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http://dx.doi.org/10.9708/jksci.2021.26.11.001

K-means clustering analysis and differential protection policy according to 3D NAND flash memory error rate to improve SSD reliability  

Son, Seung-Woo (Dept. of Aerospace and Software Engineering, Gyeongsang National University)
Kim, Jae-Ho (Dept. of Aerospace and Software Engineering, Gyeongsang National University)
Abstract
3D-NAND flash memory provides high capacity per unit area by stacking 2D-NAND cells having a planar structure. However, due to the nature of the lamination process, there is a problem that the frequency of error occurrence may vary depending on each layer or physical cell location. This phenomenon becomes more pronounced as the number of write/erase(P/E) operations of the flash memory increases. Most flash-based storage devices such as SSDs use ECC for error correction. Since this method provides a fixed strength of data protection for all flash memory pages, it has limitations in 3D NAND flash memory, where the error rate varies depending on the physical location. Therefore, in this paper, pages and layers with different error rates are classified into clusters through the K-means machine learning algorithm, and differentiated data protection strength is applied to each cluster. We classify pages and layers based on the number of errors measured after endurance test, where the error rate varies significantly for each page and layer, and add parity data to stripes for areas vulnerable to errors to provides differentiate data protection strength. We show the possibility that this differentiated data protection policy can contribute to the improvement of reliability and lifespan of 3D NAND flash memory compared to the protection techniques using RAID-like or ECC alone.
Keywords
Reliability; 3D-NAND Flash; Bit error rate; RAID; Cluster; K-means;
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