Elfadel I.M. et al. (Eds.) Machine Learning in VLSI Computer-Aided Design

Springer, 2019. — 697 p. — ISBN 978-3-030-04665-1.
Eds.: Ibrahim (Abe) M. Elfadel, Duane S. Boning, Xin Li.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.