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The degradation of orbital angular momentum (OAM) modes propagating through atmospheric turbulence fundamentally limits free-space optical communication capacity. We report a universal analytical formula governing this degradation: η_l = [1|(D/r₀/a_l)^5/3]^-1, where a_l=1.64/(∣l∣+1)/2. Based on Kolmogorov turbulence statistics, we derive an analytical integral expression for purity and obtain a closed-form scaling law through Kolmogorov-constrained closure. The exponent 5/3 emerges directly from the Kolmogorov phase structure function, while the mode-dependent coefficient a_l is determined by numerical calibration. We validate our theory against 560,000 independent Monte Carlo simulations spanning topological charges l = 1-10 and turbulence strengths D/r₀ = 0.01-20. The measured exponent b = 1.665 ± 0.042 agrees with the theoretical prediction to within 0.13%, and all fits achieve R² > 0.99. We further extend this framework to non-Kolmogorov turbulence, showing that the exponent generalizes to b(α) = α - 2 for arbitrary spectral index α. A practical lookup table enables rapid link budget estimation without Monte Carlo simulation. These results provide both physical insight into OAM degradation mechanisms and a theoretical foundation for designing high-capacity OAM communication systems.

Ultraviolet C (UVC) light sources are central to micro/nano fabrication. Current research focuses on optimizing exposure by increasing irradiance, improving precision, and implementing real-time dose control, creating an urgent demand for high-performance UVC photodetectors. To enhance responsivity, many UV detectors leverage metal nanoparticles (NPs) formed by solid-state dewetting on photosensitive substrates. However, limited characterization and simulation capabilities hinder detailed analysis of how these metasurfaces enhance responsivity, constraining device design and optimization. Therefore, developing methods to simulate and analyze the optical properties of metal NPs is essential. In this study, we propose an approach: reconstructing models of metasurfaces formed by solid-state dewetting and simulating them to support UVC photodetector design. Specifically, we combine U-Net deep learning with molecular dynamics (MD) simulations to construct accurate metasurface models, and employ the finite-difference time-domain (FDTD) method to evaluate the optical properties within each functional layer. Guided by these simulations, we fabricated a p-i-n UVC photodetector based on an ITO/Si structure, achieving an ultrashort response time (7.04 µs) and high sensitivity (0.84 A/W). Crucially, this detector also serves as an experimental platform to elucidate the mechanism by which localized surface plasmon resonance (LSPR) enhances responsivity. We achieved this by correlating the measured UVC electrical response with simulated metasurface absorption and LSPR effects. Our "algorithm-guided simulation" strategy provides a framework for designing high-performance UVC photodetectors. Furthermore, it strengthens the theoretical basis for LSPR effects through closed-loop "simulation-device-mechanism" verification.

Chalcogenide phase-change material (Ge₂Sb₂Te₅) has attracted considerable research interest in recent years for tunable structural color applications, owing to its pronounced contrast in optical properties between amorphous and crystalline states. However, the strong absorption of conventional Ge₂Sb₂Te₅ has significantly limited its applicability in the near-infrared (NIR) wavelength range. Herein, we utilize the near-zero absorption characteristic of the emerging phase-change material Sb₂Se₃ in the NIR band to construct a Fabry-Pérot cavity structure. This structure not only enables resonance peaks with high transmittance and large modulation range, but also allows a multi-channel filter to be achieved by increasing the thickness of Sb₂Se₃. This tunable multi-channel spectrally selective filter presents significant application potential in advanced fields such as multi-gas sensing, laser beam combining, and adaptive multi-spectral imaging.

Reconfigurable integrated photonics leveraging phase-change materials (PCMs) is a rapidly evolving field with diverse applications, including optical computing and programmable optics. Its development is driven by the high optical contrast and nonvolatility enabled by reversible phase switching in PCMs. Consequently, the design and modeling of such devices are critical for enabling their practical applications. In this work, we compare multiple inverse design algorithms for integrated optical structures, using a mode converter based on the low-loss PCM Sb₂Se₃ as a representative example. To bridge the gap between idealized designs and the realistic performance of integrated devices, the resulting mode converters are numerically assessed for key metrics such as mode contrast, insertion loss, and robustness against fabrication variations. To further investigate their applicability, we develop a photonic tensor core incorporating these mode converters as weight elements. We then analyze the operational range of output optical power and the image convolution quality achieved by the photonic core, highlighting the impact of the choice of weight elements on its overall performance.

We propose highly efficient, thermo-optic phase shifters based on aluminum oxide waveguides operating at UV wavelengths. The phase shifters are integrated into Mach-Zehnder interferometers for device characterization. The photonic waveguides are fabricated in a 200 mm CMOS pilot line, and heaters and silicon undercut are defined on the die-level with metal lift-off and dry etching processes. We experimentally demonstrate a phase shifter with an efficiency of 0.85 mW for a π phase shift at 360 nm wavelength. For the bandwidth characterization, we measure the 10 - 90% rise and fall times of a series of phase shifters with varying structural designs and systematically analyze the trade-off between efficiency and response time. The developed device provides a low insertion loss, high-efficiency active modulator that can be readily integrated onto the existing 200 mm aluminum oxide UV photonics platform.

The measurement of spectral smile and keystone is crucial for both the performance evaluation and spectral retrieval of hyperspectral imagers. However, traditional methods are often either complex in procedure or low in accuracy. To address this issue, this paper proposes the discrepancy function matching (DFM) method and the error-predicted squared weighted centroiding (EPSWC) method for fast and high-precision testing. Considering the influence of spectral non-uniformity, a spectral peak-based spectral response non-uniformity (SRNU) correction method is proposed for non-uniformity correction in the spectral dimension. Relevant experimental results show that the SRNU correction-based DFM and EPSWC methods maintain very simple measurement procedures while achieving significantly improved measurement accuracy compared with other approaches. On this basis, a positioning distance evaluation function (PDEF)-based method is proposed to solve the degeneracy problem caused by variations in the shape and width of the spectrometer response function. Relevant simulation results demonstrate that the proposed method can maintain high localization accuracy and width measurement accuracy under different response function shapes and widths. The method proposed in this paper is applicable to various types of response functions. The related research will help improve the accuracy of imaging spectrometer alignment and performance testing, and promote the development of hyperspectral observation technology.

The spatial distribution of phosphor particles within phosphor films is a key factor governing the optical performance of phosphor-converted white LEDs (pc-LEDs). However, real phosphor particles typically exhibit complex morphologies such as non-sphericity and agglomeration, making it difficult for Mie-theory-based models under ideal spherical assumptions to fully reproduce the coupled scattering-absorption-re-emission processes at an engineering scale. Meanwhile, the approach of relying on extensive experimental screening to obtain target spectra is disadvantageous in terms of both cost and time. Further investigation is needed to understand the relationship between LED spectra, considering multiple physical factors, and the distribution of phosphor particles within the phosphor film. To address this challenge, we constructed a spectral dataset based on realistic phosphor particles with varied spectral responses. A double-Sigmoid function and film-layered processing were employed to capture physically informative spectral descriptors and particle-distribution descriptors, respectively. Using the key parameters of the fitted function as inputs and the particle-distribution parameters as outputs, we trained an optimally structured neural network via data augmentation and neural architecture search (NAS). During training, physics-constrained loss terms derived from a one-dimensional transport approximation were further incorporated, yielding representative feasible solutions under the specified constrained assumptions (rather than physically unique solutions). The results show that the model achieves inverse-inference performance R² values of 0.913-0.994 for particle-number prediction and 0.831-0.960 for particle-size prediction. Finally, a Monte Carlo-based optical simulation model was adopted as a forward-validation benchmark: both the ground-truth and the machine-learning-predicted particle-distribution features were fed into the simulator to generate spectra, which were then compared with the measured spectra. The reconstructed spectra exhibit forward spectral consistency R² values of 0.974-0.983 relative to measurements. In terms of efficiency, the proposed approach requires approximately 1.71 s per target for a single inference, which is substantially lower than optical simulation and conventional experimental screening, demonstrating the potential for orders-of-magnitude acceleration.

This paper proposes an end-to-end, multi-domain joint compression method for 3D light field video based on a viewpoint-disparity representation. By compressing dense viewpoints into sparse viewpoints with associated disparity and establishing a closed-loop "motion vector → disparity → view synthesis" pathway, our method achieves an 81% BD-rate reduction and a 1.998 dB BD-PSNR improvement compared to the MV-HEVC standard. Furthermore, the approach successfully decouples decoding time from the number of viewpoints, maintaining a stable latency of 28 ms during 96-viewpoint rendering. This work provides an effective solution for efficient compression of dense 3D light field video while establishing a theoretical foundation for its real-time transmission.

BPSK modulation offers significant advantages for long-reach coherent optical communications, while conventional demodulation relies on complex coherent receivers, increasing system cost and footprint. In this work, we demonstrate a simplified silicon photonic transceiver chip with a TFLN electro-optic comb source, which enables simultaneous generation and demodulation of WDM signals. The on-chip MRR array facilitates direct BPSK demodulation in the optical domain, recovering data via waveform acquisition and thereby eliminating the need for coherent detection and improving energy efficiency. We experimentally demonstrate the simultaneous generation and demodulation of 3 × 20 Gbaud BPSK signals on a single chip, achieving a receiver sensitivity of -28 dBm at the 3.8×10^-3 HD-FEC threshold. Moreover, the results underscore the potential of silicon-TFLN heterogeneous integration for compact, single-chip coherent transceivers.

Ghost imaging (GI) reconstructs objects using single-pixel measurements and is widely explored for remote sensing, imaging through scattering media, and photon-limited environments. However, deep-learning-based computational ghost imaging (CGI) often relies on experimentally acquired low-resolution datasets, making data collection time-consuming and limiting denoising performance. This work reports on a simulation-based training strategy that generates high-resolution synthetic datasets replicating experimental conditions, enabling efficient network training without extensive data acquisition. Using this approach, the convolutional blind denoising network (CBDNet) achieved peak signal-to-noise ratio (PSNR) values up to 12.79 dB for complex experimental targets and approximately 10.5 dB for structured targets at 256 × 256 resolution, while preserving fine details in cross-sectional intensity profiles. These results demonstrate that simulation-driven training significantly enhances denoising performance and scalability, paving the way for high-resolution ghost imaging in complex and photon-starved scenarios.