ABSTRACT The integration of polarization‐control elements into nanoscale photonic circuits remains a central challenge for modern on‐chip optical technologies, which call for continuous miniaturization. Van der Waals (vdW) crystals provide a versatile platform for their creation because of strong optical anisotropy, low loss accompanied by high refractive indices, and atomically precise heterostructure assembly capabilities. In this work, we present an approach towards the creation of all‐vdW on‐chip birefringent polarization‐conversion elements operating in the near‐infrared (NIR) spectral region, realized by uniquely twisted heterostructures placed on a platform. Our proof‐of‐principle stacks demonstrate up to 64.5 average degree of circular polarization (DoCP) quantitatively reproduced by finite‐difference time‐domain (FDTD) simulations. The latter reveals critical roles of layer thickness, twist angle, and Fabry–Perot interference effects in the determination of polarization conversion efficiency, which yields DoCP values close to unity for a layer thickness of about twice that of zero‐order quarter‐wave plate conditions of a free‐standing slabs. Our findings establish a practical route toward fully integrated all‐vdW polarization optical elements, providing a foundation for nanoscale photonic architectures entirely based on layered materials.
ABSTRACT Optical vortices provide an additional freedom because of the orbital angular momentum (OAM), enabling significant advances in high‐dimensional information encoding, quantum communication, and super‐resolution imaging. However, the controlled conversion between spin‐orbit‐coupled (SOC) optical vortices of different orders remains challenging. Here, we propose a novel four‐step Floquet driving protocol governed by complex coupling to realize conversion between distinct angular momentum characteristics in strongly confined waveguide arrays. Optical vortices naturally give rise to complex‐valued coupling coefficients owing to their intrinsic spatial‐phase structure and spin–orbit interaction. Building on this mechanism, we implement a Floquet driving protocol in the waveguide array, which supports corner and edge states determined by the coupling parameters. Exploiting the corner states, we demonstrate dynamic conversion of total‐angular‐momentum order from to across successive two or three driving periods. This work opens a pathway toward dynamic modulation of structured light and advances the integration of OAM‐based functionalities in photonic platforms.
ABSTRACT Quaternary lithium thiostannate spinels represent a promising class of non‐toxic earth‐abundant semiconductor absorbers for photovoltaic applications, reported to be air‐stable in bulk form. To the best of our knowledge, no attempts to prepare their thin films and evaluate their photoresponse have been reported. Here, we demonstrate the first successful deposition of nanocrystalline Li 1.66 CuSn 3.33 S 8 spinel films (∼350 nm thickness) via pulsed laser deposition (PLD). We observe that the crystal structure of the PLD target governs the structure and optoelectronic properties of the resulting film. A phase‐pure spinel target yields a homogeneous layer with a high degree of crystallinity, strong optical absorptance with a clear absorption edge at ∼1.5 eV and low sub‐bandgap values, as determined by photothermal deflection spectroscopy. The photoluminescence peak centred at 836 nm, consistent with the absorption band edge, together with measurable photoconductivity, confirms semiconducting character. Therefore, we identify the PLD‐deposited Li 1.66 CuSn 3.33 S 8 films as a candidate for a photo‐absorber material, while noting that further improvements in film crystallinity, sulphur stoichiometry and Urbach energy are required before photovoltaic device integration can be assessed.
ABSTRACT Nonlinear optical nonreciprocity based on high‐Q resonances offers a promising route toward compact integrated photonic devices, yet most reported structures operate at a single resonance and are commonly analyzed using idealized Kerr‐only models. Here, we theoretically investigate a sawtooth‐shaped silicon grating supporting three symmetry‐protected bound states in the continuum (BICs) that evolve into high‐Q quasi‐BICs (QBICs) after in‐plane symmetry breaking. The three resonances exhibit distinct directional coupling asymmetries and consequently different nonlinear nonreciprocal responses. Within the idealized Kerr‐only model, full‐wave simulations predict a maximum NRIR of 2.99, whereas QBIC 2 reaches an isolation exceeding 67 dB at an incident intensity of 12.5 MW/cm 2 . To evaluate realistic parasitic nonlinearities in crystalline silicon, we further incorporate wavelength‐dependent material dispersion, two‐photon absorption, and TPA‐generated free‐carrier absorption and dispersion through a sequential frequency‐domain/time‐domain multiphysics workflow. For QBIC 2, the extended model predicts a representative isolation of 34.8 dB at 1296.13 nm and 12.5 MW/cm 2 . These results clarify how modal symmetry, directional coupling asymmetry, and realistic nonlinear material effects jointly govern multiwavelength nonreciprocal transmission, providing quantitative guidance for future experimental realization.
ABSTRACT Single‐frame fringe pattern analysis in digital holographic interferometry (DHI) is essential for precision phase measurement under dynamic conditions. Recent advances in deep learning within optical metrology are transforming the reconstruction paradigm from physics‐based modeling toward data‐driven learning. However, most existing neural networks either rely on learning statistical visual features without guidance from physical principles or fail to dynamically adjust inference parameters based on the input instance after training, thereby limiting the robustness and generalization capacity on unseen data. To this end, a novel adaptive spectrum filtering neural network (ASFNet) is proposed for phase measurement. ASFNet incorporates a physics‐aware spectral filtering block that dynamically adapts the inference process to each input instance, enabling robust and generalizable phase reconstruction under varying and unseen conditions—moving beyond static, dataset‐specific statistical modeling toward instance‐aware adaptive reconstruction. Moreover, a forward model is constructed to generate realistic simulation data for network training, reducing the reliance on experimentally labeled data. Simulation results on 400 test samples demonstrate the superiority of ASFNet in terms of both pixel‐wise accuracy and visual quality. Experimental results further confirm its strong generalization capability on unseen data, achieving high reconstruction fidelity comparable to that of hand‐tuned, physics‐based methods.
