Evaluation of deep learning model for 3D profiling of HAR features using high-voltage CD-SEM

3D-NAND memory will continue to increase in the aspect ratio of channel holes. High throughput and in-line monitoring solutions for 3D profiling of high aspect ratio (HAR) features are the key for yield improvement. A deep learning (DL) model has been developed to improve the 3D profiling accuracy of the HAR features. In this work, the HAR holes with different bowing geometries were fabricated and a high-voltage CD-SEM was used to evaluate the performance of the DL model. The accuracy and the sensitivity of the DL model was evaluated by comparing the predicted cross-sections with the X-SEM measurement. The results show that the DL model enables the maximum CD (MCD) of the bowing features to be predicted with a sensitivity of 0.93 and its depth position to be predicted with a sensitivity of 0.91. The DL learning model reduced the absolute error of the predicted MCD depth position from several hundreds of nanometers, the error occurring when using the exponential model, to within 100 nm.