基于读干扰人工神经网络耦合LDPC ECC的3D-TLC NAND闪存读热数据误差修正

2019 Silicon Nanoelectronics Workshop (SNW) Pub Date : 2019-06-01 DOI:10.23919/SNW.2019.8782950

Daiki Kojima, Toshiki Nakamura, K. Takeuchi

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引用次数: 3

摘要

针对3D-TLC NAND闪存中读热数据的错误，提出了基于读干扰模型的人工神经网络耦合LDPC ECC (RDNN-LDPC)算法。对传统的ANN-LDPC进行了优化，以纠正读冷数据的错误。但是，ANN-LDPC不能纠正读热数据的错误。为了纠正读热数据的错误，本文分析了输入参数和模型的变化情况。因此，RDNN-LDPC的测量结果将3D-TLC NAND闪存的可接受读取周期延长了10倍。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Error Crrection for Read-hot Data in 3D-TLC NAND Flash by Read-disturb Modeled Artificial Neural Network Coupled LDPC ECC

Read-disturb Modeled Artificial Neural Network Coupled LDPC ECC (RDNN-LDPC) is proposed to correct errors of read-hot data for 3D-TLC NAND flash. Conventional ANN-LDPC is optimized to correct errors of read-cold data. However, ANN-LDPC does not correct errors of read-hot data. To correct errors of read-hot data, this paper analyzes how input parameter and model change. As a result, measured results of proposed RDNN-LDPC extend acceptable read cycle of 3D-TLC NAND flash by 10-times.

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2019 Silicon Nanoelectronics Workshop (SNW)

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