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기계 학습을 이용한 셀 문턱 전압 분포 기반의 Soft Decision 센싱 레벨 예측Prediction of Soft Decision Sensing Level Based on Distribution of Cell Threshold Voltage Using Machine Learning

Other Titles
Prediction of Soft Decision Sensing Level Based on Distribution of Cell Threshold Voltage Using Machine Learning
Authors
노해동백준걸
Issue Date
2021
Publisher
대한산업공학회
Keywords
Distribution of Cell Threshold Voltage; Machine Learning; NAND Flash; Sensing Level Prediction; Soft Decision
Citation
대한산업공학회지, v.47, no.5, pp.470 - 478
Indexed
KCI
Journal Title
대한산업공학회지
Volume
47
Number
5
Start Page
470
End Page
478
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/138281
ISSN
1225-0988
Abstract
Along with the rapid growth of the NAND flash memory market, the phenomenon of stacking 3D NAND flash memory is also steadily increasing. As the number of stacks increases, NAND flash memory will inherit different cell threshold voltage distributions for different physical characteristics. Furthermore, this phenomenon intensifies as the deterioration of data neglect is added. Considering the threshold voltage of these various cells, it becomes difficult to derive the sensing level during the operation of embedded memory products. In this paper, we propose a Sensing Level (SL) prediction method for making Soft Decision (SD) using machine learning. The proposed method experimentally confirmed the possibility of constructing a model that reflects the threshold voltage distributions of various cells. The prediction accuracy of the model confirmed an excellent performance of 94 to 99%, improving 36 to 52%p compared to that of the probability-based prediction method.
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공과대학 (산업경영공학부)
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