Endurance prediction and error Reduction in NAND flash using machine learning

Fitzgerald, Barry; Hogan, Damien; Ryan, Conor; Sullivan, Joseph A.

doi:10.1109/nvmts.2017.8171304

Cited by 12 publications

(7 citation statements)

References 8 publications

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“…Furthermore, different page types (MSB even, MSB odd, LSB even, LSB odd) exhibit different error rates, and error rates are dependent on wordline number, indicating that device architecture plays a significant role in a page's error characteristics [2]. Subsequent work by the authors on the 19nm devices used for this study supported these findings [3].…”

Section: Related Researchsupporting

confidence: 54%

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An Early-Life NAND Flash Endurance Prediction System

Fitzgerald¹,

Ryan

Sullivan

2021

IEEE Access

Self Cite

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NAND flash memory -ubiquitous in today's world of smart phones, SSDs (solid state drives), and cloud storage -has a number of well-known reliability problems. NAND data contains bit errors, which require the use of error correcting codes (ECCs). The raw bit error rate (RBER) increases with programerase (P-E) cycling, and the number of P-E cycles the device can withstand before the RBER exceeds the ECC capability is called its endurance. ECC operates on data stored in a sector of NAND, and there is a large variation in the endurance of sectors within a device and across devices, resulting in excessively conservative endurance specifications. This research shows, for the first time, that a sector's true endurance can be predicted with remarkable accuracy, using a combination of the sector's location within the device, and measurements taken at the very beginning of life. Real-world data is gathered on millions of NAND sectors using a custom-built test platform. Optimised machine learning classification models are built from the raw data to predict if a sector will pass or fail to a fixed ECC threshold, after a target P-E cycling level has been reached. A novel technique is demonstrated that uses different ECC thresholds for model training and testing, which allows the models to be tuned so that they never misclassify samples that would fail. This eliminates ECC failures and data loss, allowing simpler, less expensive ECC schemes to be used for modern NAND devices. It also enables significant endurance extensions to be achieved.

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Section: Related Researchsupporting

confidence: 54%

“…Previous works by the authors have developed prediction models based on data collected at the rated endurance, to determine how much further each sector could be cycled before exceeding an RBER threshold [3], [5], [6]. Sector errors, program time and erase time were all found to have predictive value for this purpose.…”

Section: Related Researchmentioning

confidence: 99%

An Early-Life NAND Flash Endurance Prediction System

Fitzgerald¹,

Ryan

Sullivan

2021

IEEE Access

Self Cite

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“…Several machine learning-based methods have been proposed to assess the reliability of NAND flash memory under these noise conditions. [15][16][17][18] However, these methods are limited to measuring the error rate at the chip level rather than evaluating reliability at the cell level. Furthermore, they primarily focused on just a few types of noise, such as P/E cycle and retention time, and neglected other types, such as cross-temperature.…”

Section: Deep Learning For Nand Flashmentioning

confidence: 99%

Modeling of 3D NAND Characteristics for Cross‐Temperature by Using Graph Neural Network and Its Application

Cho,

Lee,

Park

et al. 2023

Advanced Intelligent Systems

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Herein, the impact of cross‐temperature on 3D NAND flash memory is modeled by considering adjacent cells using machine learning. The cells comprising NAND flash memory exhibit diverse states and connectivity patterns. To effectively capture this complexity, the 3D NAND flash is converted to graph structure and the graph neural network (GNN) is leveraged, known for its exceptional performance in handling graph data. To the best of the authors' knowledge, this is the first attempt to model 3D NAND flash memory using GNN. This method has good generalization performance across various retention times and temperatures, achieving a remarkable overlap of 95.28% between ground truth and predicted distributions. Moreover, two applications of this method are introduced that contribute to the NAND flash memory improvement. One is a GNN‐assist program, which leverages well‐trained GNN to suppress the degradation affected by cross‐temperature, resulting in reduced shift and narrower distribution width. The other is the sensitivity decomposition to identify parameters influencing the cell at cross‐temperature. It is found that cross‐temperature impact extends beyond physically connected cells to adjacent cells at close distances. Overall, this work provides valuable insights into modeling 3D NAND flash memory using GNNs and offers practical methods for enhancing NAND flash memory reliability.

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“…Fitzgerald et al [29] propose to find flash metrics that could be measured while the device was P-E cycling, and use them to predict the true endurance of individual flash codewords. This paper didn't consider the impact of data retention time.…”

Section: B Model-based Techniques For Optimizing Reliability Of Nand Flash Memorymentioning

confidence: 99%

RBER-Aware Lifetime Prediction Scheme for 3D-TLC NAND Flash Memory

Zhang

et al. 2019

IEEE Access

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NAND flash memory is widely used in various computing systems. However, flash blocks can sustain only a limited number of program/erase (P/E) cycles, which are referred to as the endurance. On one hand, in order to ensure data integrity, flash manufacturers often define the maximum P/E cycles of the worst block as the endurance of flash blocks. On the other hand, blocks exhibit large endurance variations, which introduce two serious problems. The first problem is that the error correcting code (ECC) is often over-provisioned, as it has to be designed to tolerate the worst case to ensure data integrity, which causes longer decoding latency. The second problem is the underutilized block's lifespan due to conservatively defined block endurance. Raw bit error rate (RBER) of most blocks have not arrived the allowable RBER based on the nominal endurance point, which implies that the conventional P/E cycle-based block retirement policies may waste large flash storage space. In this paper, to exploit the storage capacity of each flash block, we propose an RBER-aware lifetime prediction scheme based on machine learning technologies. We consider the problem that the model can lose prediction effectiveness over time and use incremental learning to update the model for adapting the changes at different lifetime stages. At run time, trained data will be gradually discarded, which can reduce memory overhead. For evaluating our purpose, four wellknown machine learning techniques have been compared in terms of predictive accuracy and time overhead under our proposed lifetime prediction scheme. We also compared the predicted values with the tested values obtained in the real NAND flash-based test platform, and the experimental results show that the support vector machine (SVM) models based on our proposed lifetime prediction scheme can achieve as high as 95% accuracy for flash blocks. We also apply our proposed lifetime prediction scheme to predict the actual endurance of flash blocks at four different retention times, and the experimental results show that it can significantly improve the maximum P/E cycle of flash blocks from 37.5% to 86.3% on average. Therefore, the proposed lifetime prediction scheme can provide a guide for block endurance prediction.

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Endurance prediction and error Reduction in NAND flash using machine learning

Cited by 12 publications

References 8 publications

An Early-Life NAND Flash Endurance Prediction System

An Early-Life NAND Flash Endurance Prediction System

Modeling of 3D NAND Characteristics for Cross‐Temperature by Using Graph Neural Network and Its Application

RBER-Aware Lifetime Prediction Scheme for 3D-TLC NAND Flash Memory

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