Bi-Directional Long Short-Term Memory Neural Network Modeling of Data Retention Characterization in 3-D Triple-Level Cell NAND Flash Memory
Hyundong Jang, Chanyang Park, Kihoon Nam, Hyeok Yun, Kyeongrae Cho, Jun-Sik Yoon, Hyun‐Chul Choi, Ho-Jung Kang, Min Sang Park, Jaesung Sim, Rock‐Hyun Baek · IEEE Transactions on Electron Devices · 2022
Data retention (a time-variant characteristic of 3-D- NAND flash memory) is predicted through a bi-directional long short-term memory (LSTM) neural network (NN) model that learns sequential data obtained from chip measurements of a triple-level cell (TLC). The predicted results for all time points of each program (PGM) state are accurately predicted by the threshold voltage (${V}_{\text {th}}$) distribution. Thus, the predicted${V}_{\text {th}}$can be used to analyze the cause of retention failure. When the${V}_{\text {th}}$of the target cell is high or when that of the adjacent cell is small, the${V}_{\text {th}}$loss of the target cell is large. In addition, the${V}_{\text {th}}$loss increases as the${V}_{\text {th}}$of the adjacent cell decreases. Using a fully calibrated TCAD simulation, we verify the NN-based${V}_{\text {th}}$prediction by checking the change in the electron concentration in the nitride layer. Furthermore, the NN model predicts the${V}_{\text {th}}$for cells existing in other blocks, showing that they are consistent with the measured${V}_{\text {th}}$. The prediction times were 5$\times \,\,10^{{5}}$s, 5$\times \,\,10^{{6}}$s, and 2$\times \,\,10^{{6}}$s, but using machine learning (ML), we reduced the time required to predict the${V}_{\text {th}}$to only 2 s. Therefore, the proposed ML method enables fast, accurate, and effective predictive modeling of the time-variant${V}_{\text {th}}$of 3-D TLC NAND flash memory. Finally, the predicted${V}_{\text {th}}$can be included in the read retry table or included in the lookup table of the compensation circuit in NAND solutions. This can save a significant amount of time that would otherwise be spent on actual long-term measurements.