Machine learning model for predicting threshold voltage by taper angle variation and word line position in 3D NAND flash memory

In this letter, a machine learning (ML) model is presented to predict the variation of the threshold voltage (V-th) according to the taper angle and target word line (WLT) position in 3D NAND flash memory. Through Technology Computer-Aided Design (TCAD) simulation, V-th is extracted according to taper angle and WLT position. TCAD data is used as the training data set required for learning by an artificial neural network algorithm (NNA). The completed ML model is then used to predict V-th for each word line (WL). It was also confirmed that the ML model predicted well even for TCAD data that was not used as a training data set.