Statistical Performance Modeling and Optimization
By Xin Li, Department of ECE, Carnegie Mellon University, USA, xinli@ece.cmu.edu | Jiayong Le, Extreme DA, 165 University Avenue, USA, kelvin@extreme-da.com | Lawrence T. Pileggi, Department of ECE, Carnegie Mellon University, USA, pileggi@ece.cmu.edu
Abstract
As IC technologies scale to finer feature sizes, it becomes increasingly difficult to control the relative process variations. The increasing fluctuations in manufacturing processes have introduced unavoidable and significant uncertainty in circuit performance; hence ensuring manufacturability has been identified as one of the top priorities of today's IC design problems. In this paper, we review various statistical methodologies that have been recently developed to model, analyze, and optimize performance variations at both transistor level and system level. The following topics will be 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
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.
