A shared page-aware machine learning assisted method for predicting and improving multi-level cell NAND flash memory life expectancy

Modeling 3D NAND Flash with Nonparametric Inference on Regression Coefficients for Reliable Solid-State Storage

“…To this extent, storage designers have devised dedicated parametric statistical models [10][11][12][13][14][15][16] to capture the evolution of the memory's error distribution through well-known statistical frameworks (probability distributions), like Gaussian, binomial, Poisson, gamma, and so on. Concerning the model abstraction level, we identify two families: (i) statistical models of the flash channel (i.e., the cells' threshold voltage distributions) used to extrapolate the memory reliability characteristics; and (ii) statistical models or machine-learningbased approaches directly applied to the reliability features extracted from measurements.…”

“…A similar approach, although oriented to 3D NAND flash architectures was proposed in [23], where, for the first time, the authors introduced the use of the gamma-Poisson distribution for error modeling, and in [24], where a generalized Pareto distribution was used to model real disturb errors. Other interesting solutions have relied on machine learning algorithms for memory lifetime classification and prediction [13,15,16] and on deep neural networks [25][26][27]. However, some of the developed models require a huge characterization dataset and significant computing power to run the model training process or the creation of a dedicated computational framework based on neural networks to optimize the device characteristics, such as the case discussed in [28] though for a different technology.…”