Machine learning in multi-photon laser lithography

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.