Improving multi-photon lithography with machine learning

Julian Hering; Nicolas Lang; Matthias Eifler; Andrej Keksel; Jörg Seewig; Georg von Freymann

doi:10.1117/12.2607238

5 March 2022 Improving multi-photon lithography with machine learning

Julian Hering, Nicolas Lang, Matthias Eifler, Andrej Keksel, Jörg Seewig, Georg von Freymann

Proceedings Volume PC12012, Advanced Fabrication Technologies for Micro/Nano Optics and Photonics XV; PC120120M (2022) https://doi.org/10.1117/12.2607238
Event: SPIE OPTO, 2022, San Francisco, California, United States

Abstract

To further improve the technology of 3D µ-printing, we show a promising deep learning approach for correcting aberrations of the most prominent point spread functions in (STED-inspired) multi-photon lithography. Moreover, detailed forecasts of 3D printed structures are of high interest. Therefore, an analytical method predicting deformations due to, e.g. proximity or shrinkage effects is presented. These predictions can be used as pre-compensations to achieve a maximum match between target and actual structures from the beginning. As third topic, we discuss the recently presented continuous frequency band chirp material measure for calibration utilization with regard to its different evaluation routines.

Conference Presentation

Citation Download Citation

Julian Hering, Nicolas Lang, Matthias Eifler, Andrej Keksel, Jörg Seewig, and Georg von Freymann "Improving multi-photon lithography with machine learning", Proc. SPIE PC12012, Advanced Fabrication Technologies for Micro/Nano Optics and Photonics XV, PC120120M (5 March 2022); https://doi.org/10.1117/12.2607238

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KEYWORDS

Lithography

Multiphoton lithography

Point spread functions

Machine learning

Aberration correction

Artificial intelligence

Calibration

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