Method for optimizing polar-RNNA quantizer of MLC-type NAND flash memory on basis of deep learning

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United States of America Patent

PATENT NO 11966587
APP PUB NO 20230127404A1
SERIAL NO

17905351

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Abstract

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A method for optimizing a Polar-RNNA quantizer of MLC NAND flash based on deep learning comprises the following steps: Step S1: transforming an MLC flash detection task into a deep learning task, and obtaining three hard-decision read thresholds based on a neural network; Step S2: expanding six soft-decision read thresholds based on the three hard-decision read thresholds; Step S3: constructing an LLR mapping table, and obtaining new LLR information of MLC flash based on the LLR mapping table; Step S4: symmetrizing an MLC flash channel, and performing density evolution; and Step S5: optimizing the soft-decision read thresholds based on a genetic algorithm to obtain an optimal quantization interval. According to the invention, polar codes can be directly used for MLC flash channels without the arduous work of MLC flash channel modeling, so that the reliability of MLC flash is effectively improved.

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Patent Owner(s)

  • FUZHOU UNIVERSITY

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Chen, Feng Fujian, CN 577 6004
Chen, Pingping Fujian, CN 3 28
Chen, Riqing Fujian, CN 2 0
Fang, Yi Fujian, CN 113 1330
Lin, Zhijian Fujian, CN 30 462
Luo, Xu Fujian, CN 8 16
Mei, Zhen Fujian, CN 2 0

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