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An Adaptive Method for Predicting MLC Flash Memory Voltage Thresholds Based on Deep Neural Networks

A deep neural network and voltage threshold technology, applied in the storage field, can solve problems such as inability to design properly, data retention time cannot be directly obtained, and data retention time cannot be further processed to achieve the effect of reducing system delay

Active Publication Date: 2021-07-09
NANJING UNIV OF POSTS & TELECOMM
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  • Abstract
  • Description
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Problems solved by technology

[0005] Traditional flash memory only optimizes the threshold voltage for different P / E times, but cannot further process the data retention time
The main reason is that the data retention time cannot be obtained directly, so it is impossible to design a suitable threshold voltage for different data retention times

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  • An Adaptive Method for Predicting MLC Flash Memory Voltage Thresholds Based on Deep Neural Networks
  • An Adaptive Method for Predicting MLC Flash Memory Voltage Thresholds Based on Deep Neural Networks
  • An Adaptive Method for Predicting MLC Flash Memory Voltage Thresholds Based on Deep Neural Networks

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Embodiment Construction

[0024] Below in conjunction with accompanying drawing and specific embodiment, further illustrate the present invention, should be understood that these embodiments are only for illustrating the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various aspects of the present invention Modifications in equivalent forms all fall within the scope defined by the appended claims of this application.

[0025] In flash memory, with the increase of data storage time and storage programming / erasing (program / erase, P / E), the distribution of storage voltage will change, which will cause the decoding failure of the decoder, so it is necessary to improve Stored read precision for more accurate likelihood ratio information (log-likelihood-ratio, LLR). Improving the reading accuracy of storage will bring a lot of storage delay, which requires us to propose an effective method f...

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Abstract

The invention discloses an adaptive method for predicting the voltage threshold of an MLC flash memory based on a deep neural network. Relationship to Voltage Threshold. A large number of stored voltage distribution feature values ​​are used as training input, and the training output result is used as the predicted voltage threshold. A training is performed, and the trained network weights are saved; the MLC flash memory is decoded once. If the decoding is successful, it will enter the next step. Decode once. If the decoding fails, the characteristic value of the distribution of the stored voltage is obtained; the voltage threshold is obtained; a Gaussian model is established to obtain new LLR information; and decoding is performed again. The present invention realizes voltage threshold optimization for optimizing different data retention times by establishing the relationship between the distribution of the current storage voltage and the optimal voltage threshold corresponding to the distribution.

Description

technical field [0001] The invention relates to the field of storage technology, in particular to an adaptive method for predicting the voltage threshold of an MLC flash memory based on a deep neural network. Background technique [0002] With the wide application of mobile phones, computers and other consumer electronics products, traditional storage technology is no longer suitable for the development of current high-integration, low-power and fast integrated circuit technology. Compared with DRAM, SRAM and other storage media, Flash Memory has higher storage density, lower unit cost, high read and write access speed, and has the characteristics of non-volatility, shock resistance, and low power consumption. [0003] Due to the continuous shrinking of the size of semiconductor devices and the use of high-density technology of Multi-level Cell (MLC), the influence of noise in storage is increasing. Major issues such as extended delays. Storage reliability is a key technic...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G11C16/34H03M13/11
CPCG11C16/3404H03M13/1125
Inventor 孔令军李俊王诚韦康赵熙唯
Owner NANJING UNIV OF POSTS & TELECOMM