Reliability of NAND Flash Memories: Planar Cells and Emerging Issues in 3D Devices

Spinelli, A.S.; Compagnoni, Christian Monzio; Lacaita, A.L.

doi:10.3390/computers6020016

Cited by 69 publications

(22 citation statements)

References 334 publications

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“…Figure 1(a) presents the impact of such retention errors in terms of bit error rates (BER) in 3D NAND (CT-flash) normalized to that in 2D NAND (FG-flash). In this figure, the 2D NAND curve represents the well-known trend for FG-Flash devices [35], while the 3D NAND data are derived using our proposed analytic model for similar process technology and parameters and verified against available data [7,9,16]. One can notice from the figure that while 2D NAND starts to suffer from high BER only near the end of its retention period (∼10 years), 3D NAND can experience around 70% of the peak BER only months after a program.…”

Section: Introductionmentioning

confidence: 90%

ReveNAND

Shihab¹,

Zhang

Kandemir

2018

ACM Trans. Archit. Code Optim.

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The paradigm shift from planar (two dimensional (2D)) to vertical (three-dimensional (3D)) models has placed the NAND flash technology on the verge of a design evolution that can handle the demands of next-generation storage applications. However, it also introduces challenges that may obstruct the realization of such 3D NAND flash. Specifically, we observed that the fast threshold drift (fast-drift) in a charge-trap flash-based 3D NAND cell can make it lose a critical fraction of the stored charge relatively soon after programming and generate errors. In this work, we first present an elastic read reference (V Ref) scheme (ERR) for reducing such errors in ReveNAND-our fast-drift aware 3D NAND design. To address the inherent limitation of the adaptive V Ref , we introduce a new intra-block page organization (hitch-hike) that can enable stronger error correction for the error-prone pages. In addition, we propose a novel reinforcement-learning-based smart data refill scheme (iRefill) to counter the impact of fast-drift with minimum performance and hardware overhead. Finally, we present the first analytic model to characterize fast-drift and evaluate its system-level impact. Our results show that, compared to conventional 3D NAND design, our ReveNAND can reduce fast-drift errors by 87%, on average, and can lower the ECC latency and energy overheads by 13× and 10×, respectively.

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Section: Introductionmentioning

confidence: 90%

ReveNAND

Shihab¹,

Zhang

Kandemir

2018

ACM Trans. Archit. Code Optim.

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“…2 (a), which reduces the burden of circuits. In addition, program inhibition by boosting the channel potential is used to program only one cell in a row by applying a high bias to the unselected bit-lines and a low bias to the selected bit-line [15].…”

Section: Operation Scheme Of Multi-layer Neural Networkmentioning

confidence: 99%

Operation Scheme of Multi-Layer Neural Networks Using NAND Flash Memory as High-Density Synaptic Devices

Lee

Lim

Choi

et al. 2019

IEEE J. Electron Devices Soc.

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We propose a designing of multi-layer neural networks using 2D NAND flash memory cell as a high-density and reliable synaptic device. Our operation scheme eliminates the waste of NAND flash cells and allows analogue input values. A 3-layer perceptron network with 40,545 synapses is trained on a MNIST database set using an adaptive weight update method for hardware-based multi-layer neural networks. The conductance response of NAND flash cells is measured and it is shown that the unidirectional conductance response is suitable for implementing multi-layer neural networks using NAND flash memory cells as synaptic devices. Using an online-learning, we obtained higher learning accuracy with NAND synaptic devices compared to that with a memristor-based synapse regardless of weight update methods. Using an adaptive weight update method based on a unidirectional conductance response, we obtained a 94.19% learning accuracy with NAND synaptic devices. This accuracy is comparable to 94.69% obtained by synapses based on the ideal perfect linear device. Therefore, NAND flash memory which is mature technology and has great advantage in cell density can be a promising synaptic device for implementing high-density multi-layer neural networks. INDEX TERMS Neuromorphic, NAND flash memory, deep neural networks (DNNs), synaptic device, deep learning, multi-layer neural networks, hardware-based neural network.

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“…Venkatesan [36] discussed the fundamentals and electron properties of 3D NAND Flash from the view of fabrication process integration and equipment engineering. References [11,37,38] compared 2D NAND Flash and 3D NAND Flash in terms of physical structure and working principle, and analyzed the advantages and problems brought by 3D technology. Seo [39] studied the interference between flash cells in terms of the composition of 3D NAND Flash cells.…”

Section: Related Workmentioning

confidence: 99%

Measurement and Analysis of SSD Reliability Data Based on Accelerated Endurance Test

et al. 2019

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In recent years, NAND Flash-based solid-state drives (SSDs) have become more widely used in data centers and consumer markets. Data centers generally choose to provide high-quality storage services by deploying a large number of SSDs, but there are no effective preventive measures to reduce the impact of SSD failures currently. Some existing studies have analyzed the relevant factors related to SSD failures from different angles, but the characteristics of reliability changes exhibited by SSD throughout the life cycle have not been explored in depth. On the other hand, although the 3D manufacturing process has increased the storage density of the SSD, the mutual influence between the flash units has also increased, resulting in severe degradation of the performance and lifetime of the SSD. Therefore, in order to fully understand the reliability varying process of SSD throughout the life cycle, we first designed an SSD lifetime endurance test method, then conducted the endurance test and collected the reliability data for the entire life cycle of the 3D TLC SSD in the laboratory environment with reference to the JEDEC standard. Through the analysis of experimental data and its statistical correlation, it is found that SSD will produce a large number of uncorrectable errors before reaching the endurance limit, and there will be a phenomenon of continuous high operating temperature, as well as showing some intrinsic relationships about SSD reliability data. The findings in this paper are valuable for identifying whether an SSD is going to fail.

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Reliability of NAND Flash Memories: Planar Cells and Emerging Issues in 3D Devices

Cited by 69 publications

References 334 publications

ReveNAND

ReveNAND

Operation Scheme of Multi-Layer Neural Networks Using NAND Flash Memory as High-Density Synaptic Devices

Measurement and Analysis of SSD Reliability Data Based on Accelerated Endurance Test

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