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

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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Rocha, Frederico A.E (Συγγραφέας), Martins, Ricardo M.F (Συγγραφέας), Lourenço, Nuno C.C (Συγγραφέας), Horta, Nuno C.G (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2014.
Σειρά:SpringerBriefs in Applied Sciences and Technology,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
LEADER 02698nam a22005175i 4500
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100 1 |a Rocha, Frederico A.E.  |e author. 
245 1 0 |a Electronic Design Automation of Analog ICs combining Gradient Models with Multi-Objective Evolutionary Algorithms  |h [electronic resource] /  |c by Frederico A.E. Rocha, Ricardo M.F. Martins, Nuno C.C. Lourenço, Nuno C.G. Horta. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2014. 
300 |a XI, 69 p. 39 illus.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
490 1 |a SpringerBriefs in Applied Sciences and Technology,  |x 2191-530X 
505 0 |a Introduction -- Related Work -- Gradient Model Generation -- Enhanced Circuit-Level Optimization Kernel -- Case Studies -- Conclusions and Outlook. 
520 |a 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. 
650 0 |a Engineering. 
650 0 |a Algorithms. 
650 0 |a Computational intelligence. 
650 0 |a Electronic circuits. 
650 1 4 |a Engineering. 
650 2 4 |a Circuits and Systems. 
650 2 4 |a Computational Intelligence. 
650 2 4 |a Algorithm Analysis and Problem Complexity. 
700 1 |a Martins, Ricardo M.F.  |e author. 
700 1 |a Lourenço, Nuno C.C.  |e author. 
700 1 |a Horta, Nuno C.G.  |e author. 
710 2 |a SpringerLink (Online service) 
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776 0 8 |i Printed edition:  |z 9783319021881 
830 0 |a SpringerBriefs in Applied Sciences and Technology,  |x 2191-530X 
856 4 0 |u http://dx.doi.org/10.1007/978-3-319-02189-8  |z Full Text via HEAL-Link 
912 |a ZDB-2-ENG 
950 |a Engineering (Springer-11647)