Metrology of 3D-NAND structures using machine learning assisted fast marching level-sets algorithm

Accurate segmentation of 3D-NAND memory cells and the interfaces of different materials within is the basis of reliable metrology for 3D-NAND memory fabrication. We are proposing a machine learning assisted fast marching level sets method (FMLS) to efficiently delineate material interfaces within 3D-NAND cells. This method works with single or multiple seed initialization that evolves and propagates towards object boundaries independent of topological merger and splitting. Images containing thousands of NAND cells can be processed within a few seconds using this method, making this a very convenient tool for inline metrology during fabrication. With an appropriate preprocessing, FMLS can be used to segment nonconvex structures, such as fins and gates, too.