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Fuzhou University Assigned Patent

Optimizing polar-RNNA quantizer of MLC-type NAND flash memory on basis of deep learning

Fuzhou University, Fujian, China, has been assigned a patent (11966587) developed by Chen, Pingping, Mei, Zhen, Fang, Yi, Luo, Xu, Lin, Zhijian, Chen, Feng, and Chen, Riqing, Fujian, China, for a method for optimizing polar-RNNA quantizer of MLC-type NAND flash memory on basis of deep learning.

The abstract of the patent published by the U.S. Patent and Trademark Office states: “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.

The patent application was filed on 2021-11-08 (17/905351).

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