Share Email Print
cover

Proceedings Paper • new

Using machine learning technology to accelerate the development of plasma etching processes
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The latest advances in Machine Learning (ML) produce results with unprecedented accuracy, and could signal a new era in the smart manufacturing field. We propose a framework designed to work alongside experts: learning from them and optimizing their knowledge. This framework must be considered as a tool to assist the experts in their daily work. The user creates a measurement recipe which includes an example of the feature as well as the measurements placed by the process engineer. Grouping the measurement recipes of the same object in an entity collection allows the user to train a machine learning recipe which includes a deformation model to handle variations in structure and contrast. The new images are analyzed following the machine learning pipeline which includes the detection of features, repositioning, measurement, quality evaluation and finally the results of measurement are given to the user. We discuss the pipeline and we focus on the metrics to validate the machine learning recipe, providing quantitative results for stability and robustness to variations.

Paper Details

Date Published: 20 March 2019
PDF: 8 pages
Proc. SPIE 10963, Advanced Etch Technology for Nanopatterning VIII, 109630C (20 March 2019); doi: 10.1117/12.2514705
Show Author Affiliations
A. Derville, POLLEN Metrology (France)
G. Gey, POLLEN Metrology (France)
J. Baderot, POLLEN Metrology (France)
S. Martinez, POLLEN Metrology (France)
G. Bernard, POLLEN Metrology (France)
J. Foucher, POLLEN Metrology (France)


Published in SPIE Proceedings Vol. 10963:
Advanced Etch Technology for Nanopatterning VIII
Richard S. Wise; Catherine B. Labelle, Editor(s)

© SPIE. Terms of Use
Back to Top