc.gov/vocabulary/relators/autLourenço Nuno C. Cauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910366610703321Using Artificial Neural Networks for Analog Integrated Circuit Design Automation2518625UNINA