Single-mode optical waveguides are one of the fundamental photonic components and the building block for compact multicore bundles. The cross-talk of a waveguide bundle might scramble the information and reduce the resolution. To get the highest possible resolution on a fixed field of view (FOV), we propose to optimize the core to core spacing via assessing the reconstruction accuracy of the output images of the bundle processed by a deep neural network (DNN), where the obtained bundle is 3D printed via direct laser writing. We demonstrate the DNN based optimization scheme and the fabrication of a waveguide bundle of 10-µm core-to-core spacing to image various digit layouts in a (120 µm)^2 FOV.
Owing to its many outstanding properties, polydimethylsiloxane (PDMS) has been an important engineering elastomer. An integration of compact, low-loss, three-dimensional (3D) optical waveguides with fluidic functionalities will greatly enhance the capabilities of PDMS-based devices and enable heretofore unimaginable applications. Recently, we have demonstrated the fabrication of compact polymer waveguides in PDMS through multiphoton laser direct writing both with and without a photoinitiator. While the photoinitiator-free process enjoyed a high refractive index contrast and a very low optical loss, it was subject to excessive defects due to material damage during the high-intensity optical irradiation. Our process using a photoinitiator achieved a defect-free fabrication despite a lower refractive index contrast and a higher loss. We show that the defects are a result of uncontrolled optical beam collapse through self-focusing and discuss potential solutions to mitigate the defects.
Advancements in 3D printing, specifically by 2-photon polymerization, enabled the fabrication of high resolution novel optical elements for various applications by engineering the topography of the structures. Recently, the ability to obtain a varying refractive index distribution by 2-photon polymerization also started to gain momentum to design and produce gradient-index (GRIN) micro-optics. Here, we demonstrate micro-scale volume holograms by 2-photon polymerization by tuning the exposure point by point in the 3D volume to obtain the necessary refractive index distribution. 3D printed micro-scale GRIN volume holograms lay an opportunity to open a complimentary dimension in various systems thanks to in-situ fabrication advantage, which can provide complex interconnections and beam shaping in micro-scale.
Obtaining a varying refractive index distribution has always been attracting a high interest in the optics community to produce gradient-index (GRIN) optics. The conventional way to store and process data by GRIN media is through volume holograms, where the recording is done by optical means, which prevents independently accessing each point in the volume. Additive manufacturing, specifically 2-photon polymerization, inherits this ability. Considering the scalability advantage of the 3D implementation of computation architectures and the power-speed advantage of optics, there lie many opportunities for additively manufactured GRIN optics performing complex tasks. Independent access to each voxel in fabrication volume opens the way for digital optimization techniques to design GRIN optics since each calculated voxel can be translated into fabrication. In this work, Learning Tomography (LT), which is a nonlinear optimization algorithm originally developed for optical diffraction tomography, is used as the optimization framework to calculate necessary refractive index distribution to perform computation tasks such as matrix multiplication. Here, instead of imaging an object in optical diffraction tomography, we calculate the 3D GRIN element that performs the desired task as defined by its input-output relation. This input-output relation can be chosen such that a computational functionality is satisfied. We report functional robust GRIN elements where the refractive index dynamic range (<0.005) is comparable to the dynamic range of conventional holography materials. We present the digital optimization methodology with details on the beam propagation method as the forward model and the corresponding error reduction scheme for the desired input-output mapping along with the experimental verification of the approach along with the details of the fabrication process by additive manufacturing.
Owing to its excellent elasticity, wide spectral range of transparency, and outstanding chemical and thermal stability, polydimethylsiloxane (PDMS) is an elastomer of great technological importance, being widely used in the fabrication of microfluidic and optofluidic devices in particular. Compact, low loss optical waveguides are crucial in such devices for a dense integration of optical functionalities. Compared with photolithographic methods, multiphoton laser direct writing through photopolymerization has shown great promise in the fabrication of three-dimensional (3D) optical waveguides in PDMS without the limit of planar structures. Here we demonstrate a multiphoton laser direct writing process that produces low-loss, ultra-compact waveguides in PDMS. The fabrication employs a self-initiated multiphoton polymerization of phenylacetylene infused in PDMS without additional photoinitiator. The elimination of the photoinitiator results in an excellent refractive index homogeneity and thus a low propagation loss due to scattering, as well as a greater biocompatibility by involving the fewest possible chemicals (monomer only) in the polymerization reaction. Our characterizations show that the waveguides fabricated as such are on average 1.3 µm wide with a refractive index contrast of 0.06 and a propagation loss of 0.03 dB/cm in the spectral band of 650-700 nm. Our technique will enable a broad range of applications spanning from wearable photonics to chip-scale optical interconnects.
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