A Program Interference Error Aware LDPC Scheme for Improving NAND Flash Decoding Performance

2017 ◽  
Vol 16 (5s) ◽  
pp. 1-20 ◽  
Author(s):  
Fei Wu ◽  
Meng Zhang ◽  
Yajuan Du ◽  
Xubin He ◽  
Ping Huang ◽  
...  
2012 ◽  
Vol E95.C (5) ◽  
pp. 837-841 ◽  
Author(s):  
Se Hwan PARK ◽  
Yoon KIM ◽  
Wandong KIM ◽  
Joo Yun SEO ◽  
Hyungjin KIM ◽  
...  

2016 ◽  
Vol E99.C (2) ◽  
pp. 293-301 ◽  
Author(s):  
Youngjoo LEE ◽  
Jaehwan JUNG ◽  
In-Cheol PARK

2014 ◽  
Vol 1008-1009 ◽  
pp. 659-662
Author(s):  
Hai Ke Liu ◽  
Shun Wang ◽  
Xin Gna Kang ◽  
Jin Liang Wang

The article realization of NAND FLASH control glueless interface circuit based on FPGA,comparing the advantages and disadvantages of the NAND Flash and analysising the function of control interface circuit. The control interface circuit can correct carry out the SRAM timing-input block erase, page reads, page programming, state read instructions into the required operation sequence of NAND Flash, greatly simplifies the NAND FLASH read and write timing control. According to the ECC algorithm,the realization method of ECC check code generation,error search,error correction is described.The function of operate instructions of the NAND Flash control interface circuit designed in this paper is verified on Xillinx Spartan-3 board, and the frequency can reach 100MHz.


Micromachines ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 879
Author(s):  
Ruiquan He ◽  
Haihua Hu ◽  
Chunru Xiong ◽  
Guojun Han

The multilevel per cell technology and continued scaling down process technology significantly improves the storage density of NAND flash memory but also brings about a challenge in that data reliability degrades due to the serious noise. To ensure the data reliability, many noise mitigation technologies have been proposed. However, they only mitigate one of the noises of the NAND flash memory channel. In this paper, we consider all the main noises and present a novel neural network-assisted error correction (ANNAEC) scheme to increase the reliability of multi-level cell (MLC) NAND flash memory. To avoid using retention time as an input parameter of the neural network, we propose a relative log-likelihood ratio (LLR) to estimate the actual LLR. Then, we transform the bit detection into a clustering problem and propose to employ a neural network to learn the error characteristics of the NAND flash memory channel. Therefore, the trained neural network has optimized performances of bit error detection. Simulation results show that our proposed scheme can significantly improve the performance of the bit error detection and increase the endurance of NAND flash memory.


Author(s):  
S. Gerardin ◽  
M. Bagatin ◽  
A. Paccagnella ◽  
S. Beltrami ◽  
A. Costantino ◽  
...  

Author(s):  
Ting Cheng ◽  
Jianquan Jia ◽  
Lei Jin ◽  
Xinlei Jia ◽  
Shiyu Xia ◽  
...  

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