Statistical Analysis of Bit-Errors Distribution for Reliability of 3-D NAND Flash Memories

Wang, Nian-Jia; Lee, Kuan-Yi; Lin, Hsin-Yi; Hsiao, Wei-Hao; Lee, Ming-Yi; Kuo, Li-Kuang; Lin, Ding-Jhang; Chao, Yen-Hai; Lu, Chih‐Yuan

doi:10.1109/irps45951.2020.9128993

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…A seminal work is that of [22], where the BER was modeled in different operating corners. 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].…”

Section: Introductionmentioning

confidence: 99%

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

Borghesi,

Zambelli,

Micheloni

et al. 2023

Future Internet

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Solid-state drives represent the preferred backbone storage solution thanks to their low latency and high throughput capabilities compared to mechanical hard disk drives. The performance of a drive is intertwined with the reliability of the memories; hence, modeling their reliability is an important task to be performed as a support for storage system designers. In the literature, storage developers devise dedicated parametric statistical approaches to model the evolution of the memory’s error distribution through well-known statistical frameworks. Some of these well-founded reliability models have a deep connection with the 3D NAND flash technology. In fact, the more precise and accurate the model, the less the probability of incurring storage performance slowdowns. In this work, to avoid some limitations of the parametric methods, a non-parametric approach to test the model goodness-of-fit based on combined permutation tests is carried out. The results show that the electrical characterization of different memory blocks and pages tested provides an FBC feature that can be well-modeled using a multiple regression analysis.

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Section: Introductionmentioning

confidence: 99%

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

Borghesi,

Zambelli,

Micheloni

et al. 2023

Future Internet

View full text Add to dashboard Cite

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Points-Over-Threshold Statistics for Post-Retention Read Disturb Reliability in 3D NAND Flash

Zambelli

Crippa²,

Micheloni³

et al. 2020

2020 IEEE International Integrated Reliability Workshop (IIRW)

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Investigating 3D NAND Flash Read Disturb Reliability With Extreme Value Analysis

Zambelli

Crippa²,

Micheloni³

et al. 2021

IEEE Trans. Device Mater. Relib.

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show abstract

Statistical Analysis of Bit-Errors Distribution for Reliability of 3-D NAND Flash Memories

Cited by 3 publications

References 15 publications

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

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

Points-Over-Threshold Statistics for Post-Retention Read Disturb Reliability in 3D NAND Flash

Investigating 3D NAND Flash Read Disturb Reliability With Extreme Value Analysis

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