Prediction of Random Grain Boundary Variation Effect of 3-D NAND Flash Memory Using a Machine Learning Approach

2021 ◽  
pp. 1-3
Author(s):  
Jang Kyu Lee ◽  
Kyul Ko ◽  
Hyungcheol Shin
Keyword(s):  
Machine Learning ◽  
Grain Boundary ◽  
Flash Memory ◽  
Learning Approach ◽  
Nand Flash ◽  
Nand Flash Memory ◽  
Boundary Variation ◽  
Machine Learning Approach

The Influence of Nitrogen Doping on the Chemical and Local Bonding Environment of Amorphous and Crystalline Ge2Sb2Te5

2009 ◽  
Vol 1160 ◽  
Author(s):  
Joseph Washington ◽  
Eric A. Joseph ◽  
Michael A. Paesler ◽  
Gerald Lucovsky ◽  
Jean L. Jordan-Sweet ◽  
...  
Keyword(s):  
Grain Boundary ◽  
Flash Memory ◽  
Nitrogen Doping ◽  
Phase Change Materials ◽  
Annealed Film ◽  
Nand Flash ◽  
Nand Flash Memory ◽  
Local Bonding ◽  
Memory Applications

AbstractRecent interest in phase change materials (PCMs) for non-volatile memory applications has been fueled by the promise of scalability beyond the limit of conventional DRAM and NAND flash memory [1]. However, for such solid state device applications, Ge2Sb2Te5 (GST), GeSb, and other chalcogenide PCMs require doping. Doping favorably modifies crystallization speed, crystallization temperature, and thermal stability but the chemical role of the dopant is not yet fully understood. In this work, X-ray Absorption Fine Spectroscopy (XAFS) is used to examine the chemical and structural role of nitrogen doping (N-) in as-deposited and crystalline GST thin films. The study focuses on the chemical and local bonding environment around each of the elements in the sample, in pre and post-anneal states, and at various doping concentrations. We conclude that the nitrogen dopant forms stable Ge-N bonds as deposited, which is distinct from GST bonds, and remain at the grain boundary of the crystallites such that the annealed film is comprised of crystallites with a dopant rich grain boundary.

scholarly journals Machine Learning-Based Optimization Technique for High-Capacity V-NAND Flash Memory

2021 ◽  
Author(s):  
Jisuk Kim ◽  
Earl Kim ◽  
Daehyeon Lee ◽  
Taeheon Lee ◽  
Daesik Ham ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Flash Memory ◽  
State Of The Art ◽  
Optimization Technique ◽  
High Capacity ◽  
Turnaround Time ◽  
Nand Flash ◽  
Nand Flash Memory ◽  
The Individual

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.

Influence of BEOL process on poly-Si grain boundary traps passivation in 3D NAND flash memory

2019 ◽  
Vol 156 ◽  
pp. 28-32 ◽  
Author(s):  
Yuexin Zhao ◽  
Jun Liu ◽  
Ziqun Hua ◽  
Lei Jin ◽  
Zongliang Huo
Keyword(s):  
Grain Boundary ◽  
Flash Memory ◽  
Nand Flash ◽  
Nand Flash Memory

A Dynamic Huffman Coding Method for Reliable TLC NAND Flash Memory

2021 ◽  
Vol 26 (5) ◽  
pp. 1-25
Author(s):  
Chin-Hsien Wu ◽  
Hao-Wei Zhang ◽  
Chia-Wei Liu ◽  
Ta-Ching Yu ◽  
Chi-Yen Yang
Keyword(s):  
Machine Learning ◽  
Flash Memory ◽  
Huffman Coding ◽  
Nand Flash ◽  
Nand Flash Memory ◽  
Compression Performance ◽  
Learning From Data ◽  
Physical Problems ◽  
Coding Method ◽  
Multi Level

With the progress of the manufacturing process, NAND flash memory has evolved from the single-level cell and multi-level cell into the triple-level cell (TLC). NAND flash memory has physical problems such as the characteristic of erase-before-write and the limitation of program/erase cycles. Moreover, TLC NAND flash memory has low reliability and short lifetime. Thus, we propose a dynamic Huffman coding method that can apply to the write operations of NAND flash memory. The proposed method exploits observations from a Huffman tree and machine learning from data patterns to dynamically select a suitable Huffman coding. According to the experimental results, the proposed method can improve the reliability of TLC NAND flash memory and also consider the compression performance for those applications that require the Huffman coding.

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