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Engineering - Circuits & Systems | Extreme Statistics in Nanoscale Memory Design

Extreme Statistics in Nanoscale Memory Design

Singhee, Amith, Rutenbar, Rob A. (Eds.)

2010, X, 246 p.

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  • Includes a treatment of memory design from the perspective of statistical analysis
  • Covers relevant theoretical background from other fields: statistics, machine learning, optimization, reliability
  • Explains the problem of estimating statistics of memory performance variation
  • Shows solutions recently proposed in the Electronic Design Automation (EDA) community
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.

Content Level » Research

Keywords » CMOS - Device Variability Modeling - EVT - Extreme Value Theory - Memory Design - Nanoscale VLSI - Sampling-Based Estimation - VLSI - design automation - electronic design automation - integrated circuit - material

Related subjects » Circuits & Systems - Electronics & Electrical Engineering

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