PDA-LSTM: Knowledge-driven page data arrangement based on LSTM for LCM supression in QLC 3D NAND flash memories
Qianhui Li, Weiya Wang, Qianqi Zhao, Tong Qu, Jing He, Xuhong Qiang, Jingwen Hou, Ke Chen, Bao Zhang, Qi Wang

TL;DR
This paper introduces PDA-LSTM, a physics-knowledge-driven neural network model that optimizes intra-page data arrangement in QLC 3D NAND flash memories to significantly reduce bit error rates caused by lateral charge migration.
Contribution
The paper proposes a novel LSTM-based data arrangement method that suppresses LCM effects without extra flag bits, outperforming existing intra-page data mapping strategies.
Findings
Reduces average BER by 80.4% compared to no arrangement
Outperforms WBVM and DVDS strategies by 18.4% and 15.2% respectively
Does not require extra flag bits for data transport management
Abstract
Quarter level cell (QLC) 3D NAND flash memory is emerging as the predominant storage solution in the era of artificial intelligence. QLC 3D NAND flash stores 4 bit per cell to expand the storage density, resulting in narrower read margins. Constrained to read margins, QLC always suffers from lateral charge migration (LCM), which caused by non-uniform charge density across adjacent memory cells. To suppress charge density gap between cells, there are some algorithm in form of intra-page data mapping such as WBVM, DVDS. However, we observe inter-page data arrangements also approach the suppression. Thus, we proposed an intelligent model PDA-LSTM to arrange intra-page data for LCM suppression, which is a physics-knowledge-driven neural network model. PDA-LSTM applies a long-short term memory (LSTM) neural network to compute a data arrangement probability matrix from input page data…
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Taxonomy
TopicsAdvanced Data Storage Technologies · Parallel Computing and Optimization Techniques · Error Correcting Code Techniques
