Modeling of Threshold Voltage Distribution in 3D NAND Flash Memory
Weihua Liu1, Fei Wu1,a,b, Jian Zhou1, Meng Zhang1, Chengmo Yang2, Zhonghai Lu3, Yu Wang1 and Changsheng Xie1
1Wuhan National Laboratory for Optoelectronics, Key Laboratory of Information Storage System, Engineering Research Center of Data Storage Systems and Technology, Ministry of Education of China, School of Computer Science and Technology, Huazhong University of Science and Technology, China
2University of Delawa, USA
3KTH Royal Institute of Technology, Sweden
aFeiWu@hust.edu.cn
bwufei@hust.edu.cn
ABSTRACT
3D NAND flash memory faces unprecedented complicated interference than planar NAND flash memory, resulting in more concern regarding reliability and performance. Stronger error correction code (ECC) and adaptive reading strategies are proposed to improve the reliability and performance taking a threshold voltage (Vth) distribution model as the backbone. However, the existing modeling methods are challenged to develop such a Vth distribution model for 3D NAND flash memory. To facilitate it, in this paper, we propose a machine learning-based modeling method. It employs a neural network taking advantage of the existing modeling methods and fully considers multiple interferences and variations in 3D NAND flash memory. Compared with state-of-the-art models, evaluations demonstrate it is more accurate and efficient for predicting Vth distribution.
Keywords: Modeling, Threshold Voltage Distribution, 3D NAND flash memory, ECC
