A machine learning based framework for correcting the interlayer misalignment of physical design in 3D-NAND flash
Cheol-Hwan Kim, Jong-Min Lee, Ji-Chang Sim, Sang-Eun Go, Hyo-Seok Woo, Dong-Yun Kim, Oh-Hun Kwon, Bo-Tak Lim, Jae-Sun Yun, Hongsoo Kim, Hyuck‐Joon Kwon, Jungyun Choi, Hyung-Jong Ko · 2022
NAND flash memory is widely used as the primary storage medium with the development of IT technology. In order to increase the memory density and reduce cost per bit, Vertical-NAND (VNAND) technology has attracted attention. In VNAND technology, the cell array is formed in a vertical direction by stacking the multiple multi oxide/nitride layer deposition (MOLD) layer. As the number of stacked layer (Word-Line) is increased, however, several technical issues have been encountered such as miss-alignment between stacked layers, which is caused by MOLD shrinkage in memory process like etching and anneal. The mis-alignment leads to the bridge between word-line cut (WL-CUT) and channel hole (Ch. Hole), it causes a major failure in cell region. In present study, we proposed a novel automated system called Progressive Mis-Align Correction system (PMAC), which compensates for the systematic defect caused by misalignment between stacked layers. We also introduced a modelling method to estimate the degree of mis-align in unsampled regions using Random forest machine learning algorithm. In order to evaluate the performance, we applied the PMAC system to WL-CUT layer of 170-layer NAND product. We compared the turnaround time (TAT) and accuracy of PMAC system and the way of manual correction. We confirmed that the proposed system using a tool that has correction technology for each shape effectively reduced the turnaround time by up to 90% (14d to 2d) and the error of correction by up to 0.3nm. These results suggest that the PMAC system could improve the productivity.