Electronic Design Automation of Analog ICs combining Gradient Models with Multi-Objective Evolutionary Algorithms

Nonfiction, Science & Nature, Technology, Electronics, Circuits, Computers, Advanced Computing, Artificial Intelligence
Cover of the book Electronic Design Automation of Analog ICs combining Gradient Models with Multi-Objective Evolutionary Algorithms by Frederico A.E. Rocha, Ricardo M.F. Martins, Nuno C.C. Lourenço, Nuno C.G. Horta, Springer International Publishing
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Author: Frederico A.E. Rocha, Ricardo M.F. Martins, Nuno C.C. Lourenço, Nuno C.G. Horta ISBN: 9783319021898
Publisher: Springer International Publishing Publication: September 24, 2013
Imprint: Springer Language: English
Author: Frederico A.E. Rocha, Ricardo M.F. Martins, Nuno C.C. Lourenço, Nuno C.G. Horta
ISBN: 9783319021898
Publisher: Springer International Publishing
Publication: September 24, 2013
Imprint: Springer
Language: English

This book applies to the scientific area of electronic design automation (EDA) and addresses the automatic sizing of analog integrated circuits (ICs). Particularly, this book presents an approach to enhance a state-of-the-art layout-aware circuit-level optimizer (GENOM-POF), by embedding statistical knowledge from an automatically generated gradient model into the multi-objective multi-constraint optimization kernel based on the NSGA-II algorithm. The results showed allow the designer to explore the different trade-offs of the solution space, both through the achieved device sizes, or the respective layout solutions.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This book applies to the scientific area of electronic design automation (EDA) and addresses the automatic sizing of analog integrated circuits (ICs). Particularly, this book presents an approach to enhance a state-of-the-art layout-aware circuit-level optimizer (GENOM-POF), by embedding statistical knowledge from an automatically generated gradient model into the multi-objective multi-constraint optimization kernel based on the NSGA-II algorithm. The results showed allow the designer to explore the different trade-offs of the solution space, both through the achieved device sizes, or the respective layout solutions.

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