  The practical NAND flash memory suffers from various non-stationary noises
that are difficult to be predicted. Furthermore, the data retention noise
induced channel offset is unknown during the readback process. This severely
affects the data recovery from the memory cell. In this paper, we first propose
a novel recurrent neural network (RNN)-based detector to effectively detect the
data symbols stored in the multi-level-cell (MLC) flash memory without any
prior knowledge of the channel. However, compared with the conventional
threshold detector, the proposed RNN detector introduces much longer read
latency and more power consumption. To tackle this problem, we further propose
an RNN-aided (RNNA) dynamic threshold detector, whose detection thresholds can
be derived based on the outputs of the RNN detector. We thus only need to
activate the RNN detector periodically when the system is idle. Moreover, to
enable soft-decision decoding of error-correction codes, we first show how to
obtain more read thresholds based on the hard-decision read thresholds derived
from the RNN detector. We then propose integer-based reliability mappings based
on the designed read thresholds, which can generate the soft information of the
channel. Finally, we propose to apply density evolution (DE) combined with
differential evolution algorithm to optimize the read thresholds for LDPC coded
flash memory channels. Computer simulation results demonstrate the
effectiveness of our RNNA dynamic read thresholds design, for both the uncoded
and LDPC-coded flash memory channels, without any prior knowledge of the
channel.
