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Title Extreme statistics in nanoscale memory design / editors, Amith Singhee, Rob A. Rutenbar
Published New York ; London : Springer, 2010

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Description 1 online resource
Series Integrated circuits and systems, 1558-9412
Integrated circuits and systems. 1558-9412
Contents Cover -- Extreme Statistics in Nanoscale Memory Design -- Preface -- Contents -- Contributors -- Chapter 1: Introduction -- 1.1 Yield-Driven Design: Need for Accurate Yield Estimation -- 1.2 The Case of High-Replication Circuits -- 1.3 Why Does Standard Monte Carlo Not Work? -- 1.3.1 Process Variation Statistics: Prerequisites for Statistical Analysis -- 1.3.2 Monte Carlo Simulation -- 1.3.3 The Problem with Memories -- 1.4 An Overview of This Book -- References -- Chapter 2: Extreme Statistics in Memories -- 2.1 Cell Failure Probability: An Extreme Statistic -- 2.1.1 Units of Failure Probability -- 2.1.2 An Example of Extreme Statistics in Memories -- 2.2 Incorporating Redundancy -- 2.2.1 The Poisson Yield Model -- Reference -- Chapter 3: Statistical Nano CMOS Variability and Its Impact on SRAM -- 3.1 Introduction -- 3.2 Process Variability Classification -- 3.3 Sources of Statistical Variability -- 3.4 Statistical Aspects of Reliability -- 3.5 Gate Leakage Variability -- 3.6 Simulation of Statistical Variability -- 3.7 Simulation of Statistical Reliability -- 3.8 Variability in Future Technology Generations -- 3.9 Compact Model Strategies for Statistical Variability -- 3.10 Basics of Statistical Circuit Simulations in the Presence of Statistical Variability -- 3.11 Conclusions -- References -- Chapter 4: Importance Sampling-Based Estimation -- 4.1 Introduction -- 4.2 Statistical Sampling -- 4.2.1 Probability and Statistics: A History in the Making -- 4.2.2 Overview of Sampling Methods -- 4.2.3 Random Sample Generation -- 4.3 Monte Carlo Methods -- 4.3.1 The Beginning -- 4.3.2 Numerical Integration -- 4.3.3 Statistical Inference -- 4.3.4 Rare Event Estimation and Monte Carlo -- 4.4 Variance Reduction and Importance Sampling -- 4.4.1 An Overview of Variance Reduction Methods -- 4.4.2 Importance Sampling -- 4.5 Importance Sampling for Memory Design -- 4.5.1 The Importance Sampling Distribution -- 4.5.2 SRAM Application -- 4.6 Conclusions -- References -- Chapter 5: Direct SRAM Operation Margin Computation with Random Skews of Device Characteristics -- 5.1 Introduction -- 5.2 General Metrics for SRAM Operation Margins -- 5.3 Idealization of Statistical Chaos in Single Variable -- 5.4 Formulation of the Direct Computation for SRAM Margins -- 5.5 Example ADM and WRM Computation -- 5.6 Extending DC Computation to Transient Operations -- 5.7 Unstable SRAM Cells at High Vdd and High Beta Ratio -- 5.8 Write Fail from New Floating Bodies -- 5.9 General Transient Margin Computation Sequence and DC Margin Bias Setting -- 5.10 Thinner Tinv to Mitigate Floating Body Effects Which Degrade SRAM Stability -- 5.11 SRAM Wear and Tear from NBTI and PBTI -- 5.12 SRAM Vmax Problem from DIBL -- 5.13 Getting Around Curvatures of Metric Gradients -- 5.14 Conclusion and Acknowledgment -- Appendix: SRAM Margins to Meet Yield Targets -- References -- Chapter 6: Yield Estimation by Computing Probabilistic Hypervolumes -- 6.1 Introduction: Parameter Variations and Yield Estimation -- 6.1.1 Yield Estimation -- 6.1.2 An Example: SRAM Read Access Failure -- 6.2 Approaches to Yield Estimation -- 6.2.1 Statistical Methods -- 6.2.2 Deterministic/Mixed Methods -- 6.3 Computing the Boundary and Probabilistic Hypervolumes -- 6.3.1 Basic Idea and Geometrical Explanation -- 6.3.2 YENSS Algorithm Outline -- 6.3.3 Implicit Formu
Summary 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
Bibliography Includes bibliographical references and index
In Springer eBooks
Subject Magnetic memory (Computers) -- Design
Magnetic memory (Computers) -- Design -- Statistical methods
Nanotechnology.
Extreme value theory.
COMPUTERS -- Computer Engineering.
COMPUTERS -- Hardware -- General.
COMPUTERS -- Machine Theory.
Ingénierie.
Extreme value theory.
Nanotechnology.
Form Electronic book
Author Singhee, Amith.
Rutenbar, Rob A., 1957-
ISBN 9781441966063
1441966064
1441966056
9781441966056