Read Threshold Calibration for Non-Volatile Flash Memories

Rajab, Mohammed; Thiers, Johann-Philipp; Freudenberger, Jürgen

doi:10.1109/icce-berlin47944.2019.8966181

Cited by 5 publications

(6 citation statements)

References 13 publications

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“…Similarly to the methods proposed in [32,34], we exploit the error correction code to obtain information about the cell state. Based on the error correction code, we can determine the number of errors and the asymmetry of the error distribution, i.e., the probabilities of 1-to-0 errors and 0-to-1 errors are not equal [23,25,30,42].…”

Section: Problem Statement and Principal Approachmentioning

confidence: 99%

“…Several publications proposed threshold adaptation concepts [25,[30][31][32][33][34][35]. These approaches adjust the read references to minimize bit error rates.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we propose two methods for reference calibration that aim on minimizing the BER. Similar to the methods proposed in [32,34], we exploit the error correction code to obtain information about the cell state. Based on the number of observed errors in the read codeword, we predict the shift of the reference voltages.…”

Section: Introductionmentioning

confidence: 99%

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Read Reference Calibration and Tracking for Non-Volatile Flash Memories

et al. 2021

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The performance and reliability of nonvolatile NAND flash memories deteriorate as the number of program/erase cycles grows. The reliability also suffers from cell-to-cell interference, long data retention time, and read disturb. These processes effect the read threshold voltages. The aging of the cells causes voltage shifts which lead to high bit error rates (BER) with fixed predefined read thresholds. This work proposes two methods that aim on minimizing the BER by adjusting the read thresholds. Both methods utilize the number of errors detected in the codeword of an error correction code. It is demonstrated that the observed number of errors is a good measure for the voltage shifts and is utilized for the initial calibration of the read thresholds. The second approach is a gradual channel estimation method that utilizes the asymmetrical error probabilities for the one-to-zero and zero-to-one errors that are caused by threshold calibration errors. Both methods are investigated utilizing the mutual information between the optimal read voltage and the measured error values. Numerical results obtained from flash measurements show that these methods reduce the BER of NAND flash memories significantly.

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Section: Problem Statement and Principal Approachmentioning

confidence: 99%

“…Several publications proposed threshold adaptation concepts [25,[30][31][32][33][34][35]. These approaches adjust the read references to minimize bit error rates.…”

Section: Introductionmentioning

confidence: 99%

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Read Reference Calibration and Tracking for Non-Volatile Flash Memories

et al. 2021

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“…Different methods for RRV adaptation are proposed in the literature [9], [13], [14], [15], [16], [17], [18], [19]. In [14], a Gaussian mixture model (GMM) was used to estimate the threshold voltage distribution (TVD).…”

Section: Introductionmentioning

confidence: 99%

Read Reference Voltage Adaptation for NAND Flash Memories With Neural Networks Based on Sparse Histograms

2023

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Non-volatile NAND flash memories store information as an electrical charge. Different read reference voltages are applied to read the data. However, the threshold voltage distributions vary due to aging effects like program erase cycling and data retention time. It is necessary to adapt the read reference voltages for different life-cycle conditions to minimize the error probability during readout. In the past, methods based on pilot data or high-resolution threshold voltage histograms were proposed to estimate the changes in voltage distributions. In this work, we propose a machine learning approach with neural networks to estimate the read reference voltages. The proposed method utilizes sparse histogram data for the threshold voltage distributions. For reading the information from triple-level cell (TLC) memories, several read reference voltages are applied in sequence. We consider two histogram resolutions. The simplest histogram consists of the zero-and-one ratios for the hard decision read operation, whereas a higher resolution is obtained by considering the quantization levels for soft-input decoding. This approach does not require pilot data for the voltage adaptation. Furthermore, only a few measurements of extreme points of the threshold voltage distributions are required as training data. Measurements with different conditions verify the proposed approach. The resulting neural networks perform well under other life-cycle conditions.INDEX TERMS Non-volatile NAND flash, channel estimation, machine learning, neural network, read reference adjustment.

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“…Due to the high memory density and fabrication tolerances, errors during the readout are becoming more probable, and more sophisticated error correction algorithms are necessary. To improve the error correction performance, soft reading is applied to the flash cell [9][10][11][12]. Low-density parity-check codes (LDPC) are well-suited for flash systems with hard-input or soft-input decoding [10,[13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Error Correction for TLC and QLC NAND Flash Memories Using Cell-Wise Encoding

2022

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The growing error rates of triple-level cell (TLC) and quadruple-level cell (QLC) NAND flash memories have led to the application of error correction coding with soft-input decoding techniques in flash-based storage systems. Typically, flash memory is organized in pages where the individual bits per cell are assigned to different pages and different codewords of the error-correcting code. This page-wise encoding minimizes the read latency with hard-input decoding. To increase the decoding capability, soft-input decoding is used eventually due to the aging of the cells. This soft-decoding requires multiple read operations. Hence, the soft-read operations reduce the achievable throughput, and increase the read latency and power consumption. In this work, we investigate a different encoding and decoding approach that improves the error correction performance without increasing the number of reference voltages. We consider TLC and QLC flashes where all bits are jointly encoded using a Gray labeling. This cell-wise encoding improves the achievable channel capacity compared with independent page-wise encoding. Errors with cell-wise read operations typically result in a single erroneous bit per cell. We present a coding approach based on generalized concatenated codes that utilizes this property.

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Read Threshold Calibration for Non-Volatile Flash Memories

Cited by 5 publications

References 13 publications

Read Reference Calibration and Tracking for Non-Volatile Flash Memories

Read Reference Calibration and Tracking for Non-Volatile Flash Memories

Read Reference Voltage Adaptation for NAND Flash Memories With Neural Networks Based on Sparse Histograms

Error Correction for TLC and QLC NAND Flash Memories Using Cell-Wise Encoding

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