Abstract
To achieve a low error rate of NAND flash memory, reliable reference voltages should be updated based on the accurate knowledge of program/erase (P/E) cycles and retention time, because those severely distort the threshold voltage distribution of memory cell. Due to the sensitivity to the temperature, however, a flash memory controller is unable to acquire the exact knowledge of retention time, meaning that it is challenging to estimate accurate read reference voltages in practice. In this article, we propose a novel machine-learning-based read reference voltage estimation framework for the NAND flash memory systems without the knowledge of retention time. To establish an unknown input-output relation of the estimation model, we derive input features by sensing and decoding memory cells in the minimum read unit. In order to define the relation between unlabeled input features and a pre-assigned class label, namely label read reference voltages, we propose three mapping functions: 1) $k$ -nearest neighbors-based, 2) nearest-centroid-based, and 3) polynomial regression-based read reference voltage estimators. For the proposed estimation schemes, we analyze that the storage overhead and computational complexity are increasing function of the exploited feature dimension. Accordingly, we propose a feature selection (or dimension reduction) algorithm to select the minimum dimension and corresponding features to reduce the overhead and complexity while maintaining high estimation accuracy. Based on extensive numerical analysis, we validate that the derived features successfully replace unknown knowledge of retention time, and the proposed feature selection algorithm precisely adjusts the trade-off between overhead/complexity and estimation accuracy. Furthermore, the simulation and analysis results show that the proposed framework not only outperforms the conventional estimation schemes but also achieves the near-optimal frame error rate while sustaining low latency performance.
Highlights
NAND flash memory is a non-volatile data storage medium with fast read/write speed and large storage capacity [1]
A retentionaware belief-propagation assisted channel update algorithm adjusted the input likelihood ratios (LLRs) of the second round decoding by estimating the mean and variance of the threshold voltage distribution under the assumption that the voltage distribution follows a Gaussian distribution [21]. These parametric-search-based schemes have attempted to estimate threshold voltage distribution and corresponding read reference voltages without knowledge of retention time, they rely on information acquired from multiple sensing and decoding, which results in excessive read latency
Based on the derived features, we propose the machine-learning-based read reference voltage estimation framework which consists of two phases: 1) off-line training phase and 2) on-line estimation phase
Summary
NAND flash memory is a non-volatile data storage medium with fast read/write speed and large storage capacity [1]. These parametric-search-based schemes have attempted to estimate threshold voltage distribution and corresponding read reference voltages without knowledge of retention time, they rely on information acquired from multiple sensing and decoding, which results in excessive read latency These schemes may be impractical because they cannot accurately characterize the threshold voltage distribution of an actual NAND flash memory with a limited number of parameters. To overcome the aforementioned drawbacks of conventional schemes, we propose a novel machine-learning-based read reference voltage estimation framework for NAND flash memory without the knowledge of retention time. We only perform the sensing and decoding process for the minimum read unit since one of our main goals is to develop low-latency machine-learning-based estimation framework for NAND flash memory. Since we can observe distinguishable variation of the derived features according to the change of retention time, these features are suitable to be exploited as the inputs of the machine-learning-based read reference voltage estimation framework when the knowledge of retention time is not available
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