Improvement of the tolerated raw bit error rate in NAND flash-based SSDs with the help of embedded statistics

Gherman, Valentin; Farjallah, Emna; Armani, J.M.; Seif, Marcelino; Dilillo, Luigi

doi:10.1109/test.2017.8242066

Cited by 4 publications

(2 citation statements)

References 15 publications

(31 reference statements)

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“…The same is true for 𝐷 𝑆𝑌𝑆_𝑅𝐸𝐹𝑅𝐸𝑆𝐻 when the method illustrated in Fig. 3 is adapted to implement a systematic refresh scheme [8]. In our experiments, 𝛼 𝐷𝐴𝑀𝑃 in ( 4) is selected such that the maximum tolerable UBER is reached at an 𝑅𝐵𝐸𝑅 𝑅𝐸𝑇 for which the average retention time is equal to 𝑇 𝐶𝐻𝐸𝐶𝐾 = 1/𝑓 𝐶𝐻𝐸𝐶𝐾 .…”

Section: Read and Checkmentioning

confidence: 93%

Improvement of the tolerated raw bit error rate in NAND flash-based SSDs with selective refresh

Farjallah¹,

Armani²,

Gherman³

et al. 2019

Microelectronics Reliability

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A recent large-scale study revealed that the uncorrectable bit error rates in data center solid-sate drives (SSDs) may fall far below the JEDEC standard recommendations. Here, a technique is proposed to improve the tolerated raw bit error rate (RBER) based on the observation that (a) a small SSD ratio may have a much higher RBER than the rest and (b) the RBER is dominated by the retention error rate. Instead of employing stronger but costly error-correcting codes an approach is used to estimate the remaining retention time, i.e., the reliable data storage time, of flash memory pages. This estimation can be performed each time a memory page is read based on the number of detected retention errors and the elapsed time since data was programmed. The fact that the estimated remaining retention time is smaller than a maximum time interval before the next read and check operation is an indication that data needs to be refreshed. It is estimated that the tolerated RBER can be increased by up to 35× over a storage period of 3 years if the stored data are checked on a monthly basis and refreshed only if necessary. The proposed technique has the ability to adapt the average time between refresh operations to the actual RBER. Maximum refresh time reductions of about 12x are reported as compared to systematic refresh schemes.

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Section: Read and Checkmentioning

confidence: 93%

Improvement of the tolerated raw bit error rate in NAND flash-based SSDs with selective refresh

Farjallah¹,

Armani²,

Gherman³

et al. 2019

Microelectronics Reliability

Self Cite

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“…Gherman et al [11] also investigate that data refresh scheme is based on worst-case scenarios, which can cause unnecessary data refresh operation. Thus, the authors propose to establish a prediction model to predict the data retention age, data refresh operation will be triggered if the predicted remaining retention time is smaller than the time to the next read operation, otherwise, data will be refreshed, this adaptive scheme can reduce write overhead caused by unnecessary refresh operation and can improve the lifetime of NAND flash.…”

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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BlockHammer: Improving Flash Reliability by Exploiting Process Variation Aware Proactive Failure Prediction

et al. 2020

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Improvement of the tolerated raw bit error rate in NAND flash-based SSDs with the help of embedded statistics

Cited by 4 publications

References 15 publications

Improvement of the tolerated raw bit error rate in NAND flash-based SSDs with selective refresh

Improvement of the tolerated raw bit error rate in NAND flash-based SSDs with selective refresh

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

BlockHammer: Improving Flash Reliability by Exploiting Process Variation Aware Proactive Failure Prediction

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