Share Email Print

Proceedings Paper

Machine Learning assistant technology to facilitate Fin and 3D memory measurements on SEM and TEM images
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

We present a machine learning-based metrology pipeline for electron microscope imagery in the semiconductor industry. The pipeline is targeted to reduce the time spent by Process Engineers during research and development, by automating measurements of features according to their instructions in the form of a “measurement recipe”. Specifically, we present the principles and functionality of tools to measure Fin and 3D Memory structures based on edge finding algorithms, including through direct modelling of the SEM acquisition process to better capture blurred-appearing features.

Paper Details

Date Published: 23 March 2020
PDF: 9 pages
Proc. SPIE 11329, Advanced Etch Technology for Nanopatterning IX, 113290X (23 March 2020);
Show Author Affiliations
J. Baderot, POLLEN Metrology (France)
B. Darbon, POLLEN Metrology (France)
N. Clement, POLLEN Metrology (France)
M. Bryan, POLLEN Metrology (France)
S. Martinez, POLLEN Metrology (France)
J. Foucher, POLLEN Metrology (France)

Published in SPIE Proceedings Vol. 11329:
Advanced Etch Technology for Nanopatterning IX
Richard S. Wise; Catherine B. Labelle, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?