Towards efficient structure prediction and pre-compensation in multiphoton lithography

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

doi:10.1117/12.2647529

17 March 2023 Towards efficient structure prediction and pre-compensation in multiphoton lithography

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

Proceedings Volume PC12433, Advanced Fabrication Technologies for Micro/Nano Optics and Photonics XVI; PC124330I (2023) https://doi.org/10.1117/12.2647529
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

We show first steps towards a simple, fast, and easy to implement algorithm to predict the finally printed topography in Direct Laser Writing (DLW). These robust predictions can be used prior to the printing process to minimize undesired deviations between the experimental 3D print and its target. Consequently, this approach can eliminate the need for a multitude of structural optimization loops to produce highly conformal and high-quality microstructures in the future. Moreover, we show first neural networks trained with our prediction algorithm being able to pre-compensate known and foreign structures without the need of performing any iterative correction prints.

Conference Presentation

Citation Download Citation

Julian Hering-Stratemeier, Nicolas Lang, Sven Enns, and Georg von Freymann "Towards efficient structure prediction and pre-compensation in multiphoton lithography", Proc. SPIE PC12433, Advanced Fabrication Technologies for Micro/Nano Optics and Photonics XVI, PC124330I (17 March 2023); https://doi.org/10.1117/12.2647529

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available