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Electronic design automation of analog ICs comibining gradient models with multi-objective evolutionary alogirthms

Author: Frederico A E Rocha
Publisher: Cham : Springer, 2014]
Series: SpringerBriefs in applied sciences and technology., Computational intelligence.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:

This book applies to the scientific area of electronic design automation (EDA) and addresses the automatic sizing of analog integrated circuits (ICs).

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Additional Physical Format: Print version:
Rocha, Frederico A. E.
Electronic design automation of analog ICs comibining gradient models with multi-objective evolutionary alogirthms.
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Frederico A E Rocha
ISBN: 3319021893 9783319021898
OCLC Number: 895704791
Description: 1 online resource.
Contents: Machine generated contents note: 1. Introduction --
1.1. Analog IC Design --
1.2. Analog IC Design Automation Flow --
1.3. Research Contributions --
1.4. Conclusions --
References --
2. State-of-the-Art on Automatic Analog IC Sizing --
2.1. Automatic Circuit-Level Sizing --
2.1.1. Knowledge-Based Sizing --
2.1.2. Optimization-Based Sizing --
2.2. Motivation for Model-Based Optimization --
2.3. Conclusions --
References --
3. Gradient Model Generation --
3.1. Overview of Design of Experiments (DOE) --
3.2. Design of Experiments with Full Factorial Design --
3.2.1. Characterization and Construction of the DOE Matrix --
3.2.2. Analysis of the DOE Matrix --
3.3. Design of Experiments with Fractional Factorial Design --
3.3.1. Characterization and Construction of the DOE Matrix --
3.3.2. Analysis of the DOE Matrix --
3.4. Extraction of the Gradient Model from DOE --
3.5. Conclusions --
References --
4. Enhanced AIDA's Circuit-Level Optimization Kernel --
4.1. Architecture --
4.1.1. Inputs --
4.1.2. Optimization Problem Formulation --
4.1.3. Outputs --
4.2. Integration of the Gradient Model in the Optimization Kernel --
4.2.1. Gradient Model Applied to the Crossover Operator --
4.2.2. Gradient Model Applied to the Mutation Operator --
4.3. Graphical User Interface (GUI) --
4.4. Conclusions --
References --
5. Results --
5.1. POFs Analysis --
5.2. Circuit Under Test: Single-Ended Folded Cascode Amplifier --
5.3. Case Study I: 15 Input Variables --
5.3.1. GENOM-POF --
5.3.2. GENOM-POFGM --
5.3.3. Random Model --
5.3.4. Comparison of Different Optimization/Sizing Approaches --
5.4. Case Study II: 12 Input Variables --
5.4.1. GENOM-POF --
5.4.2. GENOM-POFGM --
5.4.3. Comparison of Different Optimization/Sizing Approaches --
5.5. Conclusions --
Reference --
6. Conclusions and Future Work --
6.1. Conclusions --
6.2. Future Work.
Series Title: SpringerBriefs in applied sciences and technology., Computational intelligence.
Responsibility: Frederico A.E. Rocha [and 3 others].

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