Method for optimizing polar-RNNA quantizer of MLC-type NAND flash memory on basis of deep learning
US11966587B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 8, 2021 |
| Grant date | Apr 23, 2024 |
| Priority date | — |
| Expiry date | Nov 30, 2041 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02D10/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.