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Proceedings Paper

Cognitive learning: a machine learning approach for automatic process characterization from design
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Paper Abstract

Cutting edge innovation requires accurate and fast process-control to obtain fast learning rate and industry adoption. Current tools available for such task are mainly manual and user dependent. We present in this paper cognitive learning, which is a new machine learning based technique to facilitate and to speed up complex characterization by using the design as input, providing fast training and detection time. We will focus on the machine learning framework that allows object detection, defect traceability and automatic measurement tools.

Paper Details

Date Published: 16 March 2018
PDF: 6 pages
Proc. SPIE 10585, Metrology, Inspection, and Process Control for Microlithography XXXII, 105852R (16 March 2018); doi: 10.1117/12.2297348
Show Author Affiliations
J. Foucher, POLLEN Metrology (France)
J. Baderot, POLLEN Metrology (France)
Gipsa Lab. (France)
S. Martinez, POLLEN Metrology (France)
A. Dervilllé, POLLEN Metrology (France)
Lab. Jean Kuntzmann Grenoble (France)
G. Bernard, POLLEN Metrology (France)

Published in SPIE Proceedings Vol. 10585:
Metrology, Inspection, and Process Control for Microlithography XXXII
Vladimir A. Ukraintsev, Editor(s)

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