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000923901 020__ $$a9783030357436$$q(electronic book)
000923901 020__ $$a3030357430$$q(electronic book)
000923901 020__ $$z9783030357429
000923901 035__ $$aSP(OCoLC)on1132417279
000923901 035__ $$aSP(OCoLC)1132417279
000923901 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dLQU
000923901 049__ $$aISEA
000923901 050_4 $$aTK7874
000923901 08204 $$a621.3815$$223
000923901 1001_ $$aRosa, João P. S.
000923901 24510 $$aUsing artificial neural networks for analog integrated circuit design automation /$$cJoão P. S. Rosa [and more].
000923901 260__ $$aCham :$$bSpringer,$$c2020.
000923901 300__ $$a1 online resource (117 pages).
000923901 336__ $$atext$$btxt$$2rdacontent
000923901 337__ $$acomputer$$bc$$2rdamedia
000923901 338__ $$aonline resource$$bcr$$2rdacarrier
000923901 4901_ $$aSpringerBriefs in Applied Sciences and Technology
000923901 504__ $$aIncludes bibliographical references.
000923901 506__ $$aAccess limited to authorized users.
000923901 520__ $$aThis 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 circuits 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.
000923901 588__ $$aDescription based on print version record.
000923901 650_0 $$aIntegrated circuits$$xComputer-aided design.
000923901 650_0 $$aAnalog integrated circuits$$xComputer-aided design.
000923901 7001_ $$aGuerra, Daniel J. D.
000923901 7001_ $$aHorta, Nuno C. G.
000923901 7001_ $$aMartins, Ricardo$$q(Ferreira Martins)
000923901 7001_ $$aLourenço, Nuno C. C.
000923901 77608 $$iPrint version:$$aRosa, João P. S.$$tUsing Artificial Neural Networks for Analog Integrated Circuit Design Automation$$dCham : Springer,c2020$$z9783030357429
000923901 830_0 $$aSpringerBriefs in applied sciences and technology.
000923901 852__ $$bebk
000923901 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-35743-6$$zOnline Access$$91397441.1
000923901 909CO $$ooai:library.usi.edu:923901$$pGLOBAL_SET
000923901 980__ $$aEBOOK
000923901 980__ $$aBIB
000923901 982__ $$aEbook
000923901 983__ $$aOnline
000923901 994__ $$a92$$bISE