We demonstrate that Direct Laser Writing (DLW) can be used to print low loss planar polymer waveguides on glass, thus promising a novel soft matter platform for polymer all-optic micro-photonics. We printed straight waveguides with various cross sections and lengths up to 900 mu m on a 500 nm thin layer of low refractive index CYTOP on glass. We also printed two rectangular micro-prisms at each end of the waveguide, which provides coupling of light in and out of the waveguides. The printed structures were imaged and characterized by SEM and we measured the attenuation of light, propagating along the waveguides. While the high refractive index photosensitive resin IP-n162 shows moderate attenuation of similar to 14 dB/cm at 580 nm, the IP-S photosensitive resin shows lower attenuation of similar to 5-9 dB/cm in a rather broad window around 580 nm.

Photonic defect modes are explored as a viable alternative to standard photonic band edge modes in photonic crystal applications, especially due to their typically high Q-factors and local density of states. For example, they can be used in nonlinearity enhancement, lasing, and cavity quantum electrodynamics. However, they are strongly dependent on any structural change and need to be well-controlled to ensure the desired resonance frequency. Here, we present a study of the photonic defect modes that appear in a structure where a layer of isotropic material is embedded between two layers of cholesteric liquid crystal (CLC), using full electrodynamics numerical simulations. We present typical transmission spectra and electric field profiles of selected defect modes and then analyze the influence of geometrical and material parameters on the eigenfrequencies and Q-factors of the modes within and around the photonic bandgap, including refractive indices and thicknesses of isotropic and liquid crystal layers, and different anchoring orientations at the boundaries of the isotropic defect layer. Additionally, a connection of such defect modes to previously extensively analyzed twist defect modes is given. Eigenmodes in asymmetric resonators are also presented, where CLC layers surrounding the intermediate isotropic layer are not equally thick, enabling biasing of specific directional light emission. More generally, this work aims to contribute to the understanding and design capability in topological soft matter photonics where defect mode lasing could be realized in CLC geometries with different singular and solitonic topological defect structures.

Liquid crystals are transparent optically birefringent materials that have the ability to self-assemble into tunable photonic microstructures. They can be modified by adding chiral dopants, by anchoring on confining surfaces, temperature changes, and by external electric or magnetic fields. Cholesteric liquid crystals (CLCs), which have a periodic helical structure, act as photonic crystals and thus partially reflect light with wavelengths comparable to the period of the structure. Possessing these properties, CLCs can be utilized as resonators or even as micro-lasers if doped with organic dye. In this work, we present the findings of a numerical study of light transmission through CLCs with or without isotropic defect layers in different 1D geometries. We also show numerically calculated photonic eigenmodes and their corresponding Q-factors. Overall, this work summarizes the properties of CLC resonators that could be important for the design of liquid crystal micro-lasers and other soft-matter-based photonic devices.

Cholesteric liquid crystals exhibit a periodic helical structure that partially reflects light with wavelengths comparable to the period of the structure, thus performing as a one-dimensional photonic crystal. Here, we demonstrate a combined experimental and numerical study of light transmittance spectra of finite-length helical structure of cholesteric liquid crystals, as affected by the main system and material parameters, as well as the corresponding eigenmodes and frequency eigenspectra with their Q -factors. Specifically, we have measured and simulated transmittance spectra of samples with different thicknesses, birefringences and for various incident light polarisation configurations as well as quantified the role of refractive index dispersion and the divergence of the incident light beam on transmittance spectra. We identify the relation between transmittance spectra and the eigenfrequencies of the photonic eigenmodes. Furthermore, we present and visualize the geometry of these eigenmodes and corresponding Q -factors. More generally, this work systematically studies the properties of light propagation in a one-dimensional helical cholesteric liquid crystal birefringent profile, which is known to be of interest for the design of micro-lasers and other soft matter photonic devices.

Supervised machine learning and artificial neural network approaches can allow for the determination of selected material parameters or structures from a measurable signal without knowing the exact mathematical relationship between them. Here, we demonstrate that material nematic elastic constants and the initial structural material configuration can be found using sequential neural networks applied to the transmmited time-dependent light intensity through the nematic liquid crystal (NLC) sample under crossed polarizers. Specifically, we simulate multiple times the relaxation of the NLC from a random (qeunched) initial state to the equilibirum for random values of elastic constants and, simultaneously, the transmittance of the sample for monochromatic polarized light. The obtained time-dependent light transmittances and the corresponding elastic constants form a training data set on which the neural network is trained, which allows for the determination of the elastic constants, as well as the initial state of the director. Finally, we demonstrate that the neural network trained on numerically generated examples can also be used to determine elastic constants from experimentally measured data, finding good agreement between experiments and neural network predictions.