Extreme Statistics in Nanoscale Memory Design by Amith Singhee (auth.), Amith Singhee, Rob A. Rutenbar (eds.)

By Amith Singhee (auth.), Amith Singhee, Rob A. Rutenbar (eds.)

Extreme data in Nanoscale reminiscence layout brings jointly the various world’s top specialists in statistical EDA, reminiscence layout, gadget variability modeling and reliability modeling, to bring together theoretical and functional ends up in one whole reference on statistical options for severe information in nanoscale thoughts. The paintings covers various suggestions, together with statistical, deterministic, model-based and non-parametric equipment, besides appropriate description of the resources of adaptations and their influence on units and reminiscence layout. in particular, the authors disguise equipment from severe worth idea, Monte Carlo simulation, reliability modeling, direct reminiscence margin computation and hypervolume computation. rules also are provided either from the point of view of an EDA practitioner and a reminiscence dressmaker to supply a accomplished realizing of the state-of -the-art within the sector of maximum statistics estimation and statistical reminiscence layout. severe facts in Nanoscale reminiscence layout is an invaluable reference on statistical layout of built-in circuits for researchers, engineers and professionals.

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Systematic and stratified sampling aims at sample points that are more representative of the population. Systematic sampling: Also called interval sampling, it starts with an ordered population. g. every tenth name in a mailing list. Stratified sampling: The population is divided into different groups or strata. Each group is then randomly sampled. This way the sample points are more representative of the population. 4 Importance Sampling-Based Estimation: Applications to Memory Design l 53 Cluster sampling: The population is divided into groups or clusters.

Such percolation path is clearly visible in the upper part of the channel in Fig. 11a. A single trapped charge, which produces the sharp pick in the potential distribution in Fig. 11b, completely cuts the percolation path resulting in 30-mV threshold voltage shift, which is six times larger than the average 5-mV threshold voltage shift. NBTI/PBTI and hot carrier degradation are already a fundamental problem in flash [16] and SRAM memories [17], and starts to reduce the lifetime of digital chips dramatically.

We refer the reader to [4] for further details. 3 Random Sample Generation Most statistical sampling methods invoke random sample generators. In this section, we review software-based methods for generating uniform random samples and Gaussian random samples. 1 Pseudo-Random Number Generation A true random number generator would rely on a physical process like coin toss or a dice throw. Software-generated random numbers are often referred to as apparently random numbers or “pseudo”-random numbers because they are generated from deterministic processes or computer programs.

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