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Proceedings Paper

Machine Learning predicts printing parameters for multi-photon polymerization three-dimensional direct laser writing (3D-DLW) (Conference Presentation)
Author(s): Areti Mourka; Georgios D. Barmparis; Dimitra Ladika; Vasileia Melissinaki; David Gray; Maria Farsari

Paper Abstract

We are presenting a model for a quantitative description of the polymerization process in 3D-laser microfabrication. With aim to assist in estimating the necessary power threshold to obtain certain feature size, particularly the line characteristics, depending on the laser power and writing speed. The focal distribution as well as the photoresist is taken into account. We do not try to gain any chemical insight into the processes involved, and restrict us to a quantitative study of a multi-photon process. Machine learning is used to classify the input SEM images providing a look-up table as a custom field for optimized parameter selection.

Paper Details

Date Published: 9 March 2020
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Proc. SPIE 11271, Laser 3D Manufacturing VII, 112710A (9 March 2020); doi: 10.1117/12.2544839
Show Author Affiliations
Areti Mourka, Foundation for Research and Technology-Hellas (Greece)
Georgios D. Barmparis, Foundation for Research and Technology-Hellas (Greece)
Univ. of Crete (Greece)
Dimitra Ladika, Foundation for Research and Technology-Hellas (Greece)
Univ. of Crete (Greece)
Vasileia Melissinaki, Foundation for Research and Technology-Hellas (Greece)
David Gray, Foundation for Research and Technology-Hellas (Greece)
Maria Farsari, Foundation for Research and Technology-Hellas (Greece)


Published in SPIE Proceedings Vol. 11271:
Laser 3D Manufacturing VII
Bo Gu; Hongqiang Chen; Henry Helvajian, Editor(s)

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