Modeling of 3D NAND Characteristics for Cross‐Temperature by Using Graph Neural Network and Its Application

Abstract

Herein, the impact of cross‐temperature on 3D NAND flash memory is modeled by considering adjacent cells using machine learning. The cells comprising NAND flash memory exhibit diverse states and connectivity patterns. To effectively capture this complexity, the 3D NAND flash is converted to graph structure and the graph neural network (GNN) is leveraged, known for its exceptional performance in handling graph data. To the best of the authors' knowledge, this is the first attempt to model 3D NAND flash memory using GNN. This method has good generalization performance across various retention times and temperatures, achieving a remarkable overlap of 95.28% between ground truth and predicted distributions. Moreover, two applications of this method are introduced that contribute to the NAND flash memory improvement. One is a GNN‐assist program, which leverages well‐trained GNN to suppress the Vth degradation affected by cross‐temperature, resulting in reduced Vth shift and narrower Vth distribution width. The other is the sensitivity decomposition to identify parameters influencing the cell at cross‐temperature. It is found that cross‐temperature impact extends beyond physically connected cells to adjacent cells at close distances. Overall, this work provides valuable insights into modeling 3D NAND flash memory using GNNs and offers practical methods for enhancing NAND flash memory reliability.

Keywords

• cross-temperature
• graph neural networks
• machine learning
• neural networks
• 3D NAND flash