[1]HU Haihua,HAN Guojun,ZHANG Xiaoyi.Research on flash memory channel detection technology based on convolutional neural network[J].CAAI Transactions on Intelligent Systems,2021,16(6):1090-1097.[doi:10.11992/tis.202010029]
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Research on flash memory channel detection technology based on convolutional neural network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 6 Page number: 1090-1097 Column: 学术论文—智能系统 Public date: 2021-11-05
Title:
Research on flash memory channel detection technology based on convolutional neural network
Author(s):
HU Haihua;  HAN Guojun;  ZHANG Xiaoyi
School of Information Engineering, Guangdong University of Technology, Guangzhou 510000, China
Keywords:
NAND flash memory; channel detection; convolutional neural network; cell to cell interference; channel prior information; complexity; reliability; threshold detection
CLC:
TN919.5
DOI:
10.11992/tis.202010029
Abstract:
Channel detection technology for NAND flash memory directly affects the reliability of data storage. Lack of prior information of channel states can lead to significant reduction of detection performances. Aiming at addressing this issue, this paper proposes a channel detector based on convolutional neural networks (CNN). The detector initializes network parameters by learning the varying characteristics with application scenarios of the threshold voltages of the storage cells, and realizes channel matching through optimization of the network parameters during the idle time of the system. The simulation results show that when the channel prior information is unavailable, the CNN detector can achieve better detection performance than the optimal threshold detector. Compared with the existing cyclic neural network detector, CNN detector has lower complexity and therefore reduced detection delay.
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Last Update: 2021-12-25

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