Applied optics最新文献

Chemical vapor deposition (CVD) diamond has high thermal conductivity and a low coefficient of thermal expansion, making it a good thermal conductive material. However, conventional processing methods are often difficult to balance processing quality while pursuing high efficiency. In view of this, this study focuses on the milling of CVD single crystal diamond using nanosecond and picosecond laser technology, aiming to optimize its processing accuracy and efficiency. By systematically investigating the effects of laser incidence angle, pulse width, and spot size on the milling angle of diamond. It is found that the slope angle of the machined surface can be effectively reduced by decreasing the laser incidence angle, shortening the pulse width, and reducing the spot size. With an average power of 200 W, a pulse width of 12 ps, a spot diameter of 60 µm, an incidence angle of 3°, and a scanning speed of 30 mm/s, the milling angle of diamond can be optimized to 1.30°, and at the same time, the surface roughness Sa is 0.42 µm, the maximum height difference of the surface Sz is 2.76 µm, and the machining efficiency reaches 32.57mm³/h. When the pulse width is adjusted to 150 ns and the rest of the parameters are kept unchanged, the milling angle of diamond is 2.45°, the Sa is 0.45 µm, the Sz is 2.88 µm, and the machining efficiency is improved to 66.10mm³/h. The present study proposes a high-efficiency and low-damage machining strategy for chip bonding diamond, which provides an important reference for the application of diamond in the field of microelectronics encapsulation.

This study presents a neural-network-assisted framework for accurate radiation thermometry in high-temperature cavities, overcoming challenges from unknown emissivity and multi-reflection effects. The method combines Monte Carlo ray-tracing with deep learning through: (1) physics-informed training data including diffuse/specular reflections, (2) alternating neural networks for decoupled temperature/emissivity prediction, and (3) full multi-reflection modeling. Validated with zirconia in a graphite cavity within the 1273-1673 K temperature range and 2-16 µm spectral range, it achieves 0.7% temperature error (9 K, compared to real temperature) and 0.05-0.1 emissivity error in 2-16 µm, outperforming first-order methods (neglecting multiple reflections) by 5%-27% (peak at 2 µm) in emissivity reconstruction. The framework maintains <1% error, with only 10 spectral channels and tolerates 1% intensity noise (<1.8% variation), enabling reliable thermometry in low-emissivity materials like alloys and ceramics where conventional methods may fail.

Metamaterials, with their unique subwavelength-scale structures, enable exceptional control over electromagnetic properties, making them ideal for advanced optical devices. This study introduces a novel seven-layer metamaterial absorber, to our knowledge, designed for ultra-broadband absorption across the near-infrared to mid-infrared spectrum (2.3-7.5 µm). Comprising alternating titanium (Ti) and gallium arsenide (GaAs) layers, the absorber achieves an average absorptance of 97.8% and a peak absorptance of 99.8%. A deep neural network (DNN) optimizes structural parameters, ensuring high performance. The absorber's absorption mechanism, analyzed through electromagnetic field distributions, reveals contributions from localized surface plasmon resonance (LSPR), propagating surface plasmon resonance (PSPR), inter-ring coupling, and Fabry-Pérot resonances. The design exhibits robust performance, with insensitivity to incident and polarization angles up to 60° and 90°, respectively. Comparative analysis with recent infrared absorbers highlights its superior bandwidth and absorptance, positioning it as a promising candidate for applications in solar energy systems and infrared stealth technology.

Calibration of multimodal 3D imaging systems that combine structured light with an additional modality typically relies on targets constructed with physical features that must be detectable by all imaging modalities. Such targets can be costly to produce and are prone to fabrication defects that degrade accuracy. Furthermore, reflections, light saturation, and the limited resolution of non-visible-range cameras complicate reliable feature detection. We present a calibration approach that uses digital features generated by a screen, a mirror, and an auxiliary camera-removing the need for specialized targets with physical features. This setup recovers the intrinsic parameters of the visible camera as well as the intrinsic and extrinsic parameters of both the projector and the additional-modality camera. To illustrate our method, we employ a thermal camera, though the procedure extends readily to other imaging modalities. Experimental results show that the proposed solution achieves a 0.07mm root-mean-square error in 3D reconstructions, matching conventional techniques. By eliminating the requirement for physical features for targets, this approach reduces costs, avoids fabrication flaws, and simplifies multimodal feature detection.

Holographic display, as one of the most promising three-dimensional visualization technologies, faces dynamic advancement constraints due to the inherent trade-off between spatial light modulators refresh rates and holographic data volume. However, low-bit-depth solutions enable significantly higher refresh rates, thereby fulfilling the critical demand for dynamic display. This study proposes a quantized stochastic gradient descent iterative algorithm that enables direct-generation of 4-bit-depth phase-only holograms at 2K resolution. By implementing a differentiable quantization constraint, we successfully compress conventional 8-bit holograms to 4-bit depth while accelerating convergence through the same quantization strategy embedded in the initial random phase map. The time-multiplexing technique is employed to suppress quantization-induced speckle during reconstruction. Both numerical simulations and optical experiments demonstrate that quantized holograms perform better than traditional iterative algorithms in terms of reconstruction quality. This method provides an efficient solution for data-intensive applications including dynamic holographic displays and low-power holographic storage systems.

With the rapid development of unmanned aerial vehicle (UAV) technology, establishing effective management systems for unmanned aerial vehicles has become increasingly important. Tracking small UAVs in complex environments using infrared imagery is a crucial yet challenging task, owing to limited target visibility and significant background clutter. Further, existing feature extraction methods struggle to effectively capture pixel-level infrared UAV signatures. Therefore, this paper introduces SiamDA, a detail-attentive anchor-free Siamese tracker designed to capture more infrared spectral details to enhance the representation of weak UAV targets. First, a detail-attentive network that employs deformable convolutions to capture fine-grained features, along with a Taylor-difference-inspired edge enhancement module to sharpen boundaries and reinforce geometric shapes of small UAVs. Then, a normalized Wasserstein distance loss and a dynamic template update scheme are integrated to improve tracking robustness. Evaluations on public near-infrared UAV datasets indicate that SiamDA attains an average precision (P₅) of more than 80%, surpassing state-of-the-art trackers trained on the same dataset.

To meet the measurement requirements for the precise assembly of support trusses during the spatial reconstruction of ultra-large-aperture optical systems, this paper presents a multi-sensor-assisted alignment deviation measurement system and a suitable global calibration method. By integrating multi-source data from dual visual cameras, a biaxial inclinometer, and laser rangefinders, the system represents a unified measurement network, thereby overcoming the limitations of monocular vision systems in scenarios with sparse targets, restricted fields of view, and environmental disturbances. The paper describes the modeling of the measurement system and the calibration of the sensors. By defining coordinate frameworks and leveraging the respective transformation relationships, a measurement model for optimal truss alignment is developed. The systematic calibration approach can be applied in cases in which the system has unknown parameters, including camera focal lengths, laser ranging data, relative poses of dual cameras, and the relationship between the cameras and the alignment coordinate frame. Subsequently, the calibrated system parameters are integrated into the measurement model to quantify truss-alignment deviations. Experimental measurements confirm both the effectiveness of the developed multi-sensor measurement framework and the accuracy of the calibration parameters. Therefore, this study provides a feasible measurement and calibration solution for truss assembly in the spatial reconstruction of extremely large-aperture optical systems.

We experimentally quantify loss difference requirements in an Er:YAG monolithic nonplanar ring oscillator (NPRO) during power scaling. A linear correlation (R²=0.98) between the pump power and the required loss difference threshold was demonstrated for the first time, to the best of our knowledge. The slope of the fitting line is 0.0016%/W, indicating that at high pump powers (>6.5W), the required loss difference exceeds the empirical threshold of 0.01%. Our work provides a reference for the design of future NPRO, particularly those requiring higher power or lower magnetic field strengths.

In this paper, a security-enhanced optical image authentication method is proposed based on cascaded geometric-phase metasurfaces. In the encryption process, an original plaintext image is first encoded into an authentication amplitude by using a sparse constraint encoding algorithm. Subsequently, the Fourier phase-only hologram of the authentication amplitude is calculated by employing an iterative Fourier transform algorithm, and then it is decomposed into a ciphertext phase and a key phase. Finally, the ciphertext phase and the key phase are, respectively, constructed as the ciphertext metasurface and the key metasurface through a geometric-phase metasurface unit structure, thus forming two physically separated metasurfaces. During authentication, the ciphertext metasurface and the key metasurface need to be cascaded to achieve the authentication of user identity, overcoming a common problem that the current metasurface-based authentication methods lack physical security keys. Numerical simulations are performed to demonstrate the proposed method, and the simulation results show that the proposed method exhibits high feasibility and strong security-enhanced effect as well as large key space.

In this paper, a reflective terahertz broadband coding metasurface based on a genetic algorithm using Dirac semimetals as tunable materials is presented, which can realize flexible control of beam steering in the frequency range of 1.49-1.63 THz. First, the three-layer coding metasurface unit with the top layer of the Dirac semimetal pattern is designed using the electrically tunable dielectric constant of Dirac semimetals, which is capable of 2-bit coding in the frequency range of 1.49-1.63 THz. Then, the array arrangements of the coding metasurface are reverse-designed using a genetic algorithm. The results show that not only can both single- and multi-beams be realized at continuous arbitrary angles in the range of an elevation angle of 30° and an azimuth angle of 360°, but also the elevation angle and azimuth angle of each beam in the multi-beam can be controlled independently, which improves the flexibility of terahertz beam steering. Moreover, the steering range of the single-beam elevation angle is further expanded to 39° by employing the Fourier convolution addition rule. Therefore, the proposed terahertz broadband beam-steering Dirac semimetal-coding metasurface has certain application prospects in the fields of terahertz broadband communications and antennas.