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.
Metrology of 3D NAND device architecture is challenging due to structural complexity, low signal to noise and contrast to noise ratio in the electron micrographs. Efficient, automated tools that can measure critical dimensions of 3D NAND in electron micrographs can be a part of solution for process monitoring, uniformity control and structural modelling through OCD, CD-SEM, e-beam tech., etc. In this paper we present an automated technique based on a snake algorithm in a multi-stage, scale-space framework to delineate continuous interfaces between different materials/layers of 3D NAND cells. The snake algorithm takes an initial contour and forces the initial contours to move and deform towards the interface between different material layers using an iterative energy minimization process while preserving the continuity and smoothness of the contour. At the end of energy minimization, the interface between different materials such as central hole, core silicon oxide, poly silicon, tunneling silicon oxide, silicon nitride with storage function, blocking silicon oxide, outer metal layers, etc., are delineated and marked by labelled contours. Prior knowledge, if available, about the number of material-layers and approximate distance between them can be used to improve the efficiency of the process. The proposed method transforms the micrograph into a digital metrology image where critical dimensions such as thickness of the materials, shape of the material layers, etc., can be automatically measured. The additional information provided by continuous contours can be used with ‘Big Data’ analytics to uncover patterns, variations, and outliers that may go unnoticed in discrete measurement data.