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Titlebook: Extreme Statistics in Nanoscale Memory Design; Amith Singhee,Rob A. Rutenbar Book 2010 Springer Science+Business Media, LLC 2010 CMOS.Devi

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書目名稱Extreme Statistics in Nanoscale Memory Design
編輯Amith Singhee,Rob A. Rutenbar
視頻videohttp://file.papertrans.cn/321/320058/320058.mp4
概述Includes a treatment of memory design from the perspective of statistical analysis.Covers relevant theoretical background from other fields: statistics, machine learning, optimization, reliability.Exp
叢書名稱Integrated Circuits and Systems
圖書封面Titlebook: Extreme Statistics in Nanoscale Memory Design;  Amith Singhee,Rob A. Rutenbar Book 2010 Springer Science+Business Media, LLC 2010 CMOS.Devi
描述Knowledge exists: you only have to ?nd it VLSI design has come to an important in?ection point with the appearance of large manufacturing variations as semiconductor technology has moved to 45 nm feature sizes and below. If we ignore the random variations in the manufacturing process, simulation-based design essentially becomes useless, since its predictions will be far from the reality of manufactured ICs. On the other hand, using design margins based on some traditional notion of worst-case scenarios can force us to sacri?ce too much in terms of power consumption or manufacturing cost, to the extent of making the design goals even infeasible. We absolutely need to explicitly account for the statistics of this random variability, to have design margins that are accurate so that we can ?nd the optimum balance between yield loss and design cost. This discontinuity in design processes has led many researchers to develop effective methods of statistical design, where the designer can simulate not just the behavior of the nominal design, but the expected statistics of the behavior in manufactured ICs. Memory circuits tend to be the hardest hit by the problem of these random variations
出版日期Book 2010
關(guān)鍵詞CMOS; Device Variability Modeling; EVT; Extreme Value Theory; Memory Design; Nanoscale VLSI; Sampling-Base
版次1
doihttps://doi.org/10.1007/978-1-4419-6606-3
isbn_softcover978-1-4614-2672-1
isbn_ebook978-1-4419-6606-3Series ISSN 1558-9412 Series E-ISSN 1558-9420
issn_series 1558-9412
copyrightSpringer Science+Business Media, LLC 2010
The information of publication is updating

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Importance Sampling-Based Estimation: Applications to Memory Design,
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Direct SRAM Operation Margin Computation with Random Skews of Device Characteristics,
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Yield Estimation by Computing Probabilistic Hypervolumes,
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Book 2010led many researchers to develop effective methods of statistical design, where the designer can simulate not just the behavior of the nominal design, but the expected statistics of the behavior in manufactured ICs. Memory circuits tend to be the hardest hit by the problem of these random variations
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