Photonic waveguide bundles using 3D laser writing and deep neural network image reconstruction.

Abstract

In recent years, three-dimensional (3D) printing with multi-photon laser writing has become an essential tool for the manufacturing of three-dimensional optical elements. Single-mode optical waveguides are one of the fundamental photonic components, and are the building block for compact multicore fiber bundles, where thousands of single-mode elements are closely packed, acting as individual pixels and delivering the local information to a sensor. In this work, we present the fabrication of polymer rectangular step-index (STIN) optical waveguide bundles in the IP-Dip photoresist, using a commercial 3D printer. Moreover, we reduce the core-to-core spacing of the imaging bundles by means of a deep neural network (DNN) which has been trained with a large synthetic dataset, demonstrating that the scrambling of information due to diffraction and cross-talk between fiber cores can be undone. The DNN-based approach can be adopted in applications such as on-chip platforms and microfluidic systems where accurate imaging from in-situ printed fiber bundles suffer cross-talk. In this respect, we provide a design and fabrication guideline for such scenarios by employing the DNN not only as a post-processing technique but also as a design optimization tool.