Exploiting Error Characteristic to Optimize Read Voltage for 3-D NAND Flash Memory
3-D NAND flash memory has become increasingly popular nonvolatile storage devices due to large capacity and high performance. With the increase of program/erase (P/E) cycles and retention periods, the threshold voltage distribution of 3-D NAND flash memory is prone to shift such that it is difficult...
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Published in | IEEE transactions on electron devices Vol. 67; no. 12; pp. 5490 - 5496 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.12.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | 3-D NAND flash memory has become increasingly popular nonvolatile storage devices due to large capacity and high performance. With the increase of program/erase (P/E) cycles and retention periods, the threshold voltage distribution of 3-D NAND flash memory is prone to shift such that it is difficult to accurately obtain the read reference voltage (RRV). When reading data, read retry operations perform multiple flash sensing to read bit information correctly, inducing extended read latency. To mitigate the read latency, a method of precisely acquiring the RRV is urgently needed. Using an field-programmable gate array (FPGA) hardware testing platform, this article first studies error characteristics of 3-D triple-level cell (TLC) NAND flash memory with the floating gate (FG) structure, which includes the variations of raw bit error rates (RBERs) in different layers and pages, the variations of block reads under different read modes, and the threshold voltage shifting characteristic. Then, based on these characterizations, this article develops an error characteristic aware RRV acquisition scheme, called ECRRV, to gain optimal RRV by exploiting the least square method. Experimental results show that the proposed scheme can significantly diminish the RBER and block read count. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ISSN: | 0018-9383 1557-9646 |
DOI: | 10.1109/TED.2020.3030867 |