This work presents a simulator for predicting the program threshold voltage (Vt) distribution in charge trap–based 3D NAND flash memory by considering Z-interference caused by random grain boundaries in the poly-Si channel. A machine learning model trained with TCAD simulation data is used to analyze Vt variation in victim cells under random programming conditions. The results show that controlling grain boundary characteristics is critical for accurate Vt prediction and process optimization.