Machine learning model for predicting threshold voltage by taper angle variation and word line position in 3D NAND flash memory-Reference-Cited by-同舟云学术

Machine learning model for predicting threshold voltage by taper angle variation and word line position in 3D NAND flash memory

Published:2020-11-25 Issue:22 Volume:17 Page:20200345-20200345
ISSN:1349-2543
Container-title:IEICE Electronics Express
language:en
Short-container-title:IEICE Electron. Express

Author:

Lee Dong Chan¹,Lee Jang Kyu¹,Shin Hyungcheol¹

Affiliation:

1. Inter-University Semiconductor Research Center (ISRC) and School of Electrical Engineering and Computer Science, Seoul National University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Electrical and Electronic Engineering,Condensed Matter Physics,Electronic, Optical and Magnetic Materials

Link

https://www.jstage.jst.go.jp/article/elex/17/22/17_17.20200345/_pdf

Reference30 articles.

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2. [2] R. Micheloni, et al.: “Array architectures for 3-D NAND flash memories,” Proc. IEEE 105 (2017) 1634 (DOI: 10.1109/JPROC.2017.2697000).

3. [3] K. Parat and A. Goda: “Scaling trends in NAND flash,” 2018 IEEE International Electron Devices Meeting (IEDM) (2018) (DOI: 10.1109/IEDM.2018.8614694).

4. [4] A. Goda: “3-D NAND technology achievements and future scaling perspectives,” IEEE Trans. Electron Devices 67 (2020) 1373 (DOI: 10.1109/TED.2020.2968079).

5. [5] D. Kang, et al.: “256 Gb 3 b/cell V-NAND flash memory with 48 stacked WL layers,” IEEE J. Solid-State Circuits 52 (2016) 210 (DOI: 10.1109/JSSC.2016.2604297).

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