Machine Learning in VLSI Computer-Aided Design

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Machine Learning in VLSI Computer-Aided Design。2019年新书,介绍了机器学习在计算机辅助VLSI设计中的应用。
Ibrahim(Abe)M. Elfadel Duane s. Boning Xin li Editors Machine Learning in VLSi Computer-Aided Design Springer editors Ibrahim(Abe)M. Elfadel Duane s. Boning Department of electrical Massachusetts Institute of Technology and Computer Engineering Cambridge MA USA and Center for Cyber Physical Systems Khalifa university, abu Dhabi, UAE Department of Electrical and Computer Engineering Duke University, durham, NC, USA ISBN978-3-030-04665-1 ISBN978-3-030-04666-8( eBook) Library of Congress Control Number: 2019930838 o Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland To Shaza, Adam, Ella, and lily abe Peggy, Will, and lane To Karen and Thomas Xi Tell me and l forget Teach me and remember Involve me and l learn Ben franklin Foreword As an active branch of applied computer science, the field of VlsI computer-aided design (VlSi CAD) has always been at the technological forefront in incorporating cutting-edge algorithms in the software tools and methodologies that electronics engineers have used to weave the digital fabric of our world This book amply demonstrates that in line with its historical track record, VlsI CAD has also been at the leading edge in making good use of machine-learning technologies to further automate the design, verification, and implementation of the most advanced chips. Machine learning and vlsI CAd have in common several main characteristics that may have greatly facilitated their interlock. The first is that they are both consumers of Big Data. In fact, Moores law has essentially guaranteed that chip data grow exponentially big to the point that having tens of billions of transistors in a chip is now so common and almost taken for granted. The second characteristic that they have in common is a structured approach for controlling complexity. In machine learning, this approach is most apparent in the use of layered networks as inference and generalization engines. In VLSI CAD, complexity is controlled through a well-defined abstraction hierarchy, going from the transistor and its technology as raw data to the chip architecture as a model of processing and computation The third common characteristic of the two fields is their focus on computational efficiency, be it to shorten turn-around time in chip design, as is the case in VLSI CAD, or to promptly detect patterns in time series as is the case in mission-critical cloud analytics. The fourth common characteristic is a focus on automated optimization and synthesis that VlSI CAD has spearheaded, and synthesis is now becoming an important trend for the design of neural networks in machine learning as well It is therefore almost natural to think of VLSI CAD engineers as the original data scientists who have been instrumental not only in dealing with big data in the context of chip design but also in enabling the very chips that have ushered the Big Data era and made it a social and business reality The various chapters of this timely and comprehensive book should give the reader a thorough understanding of the degree to which machine learning methods Foreword have percolated into the various layers of the chip design hierarchy. From lithogra- y and physical design to logic and system design, and from circuit performance estimation to manufacturing yield prediction, VLSI CAD researchers have already brought state-of-the-art algorithms from supervised, unsupervised, and statistical learning to bear on pressing CAD problems such as hotspot detection, design-space exploration, efficient test generation, and post-silicon measurement minimization Machine learning in VLSI CAD is expected to play an increasingly important role not only in improving the quality of the models used in individual Cad tools but also in enhancing the quality of chip designs that result from the execution of entire CAD flows and methodologies As the semiconductor industry embraces the rising swell of cognitive systems and edge intelligence, this book could serve as a harbinger and an 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 The value proposition of automation is that it compresses schedules, reduces costs. and eliminates human errors. In the case of VLsi CAD. the automation has achieved not only these objectives but also the infinitely more important outcome of a seamless implementation of a positive feedback loop whereby computers are used to design more powerful computers. This positive feedback loop is the invisible hand of moore’slaw. 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 ongoing 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 IBMT. Watson research center Dr. Ruchir puri Yorktown Heights, NY, USA IBM Fellow. IBM Watson August 2018 cto chief architect Acknowledgments We would like to acknowledge and thank the many reviewers who have helped us in getting this book to its present state by closely reading the early versions of its chapters and sharing their valuable comments, through us, with the chapter authors Their inputs were instrumental in improving the overall quality of the entire book In alphabetical order, they are Bei Yu, Bowen Zhang, Christopher Lang, Haibao Chen, Handi Yu, Hector Gonzalez diaz, Hongge Chen, Jun Tao, Mark Po-Hung Lin, Nguyen Manh Cuong, Pinggiang Zhou, and Renjian Pan We also acknowledge the early LateX technical support we received from Shahzad Muzaffar as well as the editorial advice and guidance Charles glaser and the Springer Team provided us with The first editor would like to acknowledge the ibm t.. watson Research Center, Yorktown Heights, NY, for hosting him on his research leave in Summer 2018 during which the composition of this book was finalized Of course, a book of such scope and relevance would not have been possible without the timely contributions of all its authors. To them go our warmest thanks and deepest gratitude Abu dhabi UAE Ibrahim(abe)M. Elfadel Cambridge, MA, USA Duane s boning Durham. NC. USA Xin li August 2018 Contents 1 A Preliminary Taxonomy for Machine Learning in VLSI CAD Duane S Boning, Ibrahim(Abe)M. Elfadel, and Xin Li 1. 1 Machine Learning taxonomy 1. 2 VLSI CAD AbStraction levels 3 Organization of This Book............... 6 Re eference Part I Machine learning for Lithography and Physical Design 2 Machine Learning for Compact Lithographic Process Models ... 19 J. P. Shiely Introduction 19 2.2 The lithographic Patterning process 20 2.3 Machine Learning of Compact Process Models 27 2.4 Neural Network Compact Patterning models 2.5 Conclusions . ........................................................66 References 66 3 Machine Learning for Mask Synthesis Seongbo Shim, Suhyeong Choi, and Youngsoo Shin 3.1 Introduction 3.2 Machine learning- Guided OPc.…………,70 3.3 Machine Learning- Guided epc∴….………….78 3.4 Conclusions............................. 91 References 92 4 Machine Learning in Physical Verification, Mask Synthesis and Physical Design 95 Yibo lin and david z. pan 4.1 Introduction 4.2 Machine Learning in Physical Verification 96 4.3 Machine Learning in Mask Synthesis............... 101 Xll Contents 4.4 Machine Learning in Physical Design 106 4.5 Conclusions ........................................................112 References 113 Part II Machine Learning for Manufacturing, Yield and reliability 5 Gaussian Process-Based Wafer-Level Correlation Modeling and Its Applications 119 Constantinos Xanthopoulos, Ke Huang, Ali Ahmadi, Nathan Kupp, John carulli. Amit nahar. Bob Orr. michael pass and Yiorgos makris 119 5.2 Gaussian Process-Based regression models 123 5.3 Applications 145 5.4 Conclusions References 172 6 Machine Learning Approaches for IC Manufacturing Yield Enhancement 175 Hongge Chen and Duane s. Boning 6.1 Introduction ……175 6.2 Background of the manufacturing Process 177 6 Preliminaries 6.4 Learning Models. ...................................................185 6.5 Experimental Results 191 6.6 Conclusions 198 References 199 7 Efficient Process Variation Characterization by virtual Probe 201 Jun Tao, Wangyang Zhang, Xin Li, Frank Liu, Emrah Acar Rob A. Rutenbar, Ronald D. Blanton, and Xuan Zeng 7.1 Introduction 7.2 Virtual Probe 203 7.3 Implementation details 212 7.4 Applications of Virtual Probe 219 7.5 Numerical Experiments ....................... 221 7. 6 Conclusions .230 References.................................. 230 8 Machine Learning for vlsI Chip Testing and semiconductor Manufacturing Process Monitoring and Improvement 233 Jinjun Xiong, Yada zhu, and Jingrui He 8.1 Introduction 233 8.2 Background 234 8.3 Machine Learning for Chip Testing and Yield Optimization.. 236 8.4 Hierarchical Multitask Learning for Wafer Quality Prediction.. 247


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