Lightweight Failure Prediction Algorithms Based on Internal Characteristics of 3D NAND Flash Memory

3D NAND flash memory has attacked widespread attention due to its fast speed, high endurance, and strong reliability. However, its reliability decreases as program and erase time increases. To tackle this problem, current researches mostly employ machine learning models to predict flash memory failure, but there lacks the consideration of using the inter-layer difference and page type difference characteristics in flash memory chips to help failure prediction. Based on the internal characteristic of inter-layer difference and page type difference, two failure prediction algorithms are proposed in the paper, corresponding to the Standard1 and Standard2. For Standard1, an attention focused failure prediction (AFFP) algorithm is proposed. To predict the failure of the entire block, the proposed AFFP algorithm only focuses on the layer which is the most prone to failure and further predicts eight pages of the most likely failure pages within this layer. For Standard2, a low predict-frequency failure prediction (LPFFP) algorithm is proposed, which can reduce the frequency of failure prediction significantly and thus reduce the prediction overhead as much as possible. The experimental results show that, for Standard1, the AFFP algorithm can predict the block of failure accurately, and its data extraction and prediction overheads are reduced by 99.8% compared to the original algorithm, and meanwhile the F1-score exceeds 0.96. For Standard2, the LPFFP algorithm can predict the page of failure within a flash block accurately, and its F1-score exceeds 0.91 with a significant reduction in prediction overhead.