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  2. Elektronik teknik
  3. Elektronik: kredse og komponenter

Using Artificial Neural Networks for Analog Integrated Circuit Design Automation

Af: Nuno C. C. Lourenco, Ricardo M. F. Martins, Nuno C. G. Horta, Joao P. S. Rosa, Daniel J. D. Guerra Engelsk Paperback

Using Artificial Neural Networks for Analog Integrated Circuit Design Automation

Af: Nuno C. C. Lourenco, Ricardo M. F. Martins, Nuno C. G. Horta, Joao P. S. Rosa, Daniel J. D. Guerra Engelsk Paperback
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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. 
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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. 
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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 101
ISBN-13: 9783030357429
Indbinding: Paperback
Udgave:
ISBN-10: 3030357422
Kategori: Elektronik: kredse og komponenter
Udg. Dato: 2 jan 2020
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer Nature Switzerland AG
Oplagsdato: 2 jan 2020
Forfatter(e): Nuno C. C. Lourenco, Ricardo M. F. Martins, Nuno C. G. Horta, Joao P. S. Rosa, Daniel J. D. Guerra
Forfatter(e) Nuno C. C. Lourenco, Ricardo M. F. Martins, Nuno C. G. Horta, Joao P. S. Rosa, Daniel J. D. Guerra

Kategori Elektronik: kredse og komponenter

ISBN-13 9783030357429

Sprog Engelsk

Indbinding Paperback

Sider 101

Udgave

Længde 0mm

Bredde 155mm

Højde 235mm

Udg. Dato 2 jan 2020

Oplagsdato 2 jan 2020

Forlag Springer Nature Switzerland AG

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