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

Michela Borghesi, Cristian Zambelli, Rino Micheloni and Stefano Bonnini ()
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Michela Borghesi: Department of Economics and Management, University of Ferrara, 44121 Ferrara, Italy
Cristian Zambelli: Department of Engineering, University of Ferrara, 44121 Ferrara, Italy
Rino Micheloni: Avaneidi srl, 21047 Saronno, Italy
Stefano Bonnini: Department of Economics and Management, University of Ferrara, 44121 Ferrara, Italy

Future Internet, 2023, vol. 15, issue 10, 1-13

Abstract: 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.

Keywords: non-parametric inference; goodness-of-fit; 3D NAND flash; reliability (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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