Channel Parameter and Read Reference Voltages Estimation in 3-D NAND Flash Memory Using Unsupervised Learning Algorithms

In 3-D NAND flash memory, the channel is always offset due to the complicated interference from the program/erase (PE), including the data retention and layer interference, so that the channel estimation is desired. However, due to the physical structure of 3-D flash memory, there exists significant...

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Published in:	IEEE transactions on computer-aided design of integrated circuits and systems Vol. 43; no. 1; pp. 305 - 318
Main Authors:	Hu, Haihua, Han, Guojun, Wu, Wenhua, Liu, Chang
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	3-D NAND flash memory Algorithms Channel estimation Error analysis Error correction Estimation Flash memories Flash memory (computers) Interference low complexity Machine learning Memory Parameter estimation Parameters read reference voltage (RRV) Search algorithms Threshold voltage Unsupervised learning unsupervised learning algorithm
ISSN:	0278-0070, 1937-4151
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Abstract	In 3-D NAND flash memory, the channel is always offset due to the complicated interference from the program/erase (PE), including the data retention and layer interference, so that the channel estimation is desired. However, due to the physical structure of 3-D flash memory, there exists significant variation in channel parameters and inconsistent channel offsets among different wordlines. As a result, the process of estimating channel parameters for each individual wordline typically requires a considerable amount of computational resources, resulting in the high latency of system, which becomes a new challenge. To tackle this problem, two unsupervised learning algorithms are proposed to estimate the channel parameters, based on analyzing the error distribution in 3-D flash memory. To address the longer read latency introduced by the unsupervised learning algorithm for the channel estimation, we further propose a low-latency detection algorithm, which first detects whether the current channel needs to be updated. In the event that an update is required, the algorithm only periodically estimates the channel parameters during system idle times, resulting in a more efficient and streamlined process. Compared to the existing methods, the proposed algorithms can efficiently estimate channel parameters with lower-computational complexity. Moreover, combining with the search algorithm, a correction scheme is proposed to minimize the error between the estimated read reference voltage (RRV) and the optimal RRV in the actual device. Theoretical analysis and simulation results demonstrate that the proposed method can improve the lifetime of flash memory and reduces the number of read retries.
AbstractList	In 3-D NAND flash memory, the channel is always offset due to the complicated interference from the program/erase (PE), including the data retention and layer interference, so that the channel estimation is desired. However, due to the physical structure of 3-D flash memory, there exists significant variation in channel parameters and inconsistent channel offsets among different wordlines. As a result, the process of estimating channel parameters for each individual wordline typically requires a considerable amount of computational resources, resulting in the high latency of system, which becomes a new challenge. To tackle this problem, two unsupervised learning algorithms are proposed to estimate the channel parameters, based on analyzing the error distribution in 3-D flash memory. To address the longer read latency introduced by the unsupervised learning algorithm for the channel estimation, we further propose a low-latency detection algorithm, which first detects whether the current channel needs to be updated. In the event that an update is required, the algorithm only periodically estimates the channel parameters during system idle times, resulting in a more efficient and streamlined process. Compared to the existing methods, the proposed algorithms can efficiently estimate channel parameters with lower-computational complexity. Moreover, combining with the search algorithm, a correction scheme is proposed to minimize the error between the estimated read reference voltage (RRV) and the optimal RRV in the actual device. Theoretical analysis and simulation results demonstrate that the proposed method can improve the lifetime of flash memory and reduces the number of read retries.
Author	Wu, Wenhua Hu, Haihua Han, Guojun Liu, Chang
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SubjectTerms	3-D NAND flash memory Algorithms Channel estimation Error analysis Error correction Estimation Flash memories Flash memory (computers) Interference low complexity Machine learning Memory Parameter estimation Parameters read reference voltage (RRV) Search algorithms Threshold voltage Unsupervised learning unsupervised learning algorithm
Title	Channel Parameter and Read Reference Voltages Estimation in 3-D NAND Flash Memory Using Unsupervised Learning Algorithms
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