Extreme Statistics in Nanoscale Memory Design [electronic resource] / edited by Amith Singhee, Rob A. Rutenbar.

Extreme Statistics in Memories -- Statistical Nano CMOS Variability and Its Impact on SRAM -- Importance Sampling-Based Estimation: Applications to Memory Design -- Direct SRAM Operation Margin Computation with Random Skews of Device Characteristics -- Yield Estimation by Computing Probabilistic Hypervolumes -- Most Probable Point-Based Methods -- Extreme Value Theory: Application to Memory Statistics.

Extreme Statistics in Nanoscale Memory Design brings together some of the world’s leading experts in statistical EDA, memory design, device variability modeling and reliability modeling, to compile theoretical and practical results in one complete reference on statistical techniques for extreme statistics in nanoscale memories. The work covers a variety of techniques, including statistical, deterministic, model-based and non-parametric methods, along with relevant description of the sources of variations and their impact on devices and memory design. Specifically, the authors cover methods from extreme value theory, Monte Carlo simulation, reliability modeling, direct memory margin computation and hypervolume computation. Ideas are also presented both from the perspective of an EDA practitioner and a memory designer to provide a comprehensive understanding of the state-of -the-art in the area of extreme statistics estimation and statistical memory design. Extreme Statistics in Nanoscale Memory Design is a useful reference on statistical design of integrated circuits for researchers, engineers and professionals.