Optics letters最新文献

Accurately modelling physical perturbations in optical systems is critical for photonic device design, yet existing characterization methods are often computationally prohibitive. We introduce a data-efficient machine learning framework that learns the perturbation-dependent transmission matrix of a multimode fiber. To circumvent the spectral bias that prevents standard neural networks from resolving high-frequency phase changes, we explicitly encode perturbations into a Fourier Feature basis. This approach enables a compact multi-layer perceptron to learn the mapping from sparse training data with high fidelity. Using experimental data from a mechanically deformed fiber, our model achieves a 0.996 complex correlation with the ground truth, improving phase accuracy by an order of magnitude over standard networks while using significantly fewer parameters. This framework transforms the transmission matrix into a continuous, differentiable "digital twin" of the system, providing a robust tool for characterizing complex media in rapidly evolving environments.

Fused deposition modeling (FDM) has become a widely adopted additive manufacturing technology for producing complex functional structures, owing to its high material utilization, low cost, and design flexibility. However, current quality inspections are largely restricted to post-fabrication evaluation, making it difficult to perform comprehensive quality control and detect defects as they occur. This limitation hinders both process optimization and improvement of print reliability. In this study, we present an integrated real-time in situ monitoring system that combines an optical coherence tomography (OCT) imaging probe with the print head of an FDM printer. The probe consists of a single-mode fiber coupled with a self-focusing lens to achieve efficient collimation and spot focusing. As the print head moves, the co-mounted probe performs volumetric scanning, from which a reconstruction matrix is formulated according to the scanning trajectory. Interpolation-based algorithms are then applied to generate three-dimensional images from the acquired OCT data. We validate the system by printing and scanning a polylactic acid structure and successfully detecting multiple types of defects during fabrication. The results demonstrate that the proposed approach enables real-time defect identification and structural reconstruction, providing a powerful pathway toward enhanced precision, process control, and reliability in additive manufacturing.

Optical imaging processing is an attractive technology for extracting essential information from target objects without any digital computations. Although various nanophotonic devices have been studied for versatile image processing, their transfer function typically relies on extensive optimizations, thus increasing computational costs when faced with arbitrary wavelength requirements. Here, a metal-silicon hybrid nanostack is demonstrated for wavelength-tailorable optical image processing in the near-infrared range. By employing the cavity-induced wavelength/angle-sensitive property, the nanostack with five-layered nanofilms directly manipulates the transmittance of light in the wavevector domain, and performs edge-enhanced imaging and bright-field imaging in different wavelength channels. Moreover, due to the dual Fabry-Perot cavity architecture, the operating wavelength of the nanostack can be effectively and continuously tuned from 850 nm to 1250 nm by merely scaling the silicon layer thickness, beyond re-optimization strategies. The proposed nanostack with a simple architecture is easy to fabricate and integrate into compact optical imaging systems and suggests practical applications in machine vision, optical computing, and intelligent image recognition.

The referenced article [Opt. Lett.49, 5667 (2024)10.1364/OL.538891] has been retracted by the authors.

A fully tunable optical filter on a thin-film lithium niobate platform with a footprint of 2.5 mm × 1.2 mm is demonstrated, in which the extinction ratio, resonant wavelength, and stopband width can be independently and simultaneously adjusted via three integrated electro-optic phase shifters. The device achieves an extinction-ratio tuning range from approximately 0 dB to 31 dB. At maximum extinction ratio, the stopband width can be varied between 8.8 GHz and 9.6 GHz. Moreover, the stopband center wavelength can be shifted linearly with a tuning efficiency of 1.95(8) pm/V. This work provides a compact, electro-optically reconfigurable filter architecture with independent multi-parameter control for on-chip photonic systems.

To address the challenges of imaging through scattering media, we propose GI_UNet, a differential ghost imaging (DGI) framework that integrates Monte Carlo photon-transport modeling with an enhanced UNet. By utilizing high-fidelity point spread functions (PSFs), the framework predicts the measurement matrix of trainable speckles after their propagation through scattering media, replacing the ideal illumination patterns used in traditional DGI. Subsequently, the enhanced UNet suppresses scattering noise and restores edge details through its optimized architectural components. Simulations and experiments demonstrate that GI_UNet outperforms existing mainstream methods. Given its imaging robustness under scattering conditions and at low sampling rates, GI_UNet provides an effective solution for various scattering-limited imaging scenarios.

Squeezed states tuned to alkali atomic transitions are essential for developing atom-light quantum interfaces. We experimentally demonstrate a frequency-tunable quadrature squeezed state across the hyperfine transition of F=4(6²S_1/2)→F^'=3(6²P_1/2) of a cesium (¹³³Cs) D₁ line at 894.6 nm. Using a compact semi-monolithic optical parametric oscillator (OPO) operating below threshold, we achieve up to -4.1 dB squeezing below the standard quantum limit. When reconfigured as an optical parametric amplifier (OPA), the system generates bright squeezed light that can be finely tuned over a 50 MHz range, which is verified using electromagnetically induced transparency (EIT) spectroscopy. This portable and tunable quantum light source provides a promising platform for cesium-based quantum interfaces in emerging quantum networks.

Reciprocal lenses (RLs) overcome key limitations of conventional imaging mechanisms, which require precise optical-axis alignment and produce only inverted real images. This advancement highlights the potential of nonlocal metasurfaces for imaging applications. However, existing RLs operate only under specific polarization conditions, hindering their practical utility. In this work, we propose an RL based on a Stampfli-triangle photonic crystal slab (S-T PCS). By achieving isotropy and degeneracy of the non-Hermitian complex bands (N-H CB) of guided resonances (GR) within the operational in-plane wavevector domain, we effectively eliminate polarization dependence in the transmitted beam. The proposed lens operates for arbitrary polarization states and directly produces upright real images of complex objects. This design provides a new platform for beam shifting effects and wavevector domain optical signal manipulation, paving the way toward further miniaturization and integration of optical imaging systems that incorporate nonlocal flat devices.

In conventional topological photonic lattices, the robustness of boundary states is guaranteed by quantized topological invariants. However, this protection mechanism fundamentally depends on the topological phase of the system, making dynamic reconfiguration difficult. In this work, we introduce three auxiliary lattice sites to the left lattice point of a Su-Schrieffer-Heeger (SSH) lattice, treating the resulting four-atom structure-composed of the left lattice point and the auxiliary sites-as a rotatable cluster. This forms a cluster-doped SSH lattice system. By continuously tuning the cluster rotation angle, we modulate the coupling strength between the clusters and the substrate lattice, thereby enabling on-demand control of boundary states. Our findings reveal that the robustness of the boundary modes is co-determined by the cluster rotation angle and the boundary geometry, with their degree of localization showing a positive correlation with robustness-a behavior distinct from conventional global topological protection. This work establishes a geometry-driven paradigm for boundary-state manipulation, opening new pathways toward programmable and dynamically reconfigurable photonic circuits.

In distributed microwave photonic (MWP) radar systems, the detection performance is highly sensitive to the quality of optical signals transmitted over the long fibers connecting the indoor and outdoor units. In this Letter, we propose an MWP radar system that, in addition to its standard range measurement capability, integrates optical frequency domain reflectometry (OFDR) for distributed vibration sensing along the optical fibers. A carrier-suppressed double-sideband (CS-DSB) optical signal is generated in the indoor unit, which serves as a light source and the local reference for the OFDR, as well as a transmitted signal for the radar transmitter. For radar detection, an echo received from an antenna is sent back to the indoor unit, where it is mixed with the local reference to perform de-chirping. For OFDR, a beat signal is produced by the coherent interference of the backscattered Rayleigh signal with the local reference, which realizes distributed optical fiber sensing. The dual functions of the system are evaluated by an experiment. For range measurement, a resolution of 4.8 cm at a transmitted bandwidth of 4 GHz is obtained. For distributed sensing, a reflection point detection with a resolution of 6.7 cm and a vibration measurement with a spatial resolution of 6 m at a vibration frequency of 4 kHz are demonstrated. The experiment also verified that utilizing OFDR to determine the vibration frequency, its impact on radar detection performance can be mitigated through digital signal processing.