DNN‐aided read‐voltage threshold optimization for MLC flash memory with finite block length

Wang, Cheng; Wei, Kang; Kong, Lingjun; Shi, Long; Mei, Zhen; Li, Jun; Cai, Kui

doi:10.1049/cmu2.12311

Cited by 2 publications

(4 citation statements)

References 65 publications

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“…We first investigate the uncoded SEP performance with different quantization schemes over different P/E cycles at t = 5 × 10 6 s. The quantization scheme that directly minimizes the SEP is served as a benchmark. With read-voltage thresholds optimized by these quantizers, the SEP of 3D flash memory channel can be calculated by Equation (24). As shown in Figure 6, the SEP of these quantization schemes is consistent with their MI in Figure 5.…”

Section: Numerical and Simulation Resultssupporting

confidence: 61%

“…Many studies have examined the quantization design for 2D flash memory under limited number of read-voltage levels [2,18,21,[23][24][25]. These prior works optimized the read-voltage thresholds with perfect knowledge of channel information, e.g., the number of P/E cycles and data retention time.…”

Section: Related Workmentioning

confidence: 99%

“…To evaluate the performance of our proposed MID scheme, the thresholds optimized by minimizing SEP (MSEP) is served as a benchmark. To find the thresholds that minimize the SEP, a cross iterative searching algorithm is adopted by utilizing genetic and iterative searching algorithm [24]. Through plenty of iterations, the cross iterative searching algorithm can approach the global optimum for given number of quantization levels.…”

Section: Read Thresholds Design For Hard Decision Decodingmentioning

confidence: 99%

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Channel Modeling and Quantization Design for 3D NAND Flash Memory

Wang

Mei

et al. 2023

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As the technology scales down, two-dimensional (2D) NAND flash memory has reached its bottleneck. Three-dimensional (3D) NAND flash memory was proposed to further increase the storage capacity by vertically stacking multiple layers. However, the new architecture of 3D flash memory leads to new sources of errors, which severely affects the reliability of the system. In this paper, for the first time, we derive the channel probability density function of 3D NAND flash memory by taking major sources of errors. Based on the derived channel probability density function, the mutual information (MI) for 3D flash memory with multiple layers is derived and used as a metric to design the quantization. Specifically, we propose a dynamic programming algorithm to jointly optimize read-voltage thresholds for all layers by maximizing the MI (MMI). To further reduce the complexity, we develop an MI derivative (MID)-based method to obtain read-voltage thresholds for hard-decision decoding (HDD) of error correction codes (ECCs). Simulation results show that the performance with jointly optimized read-voltage thresholds can closely approach that with read-voltage thresholds optimized for each layer, with much less read latency. Moreover, the MID-based MMI quantizer almost achieves the optimal performance for HDD of ECCs.

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Section: Numerical and Simulation Resultssupporting

confidence: 61%

Section: Related Workmentioning

confidence: 99%

Section: Read Thresholds Design For Hard Decision Decodingmentioning

confidence: 99%

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Channel Modeling and Quantization Design for 3D NAND Flash Memory

Wang

Mei

et al. 2023

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“…The strategy first analyses the channel coding rate for error probability, then uses cross iterative search to optimize the read voltage threshold, and finally uses DNN to optimize it. The results of this simulation experiment indicate that the optimization strategy using DNN improves the stability of the programming and slows down the read time delay [15]. Manoharan V et al proposed a hybrid DNN-SHO algorithm for the performance measurement of an ECDM process for zirconia.…”

Section: Related Workmentioning

confidence: 90%

A Multi-Level Deep Neural Network-Based Tourism Supply Chain Risk Management Study

2024

SCPE

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With the rapid advancement of the tourism, the capital demand of tourism enterprises has gradually risen, but the confusion of market management has increased the difficulty of risk assessment of tourism enterprises by financial institutions, which has led to difficulties in financing tourism enterprises and seriously hindered their development. On this basis, this research first analyses the risk structure of the tourism supply chain (TSC) from the financial aspect and establishes a relevant risk assessment system. After confirming the assessment indexes, the data is dativized and normalized, and finally a multi-level deep neural network (DNN) is used to construct a TSC risk prediction model to calculate the transformed indexes and assess the risk degree of the enterprise according to the results. The experimental results indicate that the model has the best performance when the H-Net hidden layer is 3 layers and the L-Net hidden layer is 4 layers, and its accuracy reaches 93.35%, sensitivity reaches 84.13%, convergence starts at 25 iterations, the final loss value is only 0.8, and the predicted and the real value error is within 2.5%. Therefore, the multi-level DNN model constructed in this experiment has certain application value in TSC risk management.

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DNN‐aided read‐voltage threshold optimization for MLC flash memory with finite block length

Cited by 2 publications

References 65 publications

Channel Modeling and Quantization Design for 3D NAND Flash Memory

Channel Modeling and Quantization Design for 3D NAND Flash Memory

A Multi-Level Deep Neural Network-Based Tourism Supply Chain Risk Management Study

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