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.
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