## 1 Introduction
### Contribution  
Optimizing access latency by using inter-page or inter-layer error characteristics under flash memory channels to optimize reading reference voltage or LLR information to reduce access latency usually requires sacrificing storage space to obtain additional redundant bits or increasing the latency of read operations, especially in scenarios with high-frequency read and random access, where the potential advantages of such tightly coupled read and decode operations are difficult to fully utilize. Motivated by this problem, this paper proposes an innovative method based on the bit reliability imbalance in LDPC codewords and develops serial entropy feature-based LNMS (S-EFB-LNMS) and parallel entropy feature-based LNMS (P-EFB-LNMS) LDPC decoding algorithms based on entropy feature matrices. These algorithms aim to reduce access latency, reduce additional space usage, and decouple dense interactions between pages. First, an entropy feature vector is constructed to quantify the reliability of each bit in the codeword. Then, cosine similarity is applied to assess the reliability of the LPU. Finally, an LDPC decoding strategy based on the entropy feature vector is implemented, and its effectiveness is verified through experiments. The main contributions of this paper are as follows:  
1. An LPU reliability assessment method based on the entropy feature vector of codewords is proposed. This method provides a basis for selecting the appropriate LPU for scheduling during the iteration process. 2. Based on the reliability assessment of LPU, S-EFB-LNMS and P-EFB-LNMS LDPC decoding algorithms are proposed for serial and parallel architectures, respectively. These algorithms effectively optimize the transmission of redundant information in the decoding process by adjusting the scheduling strategy of LPU in each iteration, thereby reducing unnecessary calculation and decoding latency. 3. A comprehensive performance evaluation of the proposed algorithm is carried out, which confirms that the algorithm can significantly reduce the average number of LPUs in each iteration and the total number of LPU executed in the decoding process, significantly improving the time efficiency of the decoding process. In addition, through a detailed space overhead analysis, it is proved that the proposed algorithm effectively reduces the additional space occupation. The complexity analysis of the algorithm reveals its linear growth characteristics, indicating that the algorithm shows the advantages of efficiency and practicality when processing large-scale data sets. The rest of this paper is organized as follows. Section 2 first elaborates on the construction of the entropy feature vector of the codeword and then presents the LPU reliability evaluation method. Section 3 introduces the basic framework of the LDPC LNMS decoding algorithm and then proposes improved S-EFB-LNMS and P-EFB-LNMS LDPC decoding algorithms. Section 4 presents the performance analysis and discussion. Section 5 concludes this paper. _Notations:_ In this paper, the following parameters are used:  
* \(H(v)\): Voltage entropy function, where \(v\) represents the sensing threshold voltage. * \(R_{i}\): The \(i\)-th read reference voltage. * \(\varepsilon_{i}\): The voltage window between \(R_{i-1}\) and \(R_{i}\). Specifically, \(\varepsilon_{1}\) represents a voltage interval of less than \(R_{1}\) and \(\varepsilon_{7}\) represents a voltage interval greater than \(R_{6}\). * \(LLR_{\text{ch}}\): LLR obtained from the flash memory channel, where \(LLR_{\text{ch},j}\) represents the LLR of the \(j\)-th bit in a codeword. * \(\mathbf{f}_{\text{efv}}\): The entropy feature vector of a codeword, where \(f_{\text{efv},j}\) represents the entropy feature value of the \(j\)-th bit. * \(\mathbf{c}_{\text{cmp}}\): The CN processing unit constraint vector, corresponding to a row of the LDPC matrix \(\mathbf{H}\). * \(CS(\mathbf{f}_{\text{efv}},\mathbf{c}_{\text{cmp}})\): The cosine similarity between \(\mathbf{f}_{\text{efv}}\), and \(\mathbf{c}_{\text{cmp}}\).