Abstract

The retention noise in MLC NAND flash memory has a great impact on reliability and lifetime of devices. How to recover information from the data corrupted by retention noise is a challenging problem. The joint processing of channel estimation, detection and low-density parity-check (LDPC) decoding is an effective method to handle this problem, while which is hindered by the limited computational resource of NAND flash memory devices. This paper proposes a discrete retention-failure recovery method, where the reading system only processes integers. The proposed method consists of two major components referred to binary tree searching (BTS) based channel updating and information bottleneck (IB) based LDPC decoding. In channel updating, the proposed learning based BTS algorithm can acquire reference voltages directly; thus it bypasses the intermediate stage of retention noise parameter estimation. The LDPC decoder is realized by IB based message passing, where node operation is achieved by lookup tables, and the messages passed between nodes are integers. Besides, a dynamic quantization level reduction strategy for IB based decoding is proposed to reduce the memory consumption of lookup tables. Finally, using PEG-constructed regular LDPC code (8000, 7200), the numerical results show that the proposed discrete method has much lower computational complexity and similar performance compared with the conventional method.

Highlights

  • NAND flash memory is widely used in digital devices

  • In this paper, we proposed a discrete NAND flash memory retention data recovery scheme consisting of channel updating and low-density parity-check (LDPC)-information bottleneck (IB) decoding, which largely reduced the computational complexity and maintained high performance in the simulation using PEG-constructed regular LDPC code (8000, 7200)

  • The channel updating is transformed into a classification problem, which is solved by classification and regression trees (CART) trees

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Summary

Introduction

NAND flash memory is widely used in digital devices. Data in devices powered-off for a long time will suffer from serious noises, among which retention noise predominates [1], [2]. How to overcome the retention noise is an important problem in data recovery. Prevalent NAND flash memory devices are usually powered by the battery and have limited computational resources, which means that the data recovery method should have low computational complexity and power consumption. Some conventional retention noise recovery methods utilize the refresh mechanism, where data are frequently read and corrected by error control coding (ECC), they are written back before the retention noise makes the error

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