Statistical aging analysis with process variation consideration
Proceedings of the International Conference on Computer-Aided Design
Process variation aware performance modeling and dynamic power management for multi-core systems
Proceedings of the International Conference on Computer-Aided Design
Efficient parametric yield estimation of analog/mixed-signal circuits via Bayesian model fusion
Proceedings of the International Conference on Computer-Aided Design
Proceedings of the 50th Annual Design Automation Conference
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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.