Foundations and TrendsŪ in Electronic Design Automation
STATISTICAL PERFORMANCE MODELING AND OPTIMIZATION
by Xin Li (Carnegie Mellon University, USA), Jiayong Le (Extreme DA, USA) & Lawrence T Pileggi (Carnegie Mellon University, USA)
Statistical Performance Modeling and Optimization reviews various statistical methodologies that have been recently developed to model, analyze and optimize performance variations at both transistor level and system level in integrated circuit (IC) design. The following topics are discussed in detail: sources of process variations, variation characterization and modeling, Monte Carlo analysis, response surface modeling, statistical timing and leakage analysis, probability distribution extraction, parametric yield estimation and robust IC optimization. These techniques provide the necessary CAD infrastructure that facilitates the bold move from deterministic, corner-based IC design toward statistical and probabilistic design.
Statistical Performance Modeling and Optimization reviews and compares different statistical IC analysis and optimization techniques, and analyzes their trade-offs for practical industrial applications. It serves as a valuable reference for researchers, students and CAD practitioners.
Published by Now Publishers and marketed by World Scientific
Contents:
- Introduction
- Process Variations
- Transistor-Level Statistical
Methodologies
- System-Level Statistical Methodologies
- Robust Design of Future ICs
- Acknowledgments
- References
Readership: Postgraduates, researchers and professionals.
| 160pp |
Pub. date: Aug 2007 |
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