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Convolutional Neural Network (CNN)-Based Detection for Multi-Level-Cell NAND Flash Memory
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DOI：10.1109/LCOMM.2021.3112908.png)
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摘要
En 中文
With the increase of program/erase (PE) cycles and retention time, it is difficult to predict the threshold-voltage distributions for detection in NAND flash memory. To accurately acquire the log-likelihood ratios (LLRs) without the knowledge of threshold-voltage distributions, a convolutional neural network (CNN)-based detection algorithm is proposed for the multi-level-cell (MLC) flash memory. The CNN-based detection algorithm employs the trained CNN to accurately calculate the LLRs for each threshold-voltage region. Furthermore, we develop a CNN-aided read-voltage design scheme to optimize the read voltages by maximizing the mutual information between the coded bits and their corresponding LLRs. Exploiting the proposed scheme, we first design three hard-decision read voltages, and then formulate more soft-decision read voltages to further improve the detection performance. Simulation results demonstrate that the CNN-based detection algorithm can achieve performance approaching that of the optimal detection algorithm.
Keyword:
Threshold voltage
Ash
Detection algorithms
Convolutional neural networks
Training
Voltage measurement
Quantization (signal)
NAND flash memory
convolutional neural network
read voltages
detection
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
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