Assessing Scanning Electron Microscopy Stereophotogrammetry Algorithms with Virtual Test Samples

Published

March 20, 2017

Author(s)

John S. Villarrubia, Vipin Tondare, Andras E. Vladar

Abstract

Modern electronic devices are non-planar and have functional dependencies on device sizes in all three dimensions. Scanning electron microscope (SEM) images have spatial resolution at the needed nanometer level, but like most microscope images they are graphs of intensity vs. position in an image plane. I.e., there are only two spatial dimensions. The needed third dimension may be extracted from combinations of images taken from varying viewpoints. A 3D model of the sample may then be reconstructed using the methods of stereophotogrammetry. We evaluate the performance of stereophotogrammetry software using virtual test samples: mathematical (hence with all dimensions known) objects from which realistic images are generated using the JMONSEL SEM simulator. The simulated images become inputs to the software, which reconstructs a shape that is compared to the known shape. In tests of existing automated software, good reconstruction was achieved only on the roughest of 3 virtual test samples. Better manual reconstruction was possible, indicating that better software design could achieve higher accuracy than currently available software.

Proceedings Title

Frontiers of Characterization and Metrology for Nanoelectronics 2017

Conference Dates

March 21-23, 2017

Conference Location

Monterey, CA, US

Conference Title

2017 International Conference on Frontiers of Characterization and Metrology for Nanoelectronics

Pub Type

Conferences

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Keywords

Monte Carlo SEM modeling, nanometer-scale dimensional metrology, scanning electron microscopy, stereophotogrammetry

Citation

Villarrubia, J. , Tondare, V. and Vladar, A. (2017), Assessing Scanning Electron Microscopy Stereophotogrammetry Algorithms with Virtual Test Samples, Frontiers of Characterization and Metrology for Nanoelectronics 2017, Monterey, CA, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=922328 (Accessed October 10, 2025)