DNN-aided Read-voltage Threshold Optimization for MLC Flash Memory with Finite Block Length

The error correcting performance of multi-level-cell (MLC) NAND flash memory is closely related to the block length of error correcting codes (ECCs) and log-likelihood-ratios (LLRs) of the read-voltage thresholds. Driven by this issue, this paper optimizes the read-voltage thresholds for MLC flash memory to improve the decoding performance of ECCs with finite block length. First, through the analysis of channel coding rate (CCR) and decoding error probability under finite block length, we formulate the optimization problem of read-voltage thresholds to minimize the maximum decoding error probability. Second, we develop a cross iterative search (CIS) algorithm to optimize read-voltage thresholds under the perfect knowledge of flash memory channel. However, it is challenging to analytically characterize the voltage distribution under the effect of data retention noise (DRN), since the data retention time (DRT) is hard to be recorded for flash memory in reality. To address this problem, we develop a deep neural network (DNN) aided optimization strategy to optimize the read-voltage thresholds, where a multi-layer perception (MLP) network is employed to learn the relationship between voltage distribution and read-voltage thresholds. Simulation results show that, compared with the existing schemes, the proposed DNN-aided read-voltage threshold optimization strategy with a well-designed LDPC code can not only improve the program-and-erase (PE) endurance but also reduce the read latency.

Neural Network-Based Dynamic Threshold Detection for Non-Volatile Memories

The memory physics induced unknown offset of the channel is a critical and difficult issue to be tackled for many non-volatile memories (NVMs). In this paper, we first propose novel neural network (NN) detectors by using the multilayer perceptron (MLP) network and the recurrent neural network (RNN), which can effectively tackle the unknown offset of the channel. However, compared with the conventional threshold detector, the NN detectors will incur a significant delay of the read latency and more power consumption. Therefore, we further propose a novel dynamic threshold detector (DTD), whose detection threshold can be derived based on the outputs of the proposed NN detectors. In this way, the NN-based detection only needs to be invoked when the error correction code (ECC) decoder fails, or periodically when the system is in the idle state. Thereafter, the threshold detector will still be adopted by using the adjusted detection threshold derived base on the outputs of the NN detector, until a further adjustment of the detection threshold is needed. Simulation results demonstrate that the proposed DTD based on the RNN detection can achieve the error performance of the optimum detector, without the prior knowledge of the channel.