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Machine learning in VLSI computer-aided design

Author: Ibrahim (Abe) M Elfadel; Duane S Boning; Xin Li
Publisher: Cham, Switzerland : Springer, [2019]
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Machine learning in VLSI computer-aided design.
Cham, Switzerland : Springer, [2019]
(OCoLC)1057738963
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Ibrahim (Abe) M Elfadel; Duane S Boning; Xin Li
ISBN: 9783030046668 3030046664
OCLC Number: 1090129982
Description: 1 online resource
Contents: Intro; Foreword; Acknowledgments; Contents; Contributors; About the Editors; 1 A Preliminary Taxonomy for Machine Learning in VLSI CAD; 1.1 Machine Learning Taxonomy; 1.1.1 Unsupervised, Supervised, and Semisupervised Learning; 1.1.2 Parametric and Nonparametric Methods; 1.1.3 Discriminative Versus Generative Methods; 1.2 VLSI CAD Abstraction Levels; 1.3 Organization of This Book; 1.3.1 Machine Learning for Lithography and Physical Design; 1.3.1.1 Shiely-Compact Lithographic Process Models; 1.3.1.2 Shim et al.-Mask Synthesis 1.3.1.3 Lin and Pan-Physical Verification, Mask Synthesis, and Physical Design1.3.2 Machine Learning for Manufacturing, Yield, and Reliability; 1.3.2.1 Xanthopoulos et al.-Gaussian Process for Wafer-Level Correlations; 1.3.2.2 Chen and Boning-Yield Enhancement; 1.3.2.3 Tao et al.-Virtual Probe; 1.3.2.4 Xiong et al.-Chip Testing; 1.3.2.5 Vijayan et al.-Aging Analysis; 1.3.3 Machine Learning for Failure Modeling; 1.3.3.1 Singhee-Extreme Statistics in Memories; 1.3.3.2 Kanj et al.-Fast Statistical Analysis Using Logistic Regression 1.3.3.3 Tao et al.-Fast Statistical Analysis of Rare Circuit Failures1.3.3.4 Wang-Learning from Limited Data; 1.3.4 Machine Learning for Analog Design; 1.3.4.1 Tao et al.-Bayesian Model Fusion; 1.3.4.2 Lin et al.-Sparse Relevance Kernel Machine; 1.3.4.3 Singhee-Projection Pursuit with SiLVR; 1.3.4.4 Torun et al.-Integrated Voltage Regulator Optimization and Uncertainty Quantification; 1.3.5 Machine Learning for System Design and Optimization; 1.3.5.1 Ziegler et al.-SynTunSys; 1.3.5.2 Karn and Elfadel-Multicore Power and Thermal Proxies 1.3.5.3 Vasudevan et al.-GoldMine for RTL Assertion Generation1.3.5.4 Hanif et al.-Machine Learning Architectures and Hardware Design; 1.3.6 Other Work and Outlook; References; Part I Machine Learning for Lithography and Physical Design; 2 Machine Learning for Compact Lithographic Process Models; 2.1 Introduction; 2.2 The Lithographic Patterning Process; 2.2.1 Importance of Lithographic Patterning Process to the Economics of Computing; 2.2.2 Representation of the Lithographic Patterning Process; 2.2.2.1 Mask Transfer Function; 2.2.2.2 Imaging Transfer Function 2.2.2.3 Resist Transfer Function2.2.2.4 Etch Transfer Function; 2.2.3 Summary; 2.3 Machine Learning of Compact Process Models; 2.3.1 The Compact Process Model Machine Learning Problem Statement; 2.3.1.1 The Compact Process Model Task; 2.3.1.2 The CPM Training Experience; 2.3.1.3 CPM Performance Metrics; 2.3.1.4 Summary of CPM Problem Statement; 2.3.2 Supervised Learning of a CPM; 2.3.2.1 CPM Model Form; 2.3.2.2 CPM Supervised Learning Dataset; 2.3.2.3 CPM Supervised Learning Cost Function; 2.3.2.4 CPM Supervised Learning Optimization Algorithm; 2.4 Neural Network Compact Patterning Models
Responsibility: editors, Abrahim (Abe) M. Elfadel, Duane S. Boning and Xin Li.

Abstract:

This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design. Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability; Discusses the use of machine learning techniques in the context of analog and digital synthesis; Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions; Discusses the tradeoff between the cost of collecting data and prediction accuracy and provides a methodology for using prior data to reduce cost of data collection in the design, testing and validation of both analog and digital VLSI designs. From the Foreword As the semiconductor industry embraces the rising swell of cognitive systems and edge intelligence, this book could serve as a harbinger and example of the osmosis that will exist between our cognitive structures and methods, on the one hand, and the hardware architectures and technologies that will support them, on the other ... As we transition from the computing era to the cognitive one, it behooves us to remember the success story of VLSI CAD and to earnestly seek the help of the invisible hand so that our future cognitive systems are used to design more powerful cognitive systems. This book is very much aligned with this on-going transition from computing to cognition, and it is with deep pleasure that I recommend it to all those who are actively engaged in this exciting transformation. Dr. Ruchir Puri, IBM Fellow, IBM Watson CTO & Chief Architect, IBM T.J. Watson Research Center.

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