Machine learning in VLSI computer-aided design. ([2019])

• Record Type: Book
• Further Information: Note: Editors, Abrahim (Abe) M. Elfadel, Duane S. Boning and Xin Li.
• Editors: Elfadel, Ibrahim (Abe) M; Boning, Duane S; Li, Xin
• 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
• Publisher Details: Cham, Switzerland : Springer
• Publication Date: 2019
• Extent: 1 online resource
• Subjects: 621.395; Integrated circuits -- Very large scale integration -- Computer-aided design; Machine learning; TECHNOLOGY & ENGINEERING / Mechanical; Electronic books; Electronic books
• Languages: English
• ISBNs: 9783030046668; 3030046664
• Related ISBNs: 9783030046651; 3030046656
• Notes: Note: Includes bibliographical references and index.; Note: Online resource; title from PDF title page (EBSCO, viewed March 20, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.