TY - GEN
T1 - Machine Learning-Driven Modeling for Fast and Accurate Z-Interference Estimation in 3D NAND Scaling
AU - Yun, Hyeon Seo
AU - Park, Jong Kyung
N1 - Publisher Copyright:
© 2025 Japan Society of Applied Physics.
PY - 2025
Y1 - 2025
N2 - This study presents a CatBoost-based regression model to predict Z-interference (Z-ITF) in scaled 3D NAND Flash. Unlike traditional TCAD or curve fitting, the model uses initial/final attack and victim cell states, improving adaptability to future scaling. It accurately predicts Vth shifts across various WL pitches, showing a 2-3× Z-ITF increase as ON pitch shrinks from 63 nm to 50 nm. The model helps identify critical scaling limits for improved reliability and error correction.
AB - This study presents a CatBoost-based regression model to predict Z-interference (Z-ITF) in scaled 3D NAND Flash. Unlike traditional TCAD or curve fitting, the model uses initial/final attack and victim cell states, improving adaptability to future scaling. It accurately predicts Vth shifts across various WL pitches, showing a 2-3× Z-ITF increase as ON pitch shrinks from 63 nm to 50 nm. The model helps identify critical scaling limits for improved reliability and error correction.
UR - https://www.scopus.com/pages/publications/105013621282
U2 - 10.23919/SNW65111.2025.11097230
DO - 10.23919/SNW65111.2025.11097230
M3 - Conference contribution
AN - SCOPUS:105013621282
T3 - 2025 Silicon Nanoelectronics Workshop, SNW 2025
SP - 94
EP - 95
BT - 2025 Silicon Nanoelectronics Workshop, SNW 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 Silicon Nanoelectronics Workshop, SNW 2025
Y2 - 8 June 2025 through 9 June 2025
ER -