Hybrid Neural Network Model for Raw Bit Error Rate Prediction of 3D TLC NAND Flash Memory

3D TLC NAND flash memory achieves high storage density due to multi-bit technology and vertical stacked structure, while suffering from severe reliability issues, particularly a high raw bit error rate (RBER). Accurate prediction of RBER in 3D TLC NAND flash memory enables proactive data management, thereby enhancing performance, improving reliability, and extending the lifetime of the memory. This paper focuses on accurately estimating the RBER of flash memory to provide more detailed information for the precise control and optimization strategies. In this paper, we propose a Hybrid Neural Network model (HNN) for RBER prediction, which combines the advantages of multi-layer perceptron (MLP) and recurrent neural network (RNN). The model utilizes a multi-layer perceptron (MLP) for static feature extraction and the gated recurrent unit (GRU) for time series analysis. By combining these approaches, the HNN can effectively capture the complex relationships and temporal patterns that affect the RBER. Experimental results, based on actual chip data, demonstrate that the HNN can predict the RBER with an r-squared value greater than 0.96, significantly outperforming traditional machine learning algorithms and neural networks.