Machine learning in multi-photon laser lithography

Julian Hering-Stratemeier; Sven Enns; Nicolas Lang; Georg von Freymann

doi:10.1117/12.3021999

18 June 2024 Machine learning in multi-photon laser lithography

Julian Hering-Stratemeier, Sven Enns, Nicolas Lang, Georg von Freymann

Proceedings Volume PC12995, 3D Printed Optics and Additive Photonic Manufacturing IV; PC1299507 (2024) https://doi.org/10.1117/12.3021999
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

3D µ-printing is a versatile technology with huge potential for fabricating high-quality microstructures. However, most structures initially deviate from their designed dimensions due to photo resin properties and/or optical aberrations. We present a deep learning approach to predict and subsequently correct these optical aberrations in high numerical aperture systems, commonly employed in multi-photon lithography. The neural network identifies and calculates corrections for prominent aberrations and allows for easy scaling to arbitrary laser wavelengths. We also demonstrate our first steps of a machine learning approach that allows pre-compensation of microstructures without several (intensive) iterative correction prints.

Conference Presentation

Citation Download Citation

Julian Hering-Stratemeier, Sven Enns, Nicolas Lang, and Georg von Freymann "Machine learning in multi-photon laser lithography", Proc. SPIE PC12995, 3D Printed Optics and Additive Photonic Manufacturing IV, PC1299507 (18 June 2024); https://doi.org/10.1117/12.3021999

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KEYWORDS

Lithography

Machine learning

Multiphoton lithography

Optical aberrations

3D microstructuring

Laser microstructuring

Laser applications

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