Electronic Design Automation of Analog ICs combining Gradient Models with Multi-Objective Evolutionary Algorithms [electronic resource] / by Frederico A.E. Rocha, Ricardo M.F. Martins, Nuno C.C. Lourenço, Nuno C.G. Horta.

By:

Rocha, Frederico A.E [author.]

Contributor(s):

Series: SpringerBriefs in Applied Sciences and TechnologyPublisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: XI, 69 p. 39 illus. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783319021898

Subject(s):

Genre/Form:

Electronic books.

Additional physical formats: Printed edition:: No titleDDC classification:

621.3815 23

LOC classification:

TK7888.4

Online resources:

Click here to access online

Contents:

Introduction -- Related Work -- Gradient Model Generation -- Enhanced Circuit-Level Optimization Kernel -- Case Studies -- Conclusions and Outlook.

In: Springer eBooksSummary: 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.

Item type:

eBooks

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Introduction -- Related Work -- Gradient Model Generation -- Enhanced Circuit-Level Optimization Kernel -- Case Studies -- Conclusions and Outlook.

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.