Machine Learning in VLSI Computer-Aided Design (Hardcover, 2019)

Chapter1: A Preliminary Taxonomy for Machine Learning in VLSI CAD.- Chapter2: Machine Learning for Compact Lithographic Process Models.- Chapter3: Machine Learning for Mask Synthesis.- Chapter4: Machine Learning in Physical Verification, Mask Synthesis, and Physical Design.- Chapter5: Gaussian Process-Based Wafer-Level Correlation Modeling and its Applications.- Chapter6: Machine Learning Approaches for IC Manufacturing Yield Enhancement.- Chapter7: Efficient Process Variation Characterization by Virtual Probe.- Chapter8: Machine learning for VLSI chip testing and semiconductor manufacturing process monitoring and improvement.- Chapter9: Machine Learning based Aging Analysis.- Chapter10: Extreme Statistics in Memories.- Chapter11: Fast Statistical Analysis Using Machine Learning.- Chapter12: Fast Statistical Analysis of Rare Circuit Failure Events.- Chapter13: Learning from Limited Data in VLSI CAD.- Chapter14: Large-Scale Circuit Performance Modeling by Bayesian Model Fusion.- Chapter15: Sparse Relevance Kernel Machine Based Performance Dependency Analysis of Analog and Mixed-Signal Circuits.- Chapter16: SiLVR: Projection Pursuit for Response Surface Modeling.- Chapter17: Machine Learning based System Optimization and Uncertainty Quantification of Integrated Systems.- Chapter18: SynTunSys: A Synthesis Parameter Autotuning System for Optimizing High-Performance Processors.- Chapter19: Multicore Power and Thermal Proxies Using Least-Angle.- Chapter20: A Comparative Study of Assertion Mining Algorithms in GoldMine.- Chapter21: Energy-Efficient Design of Advanced Machine Learning Hardware.