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This book provides comprehensive coverage of the recent advances in symbolic analysis techniques for design automation of nanometer VLSI systems. The presentation is organized in parts of fundamentals, basic implementation methods and applications for VLSI design. Topics emphasized include statistical timing and crosstalk analysis, statistical and parallel analysis, performance bound analysis and behavioral modeling for analog integrated circuits. Among the recent advances, the Binary Decision Diagram (BDD) based approaches are studied in depth. The BDD-based hierarchical symbolic analysis approaches, have essentially broken the analog circuit size barrier.

This book provides comprehensive coverage of the recent advances in symbolic analysis techniques for design automation of nanometer VLSI systems. The presentation is organized in parts of fundamentals, basic implementation methods and applications for VLSI design. Topics emphasized include  statistical timing and crosstalk analysis, statistical and parallel analysis, performance bound analysis and behavioral modeling for analog integrated circuits. Among the recent advances, the Binary Decision Diagram (BDD) based approaches are studied in depth. The BDD-based hierarchical symbolic analysis approaches, have essentially broken the analog circuit size barrier. In particular, this book    Provides an overview of classical symbolic analysis methods and a comprehensive presentation on the modern  BDDbased symbolic analysis techniques;  Describes detailed implementation strategies for BDDbased algorithms, including the principles of zerosuppression, variable ordering and canonical reduction;  Introduces the two  successful BDDbased symbolic analysis algorithms, Determinant Decision Diagrams (DDD) and GraphPair Decision Diagrams (GPDD);  Discusses statistical timing and crosstalk analysis methods based on symbolic moment computation;  Includes an application of the DDD algorithm to symbolic performance bound estimations of analog circuits subject to process variations;  Presents an application of the DDD algorithm to fast parallel Monte Carlo statistical analysis with an implementation on a popular GPU platform.