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Proceedings Paper

Metrology of 3D-NAND structures using machine learning assisted fast marching level-sets algorithm
Author(s): Umesh Adiga; Derek Higgins; Sang Hoon Lee; Mark Biedrzycki; Dan Nelson
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Paper Abstract

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.

Paper Details

Date Published: 20 March 2020
PDF: 9 pages
Proc. SPIE 11325, Metrology, Inspection, and Process Control for Microlithography XXXIV, 1132530 (20 March 2020); doi: 10.1117/12.2552080
Show Author Affiliations
Umesh Adiga, Thermo Fisher Scientific Inc. (United States)
Derek Higgins, Thermo Fisher Scientific Inc. (United States)
Sang Hoon Lee, Thermo Fisher Scientific Inc. (United States)
Mark Biedrzycki, Thermo Fisher Scientific Inc. (United States)
Dan Nelson, Thermo Fisher Scientific Inc. (United States)


Published in SPIE Proceedings Vol. 11325:
Metrology, Inspection, and Process Control for Microlithography XXXIV
Ofer Adan; John C. Robinson, Editor(s)

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