Surface Plasmon Resonance （SPR） sensing technology detects changes in environmental parameters， such as Refractive Index （RI）， by exciting surface plasmon waves through the interaction between incident light and free electrons on a metal surface. Fiber-optic SPR sensors have been widely applied in medical diagnostics， food safety， and environmental monitoring due to their compact size， low cost， and strong resistance to electromagnetic interference. However， conventional single-channel SPR sensors contain only one sensing region， which limits their capability for simultaneous multi-parameter detection in complex environments. Meanwhile， the emerging two-dimensional material Ti₃C₂-MXene （hereafter referred to as MXene）， featuring a high specific surface area and anisotropic electrical properties， has demonstrated great potential for enhancing sensor sensitivity and enabling accurate detection of trace analytes.

Leveraging the properties of MXene， in this study， a dual-channel No-core Fiber （NCF） SPR sensor based on MXene is proposed and optimized to address the demand for high-sensitivity and multi-parameter detection in complex environments. The sensor is constructed by cascading a Multimode Fiber （MMF） with two segments of NCFs. Channel 1 is coated with a gold film， while channel 2 is functionalized with a composite film of gold-cysteamine-MXene. Owing to the different dielectric constants of the two sensing coatings， SPR is excited at distinct spectral regions， enabling dual-channel RI sensing. The optimal structural parameters， including a 50 nm gold layer， NCF lengths of 5 mm （channel 1） and 12 mm （channel 2）， and MXene film thickness， are determined via the commercial finite element method software COMSOL Multiphysics.

In the experiment， a gold film with a thickness of approximately 50 nm is deposited on channel 1 using an ionsputter coater （SBC-12）， with a sputtering time of 310 s and a current of 6 mA. MXene nanosheets are subsequently immobilized onto the gold surface of channel 2 via electrostatic self-assembly. Specifically， channel 2 is immersed in a 4 mg/mL cysteamine solution for 30 minutes， during which the thiol groups of cysteamine formed Au-S bonds with the gold film， while the ammonium ions render the surface positively charged. Thereafter， channel 2 is immersed in a 5 mg/mL monolayer-dispersed MXene solution for 2 hours. The negatively charged —F and —O terminal groups on the MXene surface interacts electrostatically with the positively charged ammonium groups， resulting in the uniform deposition of a monolayer MXene film on the fiber-optic surface. Surface characterization using Scanning Electron Microscope （SEM） and Energy Dispersive Spectroscopy （EDS） confirms the successful immobilization of gold， cysteamine， and MXene. The sensor's RI sensitivity is evaluated by monitoring resonance shifts in NaCl solutions with varying RI. Simulation and experimental results consistently demonstrate that increasing MXene thickness improves RI sensitivity and expands the dual-peak separation. This improvement is attributed to the low carrier concentration and high mobility of MXene， which modulate the SPR resonance from the visible to the near-infrared region and enhance the interfacial electric field.

Within the RI range of 1.333 to 1.366， as the number of MXene layers deposited on the surface of channel 2 increases from 0 to 5， the resonance wavelength of channel 2 exhibits a red shift， which is consistent with the simulation results. When the MXene reaches three layers， the resonance peaks of the two channels become completely separated. The RI sensitivity of channel 1 remains stable between 1 850 and 1 900 nm/RIU， with its Full Width At Half Maximum （FWHM） and Figure Of Merit （FOM） showing minimal variation. In contrast， the resonance wavelength of channel 2 （SPR-2） red-shifts from 604.15 nm to 781.28 nm， and its RI sensitivity increases from 2 016.31 nm/RIU to 4 209.16 nm/RIU， representing an improvement of 108.75%. Meanwhile， the resonance wavelength undergoes a red-shift from 604.15 nm to 781.28 nm. However， excessive MXene thickness results in a deterioration in the FOM. To achieve a balance between sensitivity and FOM， a four-layer MXene structure is selected for channel 2. After cascading with channel 1， the dual-channel SPR sensor exhibits optimal performance， achieving a dual-peak separation of 125.35 nm. The RI sensitivities of channel 1 and channel 2 are 1 894.27 nm/RIU and 4 001.48 nm/RIU， respectively， with corresponding FOM values of 16.86 RIU^-1 and 32.99 RIU^-1. Compared with the pure gold film structure， the RI sensitivity of channel 2 is increased by 98.45%.

The dual-parameter detection capability is validated by immersing the two sensing regions in solutions with different RIs. The results demonstrates that both channels responded independently to external RI changes. In the independent response tests， channel 1 and channel 2 achieve RI sensitivities of 1 826.25 nm/RIU and 4 317.96 nm/RIU， respectively， confirming the sensor's potential for simultaneous detection of different analytes. To evaluate the stability of the sensor， both sensing regions are simultaneously immersed in pure water and monitored continuously for 60 minutes. The maximum resonance wavelength fluctuations are only ±0.17 nm for channel 1 and ±0.21 nm for channel 2， indicating the excellent stability of the sensor. Compared with existing dual-channel SPR sensors， the proposed sensor features a simplified structure， ease of fabrication， and low cost， while delivering significantly enhanced RI sensitivity. These advantages make it a promising candidate for multi-parameter biosensing and environmental monitoring applications.

This study aims to enhance the accuracy， robustness， and training stability of deep-learning-based single-frame fringe projection profilometry. It focuses on two practical limitations commonly encountered in existing methods： the incompatibility between conventional intensity normalization schemes and the highly skewed brightness distribution of fringe images， and the lack of physically consistent data augmentation strategies under limited labeled data conditions. The objective is to develop a preprocessing framework that preserves effective fringe information， improves model adaptability across different neural network architectures， and provides a model-aware optimization strategy without altering network structures or learning objectives.

To achieve this goal， a unified preprocessing framework integrating Range-clipped Piecewise Contrast Normalization （RPCN） and Structured Stripe Occlusion Augmentation （SSOA） is proposed. RPCN is specifically developed to address the intrinsic characteristics of fringe images， which typically exhibit a large proportion of low-intensity pixels and a small number of extreme outliers caused by saturation， specular reflection， or sensor noise. The method combines interval clipping to suppress extreme pixel values， power-law mapping to enhance contrast in dark fringe regions， and piecewise remapping to separate shadow-dominated regions from effective fringe regions. This method redistributes pixel intensities into a range more suitable for neural network learning while maintaining stripe continuity and contrast stability. In parallel， SSOA is introduced to improve data diversity in a physically meaningful manner. Instead of directly masking or zeroing pixels， SSOA generates structured occlusions whose missing regions are reconstructed using neighboring stripe patterns， thereby preserving spatial continuity and phase-related information. This augmentation strategy simulates realistic measurement disturbances such as partial occlusion， surface defects， and local signal loss while maintaining physical consistency. The proposed framework is evaluated on multiple network architectures with varying capacities， including UNet， UNet++， and DeepLabv3+ with a MobileNetV2 backbone， to assess its generality， adaptability， and sensitivity to network complexity.

Extensive experiments demonstrate that single-frame depth estimation performance is significantly improved across different network architectures by the proposed preprocessing strategies， while distinct effects are observed depending on model capacity. On the UNet architecture， the mean absolute error was reduced by 17.9% through the application of RPCN， indicating enhanced feature representation under limited normalization adaptability. When SSOA was applied independently， the root mean square error was reduced by 9.8%， and the peak signal-to-noise ratio was increased from 28.48 dB to 29.38 dB， reflecting improved robustness to structured disturbances. The joint application of RPCN and SSOA further increased the structural similarity index from 0.967 8 to 0.971 8. For the more powerful UNet++ architecture， the combined RPCN-SSOA framework yielded the best overall performance， with the structural similarity index increased from 0.977 2 to 0.979 1 and the peak signal-to-noise ratio improved from 29.73 dB to 30.34 dB. In contrast， for lightweight models such as DeepLabv3+ with a MobileNetV2 backbone， performance gains were constrained when SSOA was introduced， as the increased data complexity hindered effective learning， whereas a more favorable balance between accuracy improvement and training stability was achieved using RPCN alone. Additional evaluations across multiple test scenes further confirm that prediction stability is consistently improved under varying surface reflectance and illumination conditions. These results indicate that preprocessing strategies interact strongly with network capacity and architectural design， and that inappropriate augmentation may limit performance gains in compact models.

The proposed RPCN-SSOA preprocessing framework provides a practical and adaptable solution for improving deep-learning-based single-frame fringe projection profilometry. By aligning intensity normalization with fringe-specific brightness distributions and enforcing physical consistency in data augmentation， the framework enhances depth estimation accuracy and robustness without increasing model complexity. The results highlight the importance of model-aware preprocessing demonstrate that normalization and augmentation strategies should be tailored to network capacity rather than uniformly applied. This work offers a feasible preprocessing paradigm and practical guidance for fringe-based three-dimensional measurement systems in both research and engineering applications.

Infrared small target detection， as the core technology of infrared search and tracking systems， plays a crucial role in military precision guidance， low-altitude economic monitoring， border surveillance， and space situational awareness. Its primary objective is to achieve rapid and accurate localization of small targets with low signal-to-noise ratios and weak contrast under long-distance conditions. In recent years， detection methods based on deep learning have gradually become a research hotspot and have achieved remarkable progress in detection accuracy. Most mainstream approaches adopt mask-to-mask or box-to-box regression training strategies， which can effectively enhance model robustness with the support of large-scale annotated datasets. However， these methods suffer from two major drawbacks. On the one hand， they heavily rely on high-quality， large-scale annotations， which are costly to obtain. On the other hand， in order to further improve accuracy， these methods often increase the depth and width of the network to expand model capacity， leading to massive parameters and complex architectures. To mitigate these issues， some studies have attempted to use weak supervision deep learning as a replacement for traditional mask or bounding-box annotations to reduce annotation costs. However， due to the limited granularity of supervision， the detection accuracy is often significantly constrained， making it difficult to balance practicality and precision.

To address the above challenges， this paper proposes a lightweight infrared small target detection algorithm based on point-to-point regression. The goal is to achieve high-accuracy detection while significantly reducing annotation and computational costs. The main contributions of this work are as follows. First， an asymmetric full-dimensional dynamic feature extraction module is designed. This module integrates channel shuffle with omni-dimensional dynamic convolution， which effectively models fine-grained dependencies across channel and spatial dimensions while maintaining network lightweightness. As a result， it enhances the discriminative features of the target and suppresses background interference. Second， a high-resolution cross-feature enhancement module is proposed. This module aligns multi-level features via upsampling， downsampling， and convolution mapping， and builds cross-layer interaction branches to dynamically fuse low-level detail features with high-level semantic features. In this way， it strengthens semantic representation while preserving spatial detail information， making it particularly suitable for weak and small target detection scenarios. Finally， an adaptive point regression detection head is introduced. This head models the distribution of target centers using Gaussian heatmaps， and combines local maxima filtering with a scale-adaptive mechanism to achieve precise prediction and sub-pixel localization. In this process， only single-point annotations are required for training， which substantially reduces the cost of data labeling.

Extensive experiments conducted on two public datasets， IRSTD-1K and NUDT-SIRST， demonstrate the effectiveness of the proposed method. With an extremely small number of parameters （only 0.24 M） and low computational complexity （1.53 GFLOPs）， the proposed algorithm achieves outstanding performance： F1 score of 91.79% on IRSTD-1K and 97.44% on NUDT-SIRST， which are significantly superior to existing state-of-the-art methods. In addition， the model reaches an inference speed of 36 FPS on a single GPU， fully demonstrating its potential for real-time infrared small target detection in practical applications.

The broad field of view inherent to remote sensing images offers significant advantages for large-scale monitoring tasks， enabling comprehensive surveillance over vast geographical areas with a single acquisition. However， this advantage is often offset by the presence of complex backgrounds， such as cloud cover， shadows， and varying terrain textures， which frequently introduce channel-level noise that interferes with target features. This interference significantly compromises the robustness and reliability of detection models， particularly in adverse weather conditions or densely cluttered environments. Additionally， the substantial variation in target sizes， ranging from small objects like vehicles and helicopters to large structures such as ports and bridges， poses a significant challenge. Detection models struggle to simultaneously capture high-resolution details necessary for identifying small targets and low-resolution semantic information essential for recognizing larger objects， leading to unbalanced detection performance across different scales.

To address the aforementioned challenges， this paper proposes a remote sensing image object detection algorithm based on adaptive feature weighting， built upon the Oriented R-CNN framework. The method introduces two core modules： the Adaptive Channel Weighting Module （ACWM） and the Adaptive Multi-Scale Fusion Pyramid （AMFP）， aimed at tackling background interference and multi-scale target detection issues. The ACWM module is applied after the STAGE 2 and STAGE 3 phases of the feature extraction network to adaptively adjust channel weights， a critical step for enhancing feature representation. By analyzing statistical correlations between channels using the Correlation-Aware Covariance Weighting （CACW） method， it generates dynamic channel weights to enhance target-related features while suppressing background noise and redundant information， balancing the expression of different channels and thereby improving the feature extraction network's ability to extract features from rotated targets. Subsequently， the AMFP module processes the multi-scale features （C2， C3， C4， and C5） extracted by the feature extraction network， employing a hierarchical approach for feature integration. The Multi-scale Perception and Context Integration Module （MPCIM） fuses global semantic features from deep layers with edge and positional details from shallow layers， effectively addressing the issue of small targets being obscured. The Adaptive Layer Weighting Module （ALWM） optimizes the fusion quality of multi-scale features through adaptive layer-wise weighting， enhancing the model's capability to handle diverse target scales ranging from small vehicles to large ports by dynamically adjusting the contribution of each feature level， thus improving adaptability to various target scales. The optimized features （P2， P3， P4， and P5） are then fed into the Oriented RPN and Oriented RCNN head， which， based on the Oriented R-CNN framework， generates high-precision rotated bounding boxes to complete classification and localization tasks.

To validate the effectiveness of the proposed algorithm， extensive experiments were conducted on the DOTA1.0 and DIOR-R datasets. Ablation studies were performed to assess the contributions of the ACWM and AMFP modules. Results indicate that the ACWM module alone improves the mean Average Precision （mAP） by 0.97% on DOTA1.0 and 1.73% on DIOR-R， while the AMFP module contributes an additional 1.30% and 2.01% improvement， respectively. When both modules are combined， the mAP reaches 78.23% on DOTA1.0 and 68.17% on DIOR-R， representing overall enhancements of 2.36% and 3.87% compared to the baseline Oriented R-CNN model. Visualization comparisons further demonstrate the superiority of the proposed method， showing a significant reduction in missed detections and false positives for small targets， particularly in categories such as Small Vehicle， Swimming Pool， Helicopter， and Storage Tank. The method also exhibits improved localization accuracy and better contour fitting for rotated objects， especially in complex backgrounds with varying angles and dense distributions. The experimental results highlight the robustness and adaptability of the proposed algorithm across diverse remote sensing scenarios.

Compared to state-of-the-art methods such as Faster R-CNN， RoI Transformer， and ARC， the proposed approach achieves superior performance in handling small targets and complex orientations， as evidenced by its higher mAP and enhanced visual detection outcomes. However， limitations remain， including potential performance degradation in extremely cluttered scenes or with ultra-small targets， suggesting avenues for future optimization.

Electrowetting Display （EWD）， as a novel reflective display technology， offers high reflectivity， wide viewing angles， fast response， and full-color display， making them highly promising for applications in portable terminals， wearable devices， and outdoor information displays. However， as display panels evolve toward larger sizes and higher resolutions， the scale of driving circuits and refresh rates increases significantly， leading to growing power consumption issues that directly affect device battery life and application scope. Existing low-power studies rarely fully integrate the reflective imaging characteristics and optoelectronic properties of EWD， and power optimization under complex ambient lighting conditions still has considerable room for improvement.

To address these challenges， this paper proposes a low-power algorithm based on ambient light compensation， designed to reduce power consumption and improve display quality by leveraging the reflective characteristics of EWD. In addition， an adaptive voltage adjustment technique is introduced， which dynamically adjusts the driving voltage according to ambient light intensity， further reducing driving power consumption. The proposed low-power algorithm consists of four main components： 1） voltage–reflective luminance curve calibration， ensuring that the luminance differences between adjacent gray levels under various ambient light conditions are closer to those under standard ambient light； 2） extraction of the original grayscale information of the image， followed by grayscale correction using a mapping table generated during the calibration process； 3） detail enhancement to improve edge and texture representation in the image after calibration， compensating for potential detail loss； and 4） calculation of the driving voltage corresponding to the highest pixel value after processing， based on the Pulse Amplitude Modulation （PAM） driving method， and setting it as the maximum driving voltage for the given ambient light condition to achieve adaptive voltage adjustment.

The proposed ambient light dynamic compensation calibration method was applied to an EWD system. Test points were set at the positive and negative power supply terminals of the source driver， and an EKA1080M power consumption tester was used to measure the power consumption before and after algorithm processing. The test video sequence consisted of black screen-original image-black screen-processed image-black screen， enabling comparison of power consumption differences before and after processing. Results show that， in the source driver voltage section， the processed images consumed significantly less power than the original images. Further measurements of current and voltage at the test points under different ambient light conditions， combined with the power calculation formula， revealed that the overall power consumption under standard ambient light was 18.93 mW/in²， and decreased to 14.21 mW/in²， 12.06 mW/in²， and 11.72 mW/in² under ambient light intensities of 483 cd/m²， 970 cd/m²， and 1 730 cd/m²， respectively—representing reductions of 24.9%， 36.3%， and 38.1% compared to the unoptimized case.

In terms of image quality， the objective evaluation metric Average Gradient （AG） increased by 4.9%， 3.9%， and 2.3% under the three ambient light conditions， respectively， indicating that the processed images exhibited higher clarity， more prominent edges， and richer details. Moreover， the brightness of the processed images was closer to that of the standard ambient light display， providing a more comfortable and stable viewing experience under varying lighting conditions. In summary， the proposed method significantly reduces the power consumption of EWD while effectively maintaining and even enhancing display quality.

The primary objective of this study is to design and experimentally validate a low-loss edge coupler based on a bi-level taper structure on the thin-film lithium niobate platform， specifically targeting efficient coupling with standard single-mode fibers having a mode field diameter of 6 μm at the 1 310 nm wavelength， to address the critical challenge of high coupling losses between Thin-film lithium niobate waveguides and optical fibers that hinder practical applications in integrated photonic systems such as fiber optic gyroscopes， aiming for a solution compatible with mass production via deep ultraviolet lithography while maintaining low optical loss and high alignment tolerance.

To achieve this， the coupler design incorporates two sequentially fabricated inverted tapers—a fully etched lower taper and a partially etched upper taper—within a silicon dioxide cladding， and the structural parameters were rigorously optimized using three-dimensional finite-difference time-domain simulations to maximize the mode field overlap integral between the waveguide and fiber， where key geometric variables including the taper lengths L₁ and L₂ （both set equal to L for simplicity）， the lower taper tip width W₁， the upper taper tip width W₂， the lower taper output width W₃， and the straight waveguide width W₄ were systematically varied to assess their impact on coupling efficiency， with initial simulations determining an optimal taper length L of 300 μm to satisfy adiabatic conditions and avoid excessive transmission loss or fabrication-induced defects， followed by width optimizations revealing optimal values of W₁ at 0.2 μm， W₂ at 0.2 μm， W₃ at 3.5 μm， and W₄ at 1.2 μm for single-mode operation， while additional analysis of the silicon dioxide upper cladding thickness indicated that a minimum thickness of 3 μm is essential to confine the mode effectively and prevent vertical leakage， with negligible efficiency variation between 3 and 6 μm.

Fabrication involved electron beam lithography for rapid prototyping and validation， where X-cut Thin-film lithium niobate with a 300 nm thick lithium niobate layer on a 4.7 μm silicon dioxide substrate was patterned using two Electron beam lithography steps followed by inductively coupled plasma-reactive ion etching to define the upper and lower tapers sequentially， and a 6-micrometer silicon dioxide cladding was deposited via a combination of low-pressure chemical vapor deposition and plasma-enhanced chemical vapor deposition to ensure density and minimize hydrogen-related losses. Experimental characterization employed a 1 310 nm superluminescent diode source and single-mode fibers in a cut-back method setup to measure total insertion loss， yielding a coupling loss of approximately 2.3 dB per facet for the optimized design， with the straight waveguide propagation loss separately quantified as 0.47 dB per centimeter， and scanning electron microscopy imaging confirmed the fabricated structure but indicated an approximate 110-nanometer lateral misalignment between the upper and lower tapers， though subsequent Finite-difference time-domain simulations demonstrated that such misalignments up to ±300 nm introduce negligible additional loss below 0.03 dB， affirming high fabrication tolerance. Further experimental sweeps of parameters W₁ and L showed consistent trends with simulations， where coupling loss decreased with increasing L up to 300 μm and increased with W₁ above 0.2 μm， and comparative analysis with existing coupler designs highlighted that this bi-level taper structure achieves a favorable balance between performance and process complexity， requiring only two etching steps and avoiding reliance on costly ultra-high numerical aperture or lensed fibers， thus enabling practical， low-cost deployment in fiber gyroscope applications.

In conclusion， the proposed bi-level taper edge coupler successfully demonstrates low coupling loss， robust manufacturability with substantial alignment tolerances， and full compatibility with standard single-mode fibers and deep ultraviolet lithography mass production， thereby providing a viable pathway for scalable integration of Thin-film lithium niobate based photonic devices.

In recent years， visible-light integrated photonics has undergone rapid development， demonstrating significant applications across various fields. In biosensing， integrated optical waveguide sensors based on the evanescent wave effect enable high-sensitivity real-time monitoring of analytes by detecting optical parameter variations induced by changes in analyte properties. For consumer electronics， miniaturized on-chip spectrometers provide real-time spectral analysis capabilities for smart devices， supporting applications such as food safety testing and health monitoring. In optical computing， metasurfaces and diffractive neural network technologies have facilitated the development of low-power intelligent sensing chips with ultra-fast parallel processing capabilities， capable of performing tasks such as image recognition and object tracking.

Extending the operational wavelength range of these devices to cover the entire visible spectrum holds considerable importance， as it can enhance the detection accuracy and applicability of miniaturized spectrometers， broaden the bandwidth of visible-light communication systems， and enable biosensors to simultaneously detect multiple biomolecules in complex samples. As fundamental components of integrated photonic devices， waveguides serve as the core elements for directional photon transport and device interconnections in most on-chip systems. However， current research on full-visible-spectrum waveguides remains incomplete， with most studies focusing on waveguide dimension design for specific narrow bands. The investigation of low-loss transmission across the entire visible spectrum in single waveguide structures remains insufficient， which may limit their applications in on-chip full-visible-spectrum devices. Therefore， this study proposes a waveguide design that supports the entire visible spectrum （435~760 nm）.

Generally， single-mode waveguides exhibit superior performance compared to multimode waveguides. However， research has revealed conflicting requirements for single-mode operation across different wavelengths： shorter wavelengths require smaller dimensions to suppress higher-order modes， while longer wavelengths necessitate larger dimensions to prevent energy leakage. Consequently， none of the three commonly used waveguide structures （rectangular waveguides， rib waveguides， and slab waveguides） can achieve single-mode operation across the entire visible spectrum. As an alternative approach， this study systematically investigates key parameters for minimizing propagation loss under multimode transmission conditions.

Considering the respective advantages and research value of polarization-sensitive and polarization-insensitive waveguide platforms， dimension designs were completed for both TE-polarized thin waveguides and polarization-insensitive thick waveguides based on silicon nitride platforms with thicknesses of 500 nm and 1 μm， respectively： For thin waveguides， the thickness was optimized at 0.5 μm after comprehensive consideration of loss and fabrication challenges， while thick waveguides were designed with 1 μm thickness to achieve polarization-insensitive low-loss transmission. The width of thin strip waveguides was selected as 1.5 μm for lower loss， whereas thick waveguides were designed with 2 μm width to mitigate scattering loss induced by sidewall roughness. Regarding spatial configuration， thin waveguides require minimum waveguide spacing of 0.6 μm and minimum bending radius of 15 μm， while thick waveguides permit tighter configurations with 0.4 μm spacing and 10 μm bending radius. For both waveguide types， fabrication dimensional tolerances exhibit negligible impact on optical transmission. Additionally， thin waveguides demonstrate high sensitivity to sidewall roughness， whereas thick waveguides maintain relatively low transmission loss when the root-mean-square roughness is ≤10 nm.

Comparative analysis of the two waveguide configurations reveals distinct advantages　thin waveguides offer easier fabrication and higher integration density， though they are limited to TE-mode operation； thick waveguides feature comparable effective refractive indices for TE and TM modes， making them suitable for polarization-insensitive applications in systems employing broadband LED sources （e.g.， biosensors and color imaging）， despite requiring specialized stress-control processes during fabrication. Both configurations present unique advantages that can be selected based on specific device requirements.

In summary， this study analyzes propagation loss and mode distribution in waveguides to determine optimal parameters for low-loss transmission， including waveguide dimensions， bending radii， and spacing. Furthermore， it evaluates the impact of fabrication tolerances on waveguide performance， providing valuable references for the design and manufacturing of on-chip photonic devices operating across the full visible spectrum.

Quasi-phase matching technology overcomes the limitations of photonic crystal materials and their transmission bands by artificially designing nonlinear photonic structures. Through periodic ferroelectric domain inversion achieved by electric field poling， this approach compensates for phase mismatch while enhancing the efficiency and flexibility of nonlinear frequency conversion. Quasi-periodic structures， with their aperiodic yet ordered arrangements， provide abundant reciprocal lattice vectors. An increasing variety of superlattice structures have been implemented in optical materials to explore their advantages for generating harmonics across diverse wavelengths.

In this study， we incorporated the aperiodic hat-tiling monotile structure into nonlinear optical superlattices， successfully designing and fabricating a two-dimensional planar hat-tiling superlattice. The attainable harmonic wavelengths and their characteristics were systematically investigated. Experimental results demonstrate that the reciprocal space of the aperiodic hat-tiling superlattice exhibits pronounced sixfold rotational symmetry while maintaining periodic intense diffraction points. The diffraction pattern of the hat-tiling superlattice structure exhibits not only hexagonal symmetry but also additional bisecting points between every two vertices of the hexagon. Comparative analysis with conventional periodic structures reveals that the hat-tiling pattern demonstrates more pronounced intensity variations among diffraction spots. Furthermore， the reciprocal vectors in this configuration include additional orientations deviated by 30° from the horizontal axis as well as vertical directions. Under collinear conditions， the structure successfully achieved second-harmonic generation at fundamental wavelengths of 858 nm， 895 nm， 932 nm， 1 055 nm， and 1 100 nm， while under non-collinear conditions， the hat-tiling structure enabled efficient SHG at multiple fundamental wavelengths including 892 nm， 971 nm， 1 089 nm， 1 103 nm， and 1 389 nm. Comparative analysis with periodic hexagonal structures confirmed that the aperiodic hat-tiling offers richer reciprocal vectors. Moreover， the periodicity in reciprocal space significantly enhances the conversion efficiency of nonlinear photonic crystals.

In conclusion， the hat-tiling structure offers reciprocal vectors of varying magnitudes along multiple directions. While preserving the high conversion efficiency characteristic of regular hexagonal structures， it overcomes the limitation of single reciprocal vectors in hexagonal lattices. The hat-tiling structure can provide reciprocal vectors of varying magnitudes along multiple directions to compensate for phase mismatch， thereby extending the achievable frequency bandwidth of fundamental waves. Based on the experimental results， we propose an optimized design by scaling the hexagonal edge length of the hat-tiling superlattice structure to 32.2 μm. This modification enables the strongest diffraction point to correspond to a reciprocal lattice vector that achieves second-harmonic generation for a fundamental wavelength of 1 550 nm， which is particularly significant for optical communication applications. The experimental data provide crucial references for further optimization to fully exploit the high-efficiency diffraction points. These findings advance the development of multi-channel nonlinear optical devices with enhanced spectral flexibility.

Surface waves propagating along the interface of two distinct media have garnered significant attention in recent years， owing to their promising applications in fields such as optical sensing， optical switching， and surface characterization. Extensive research has been conducted on surface solitons based on the standard Schrödinger equation， and many novel characteristics have been found.

Recently， an extended version of the standard Schrödinger equation has been introduced into the system， where the diffraction term is described by a fractional-order derivative rather than the second-order derivative in the conventional form. This extended equation is referred to as the fractional Schrödinger equation. Promptly thereafter， an optical realization of the equation was proposed based on the transverse light dynamics in an aspherical optical resonator， alongside a scheme for generating dual-Airy states. Notably， this marked the first introduction of the fractional Schrödinger equation into the field of optics， which opened up a novel research direction in optics. Since then， numerous studies on solitons with fractional diffraction have been reported. However， relevant research on surface solitons supported by the fractional nonlinear Schrödinger equation remains relatively scarce. Focusing on the propagation of two-dimensional surface gap solitons under fractional diffraction， this study investigates the existence， stability， and propagation properties of fundamental and in-phase dipole solitons at the interface between bulk media and an optical lattice in self-defocusing Kerr nonlinear media via numerical methods.

The band structure of the optical lattice was calculated via the plane-wave expansion method. Subsequently， the existence region of surface gap solitons was obtained by modified squared operator iteration method， while their stability was analyzed through the Fourier collocation method and further verified via the split-step Fourier method.

The results show that　the first bandgap and the semi-infinite bandgap gradually broaden with an increase in the Lévy index， while the other bandgaps progressively narrow as the Lévy index increases. Both fundamental and in-phase dipole solitons can exist within the first gap of the Bloch bands， and both fundamental and in-phase dipole solitons at the interface exhibit an asymmetric morphology. As the Lévy index increases， the peak intensity of the soliton decreases. Similarly， the peak intensity also declines with an increase in the propagation constant. Combined verification through propagation simulations and linear stability analysis reveals that： except for the in-phase dipole solitons away from the interface， which exhibit instability in a small region near the lower edge of the first Bloch band， all other surface gap solitons maintain stability throughout their entire existence range. Additionally， it also indicates that as the Lévy index increases， both the existence interval and stability interval of surface gap solitons gradually broaden. When a phase tilt factor is introduced to the surface gap solitons， applying the phase tilt in a single direction yields distinct behaviors： a small phase tilt causes the soliton center to oscillate periodically only along the direction of the applied phase tilt. A large phase tilt in the y-direction not only induces irregular oscillations of the soliton center along the y-direction but also elicits such oscillations along the x-direction. However， a large phase tilt in the x-direction solely results in irregular oscillations of the soliton center along the x-direction. If the same phase tilt is introduced in both directions， a small phase tilt causes the soliton center to undergo periodic oscillations in both directions， while a large phase tilt leads the soliton center to exhibit irregular oscillations in both directions. The results indicate that a small phase tilt induces slight periodic oscillations in the soliton's mass center， while the soliton shape remains nearly unchanged. In contrast， a large phase tilt leads to irregular oscillations in the mass center， accompanied by gradual distortion of the soliton.

Optical lattice solitons， as key entities at the intersection of nonlinear optics and photonics， not only provide an ideal model for uncovering fundamental physical principles of nonlinearity and periodicity coupling， but also break through technical bottlenecks in conventional optical transmission， manipulation， and storage. A considerable number of studies have been reported on the existence and stability of defect solitons under both normal and fractional diffraction mechanisms. However， the properties of defect solitons in optical lattices under the combined influence of fractional effects and PT-symmetric potentials have not been thoroughly investigated. This paper focuses on studying the existence， stability， and propagation dynamics of defect solitons in PT-symmetric optical lattices with fractional diffraction， examining the impact of fractional effects and defect types on the bandgap structure， analyzing the existence domain of defect solitons， elucidating their linear stability， and discussing the influence of system parameters on the characteristics of defect solitons.

A theoretical model for optical wave propagation in Kerr nonlinear optical lattices with PT-symmetric potentials under fractional-order effects is established based on the fractional nonlinear Schrödinger equation. The plane wave expansion method is used to analyze the effects of defect strength and fractional-order characteristics on the bandgaps of defect solitons. The modified squared-operator iteration method is employed to numerically solve the model and obtain steady-state solutions of the defect solitons. Additionally， perturbations are introduced to the steady-state solutions， and linear stability analysis is performed using the Fourier collocation method. Finally， the propagation properties of the solitons are verified by means of the symmetrized split-step Fourier method.

The bandgap range of the optical lattice narrows as the Lévy index decreases. As the defect strength increases from negative to positive， the semi-infinite gap gradually narrows， while the other bandgaps progressively widen. Under negative defect conditions， the power of solitons in both the semi-infinite gap and the first gap generally decreases with increasing propagation constant， except for an initial increase followed by a decrease in the low-power region of the semi-infinite gap and the medium-power region of the first gap. As the Lévy index varies， the stable region of solitons in the semi-infinite gap gradually shrinks， while solitons in the first gap remain unstable. Under zero defect conditions， the soliton power in both the semi-infinite and first gaps decreases monotonically with the propagation constant. The influence of the Lévy index on soliton stability is similar to that under negative defects. Under positive defect conditions， the soliton power in both gaps decreases with increasing propagation constant， but a cutoff point exists where the power reaches zero. Moreover， a smaller α results in a smaller propagation constant at the cutoff point. Similarly， a decrease in α reduces the stable region of solitons in the semi-infinite gap， although stable solitons exist in the first gap. Additionally， for all three defect types， the width of the defect soliton envelope narrows slowly as α decreases， while the peak intensity increases gradually. The power decreases with reduced α， and the peak intensity of the soliton envelope decreases as the defect strength changes from negative to positive.

This study investigates the existence， stability， and propagation dynamics of defect solitons in PT-symmetric optical lattices with fractional diffraction. Under fractional diffraction， as the Lévy index increases， the power of defect solitons in the semi-infinite bandgap and the first bandgap gradually increases for all defect lattices， while the stability of solitons improves. In cases of negative defects and zero defects， defect solitons only have stable intervals in the semi-infinite bandgap and are completely unstable in the first bandgap. In cases of positive defects， defect solitons have stable intervals in both the semi-infinite bandgap and the first bandgap. Additionally， within the semi-infinite bandgap， as the Lévy index decreases， the peak of the defect soliton envelope increases while its width narrows； as the propagation constant increases， the peak of the defect soliton envelope decreases rapidly. Within the first bandgap， the peak of the soliton envelope decreases as the Lévy index decreases and decreases as the propagation constant increases under positive defect conditions. In summary， the envelope， power， and stability of defect solitons are significantly influenced by the defect strength， Lévy index， and propagation constant.