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

Autor: Michela Borghesi, Cristian Zambelli, Rino Micheloni, Stefano Bonnini
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Future Internet, Vol 15, Iss 10, p 319 (2023)
Druh dokumentu: article
ISSN: 1999-5903
DOI: 10.3390/fi15100319
Popis: 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.
Databáze: Directory of Open Access Journals
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