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

SBAST: ELC DSGN ATMT ANLG ICS CMB GD MDL ML OBJ EVL ALG

Information Technology

Computing & Information Technology

Gt Product Group

Binding Format

Marketing Imprint Name

B2B GT

CL Carton Count

Imprint Name

B2B & B2C

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.

POST SECONDARY FOUR YEAR

COLFR

COLJR

COLSO

COLSR

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Other Frontmatter.
Title Page.
Copyright Page.
Dedication.
Preface.
Contents.
Abbreviations.
1: Introduction.
2: State-of-the-Art on Automatic Analog IC Sizing.
3: Gradient Model Generation.
4: Enhanced AIDA&#x0027;s Circuit-Level Optimization Kernel.
5: Results.
6: Conclusions and Future Work.

Electronic Design Automation of Analog ICs combining Gradient Models with Multi-Objective Evolutionary Algorithms.css-1p8rvzy{position:absolute;padding-left:8px;}

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