Using Artificial Neural Networks for Analog Integrated Circuit Design Automation (Record no. 78733)

000 -LEADER
fixed length control field 04105nam a22005775i 4500
001 - CONTROL NUMBER
control field 978-3-030-35743-6
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220801220609.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 191211s2020 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783030357436
-- 978-3-030-35743-6
082 04 - CLASSIFICATION NUMBER
Call Number 621.3815
100 1# - AUTHOR NAME
Author Rosa, João P. S.
245 10 - TITLE STATEMENT
Title Using Artificial Neural Networks for Analog Integrated Circuit Design Automation
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2020.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XVIII, 101 p.
490 1# - SERIES STATEMENT
Series statement SpringerBriefs in Applied Sciences and Technology,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Introduction -- Related Work -- Overview of Artificial Neural Networks (ANNs) -- On the Exploration of Promising Analog IC Designs via ANNs -- ANNs as an Alternative for Automatic Analog IC Placement -- Conclusions. .
520 ## - SUMMARY, ETC.
Summary, etc 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. .
700 1# - AUTHOR 2
Author 2 Guerra, Daniel J. D.
700 1# - AUTHOR 2
Author 2 Horta, Nuno C. G.
700 1# - AUTHOR 2
Author 2 Martins, Ricardo M. F.
700 1# - AUTHOR 2
Author 2 Lourenço, Nuno C. C.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-030-35743-6
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2020.
336 ## -
-- text
-- txt
-- rdacontent
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-- computer
-- c
-- rdamedia
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-- online resource
-- cr
-- rdacarrier
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-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Electronic circuits.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Signal processing.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational intelligence.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Electronic Circuits and Systems.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Signal, Speech and Image Processing .
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational Intelligence.
700 1# - AUTHOR 2
-- (orcid)0000-0002-1687-1447
-- https://orcid.org/0000-0002-1687-1447
700 1# - AUTHOR 2
-- (orcid)0000-0002-8251-1415
-- https://orcid.org/0000-0002-8251-1415
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 2191-5318
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-- ZDB-2-ENG
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-- ZDB-2-SXE

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