@inproceedings{63d83855b00d4101bcf9b42cdb52b850,
title = "Machine Learning-Based Optimization Technique for High-Capacity V-NAND Flash Memory",
abstract = "In the NAND flash manufacturing process, thousands of internal electronic fuses (eFuse) should be tuned in order to optimize performance and validity. In this paper, we propose a machine learning-based optimization technique that can automatically tune the individual eFuse value based on a deep learning and genetic algorithm. Using state-of-the-art triple-level cell (TLC) V-NAND flash wafers, we trained our model and validated its effectiveness. The experimental results show that our technique can automatically optimize NAND flash memory, thus reducing total turnaround time (TAT) by 70 % compared with the manual-based process.",
author = "Jisuk Kim and Earl Kim and Daehyeon Lee and Taeheon Lee and Daesik Ham and Miju Yang and Wanha Hwang and Jaeyoung Kim and Sangyong Yoon and Youngwook Jeong and Eunkyoung Kim and Song, {Ki Whan} and Song, {Jai Hyuk} and Myungsuk Kim and Choi, {Woo Young}",
note = "Publisher Copyright: Copyright {\textcopyright} 2021 ASM International{\textregistered} All rights reserved.; 47th International Symposium for Testing and Failure Analysis Conference, ISTFA 2021 ; Conference date: 31-10-2021 Through 04-11-2021",
year = "2021",
doi = "10.31399/asm.cp.istfa2021p0020",
language = "English",
series = "Conference Proceedings from the International Symposium for Testing and Failure Analysis",
publisher = "ASM International",
pages = "20--22",
booktitle = "ISTFA 2021",
address = "United States",
}