Optics express最新文献

High-efficiency in-couplers with unpolarized responses are crucial for the performance of waveguide augmented reality displays. Freeform quasi-3D metasurfaces (FQ3DM), which integrate freeform metasurfaces with multilayer films, is one possible solution to achieve this. However, the performance of FQ3DM is limited by the lack of inverse design algorithms capable of optimizing its overall structure. In this work, we proposed a hybrid topology optimization combining simulated annealing (HTO-SA) algorithm that alternates between topology optimization and simulated annealing to find the global optimum for both the shape and thickness of FQ3DM. With the HTO-SA algorithm, we designed an unpolarized high-efficiency in-coupler that achieves an average efficiency of 90% across a 20° field-of-view for both transverse electric and transverse magnetic polarization. We envision that our proposed approach can be generalized to the design of high-performance diffractive optical devices.

In this paper, a novel hollow-core anti-resonant optical fiber is proposed. We confirm that the U-shaped nested tubes can better compress the fiber core compared with the circular and semi-circular nested tubes to further reduce the loss and improve the single polarization characteristics. The proposed optical fiber has an ultra-low loss of 0.005 dB/m in the considered wavelength range. This is difficult to achieve in most of the previous studies. In the common wavelength band of 1550 nm, the designed fiber achieves a birefringence of about 3 × 10^-5 and a single polarization PER index of 2259 and is capable of polarization filtering with a broadband of 16 nm. Meanwhile, the proposed structure still has extremely excellent bending resistance. The critical bending radius of the designed structure is approximately 0.4 cm. It also confirms that our proposed structure has a certain ability to withstand harsh environments and can be widely applied in small, flexible fields. We believe that the designed structure has a wider range of applications in the field of fiber optic communication systems that are more sensitive to polarization, such as fiber optic gyroscopes, optical amplifiers, and fiber lasers.

The Shack-Hartmann wavefront sensor (SHWFS) is critical in adaptive optics (AO) for measuring wavefronts via centroid shifts in sub-apertures. Under extreme conditions like strong turbulence or long-distance transmission, wavefront information degrades significantly, leading to undersampled slope data and severely reduced reconstruction accuracy. Conventional algorithms struggle in these scenarios, and existing neural network approaches are not sufficiently advanced. To address this challenge, we propose a mathematically interpretable neural network-based wavefront reconstruction algorithm designed to mitigate the impact of slope loss. Experimental results demonstrate that our algorithm achieves what is believed to be unprecedented fidelity in full-aperture aberration reconstruction with up to 70% wavefront undersampling, representing a precision improvement of approximately 89.3% compared to modal methods. Moreover, the algorithm can be fully trained using simulation data alone, eliminating the need for real data acquisition and significantly enhancing practical applicability.

Based on the generative adversarial network (GAN), we present a multifunctional X-ray tomographic protocol for artifact correction, noise suppression, and super-resolution of reconstruction. The protocol mainly consists of a data preprocessing module and multifunctional GAN-based loss function simultaneously dealing with ring artifacts and super-resolution. The experimental protocol removes ring artifacts and improves the contrast-to-noise ratio (CNR) and spatial resolution (SR) of reconstructed images successfully, which shows the capability to adaptively rectify ring artifacts with varying intensities and types while achieving super-resolution. Compared with the main existing deep learning models or conventional tomographic correction methods, it also enables higher processing speed and minimal information loss, especially for images of smaller dimensions. This study provides a robust optimization tool for the equivalent realization of large fields of view and high-resolution X-ray tomography. The experimental datasets were collected from a series of X-ray cone-beam computed tomography scans of biological samples.

In conventional tomographic reconstruction, the pre-processing step includes flat-field correction, where each sample projection on the detector is divided by a reference image taken without the sample. When using coherent X-rays as a probe, this approach overlooks the phase component of the illumination field (probe), leading to artifacts in phase-retrieved projection images, which are then propagated to the reconstructed 3D sample representation. The problem intensifies in nano-holotomography with focusing optics, which, due to various imperfections creates high-frequency components in the probe function. Here, we present a new iterative reconstruction scheme for holotomography, simultaneously retrieving the complex-valued probe function. Implemented on GPUs, this algorithm results in 3D reconstruction resolving twice thinner layers in a 3D ALD standard sample measured using nano-holotomography.

The frequency stability of long-distance two-way fiber-optic radio frequency (RF) transfer is directly affected by the optical signal-to-noise ratio (OSNR) of optical amplifiers. In this paper, we have proposed a stimulated Brillouin scattering (SBS)-based optical amplification scheme with high OSNR for two-way fiber-optic RF frequency transfer over single mode fibers (SMF). At the remote and local site, the modulated carrier transferred from the opposite was amplified and then frequency upshifted by Brillouin frequency shift (BFS) for pump generation. This approach can avoid the use of additional pump lasers and phase-locked loops for wavelength stabilization of the pump. The pump was injected into the fiber link and counter-propagated with the signal to amplify the modulated carrier. A 2.2 GHz RF signal transfer with the proposed SBS-based optical amplification schemes was demonstrated over a 100 km fiber link. The experimental results illustrated that the OSNR was increased by 35 dB and 32 dB at the local site and the remote site, respectively, compared to the results obtained with erbium-doped fiber amplifiers (EDFA)-based optical amplification. Benefiting from improved OSNR, the frequency stability was increased by more than one order of magnitude below 1000 s averaging time, and the phase noise was reduced to the noise floor below 0.1 Hz offset frequency. The proposed optical signal amplification approach has a potential application for transferring atomic clocks over long-haul fiber links.

This work presents an integrated chip of a resonant cavity light emitter and photon detector (RCLEPD) to address the requirements of wearable optical medical devices for compact size, high efficiency, and interference resistance sensors. The optical radiation pattern and light extraction efficiency of the resonant cavity light emitting diode (RCLED) as well as the optical absorption spectrum of the resonant cavity enhanced photon detector (RCEPD) are theoretically simulated. Additionally, the wavelength selectivity of the RCEPD absorption spectrum is analyzed. Material epitaxial growth of RCLEPD was performed using metal-organic chemical vapor deposition (MOCVD), and an integrated sensing chip with an area of 2 × 2 mm² was fabricated. Experimental results demonstrate that RCLED achieves a maximum external quantum efficiency of 10.206%, consistent with the simulation results, while maintaining a peak wavelength at 677.5 nm within a current range of 0-20 mA. Furthermore, the RCEPD exhibits a peak response wavelength at 678 nm, matching that of the RCLED. Utilizing RCLEPD as the sensor, photoplethysmography (PPG) signals are collected from the human wrist under different RCLED driving currents resulting in an average period of 977 ms which aligns with a human pulse frequency of 61 beats/min. With further processing techniques applied to PPG signals, RCLEPD is expected to be used as a sensor in wearable blood pressure and glucose monitoring devices.

Machine learning-based demodulation of multi-peak fiber Bragg grating (FBG) sensors has been extensively studied, demonstrating superior performance compared to conventional algorithms because it can neglect potential physical constraints. As the number of real-world monitoring points increases, the volume of fiber-optic sensing data grows exponentially. This necessitates aggregating data from various regions (e.g., via Wi-Fi), unlike traditional single-point monitoring, which challenges server storage capacity and communication efficiency. To address these issues, this paper proposes a federated learning (FL)-based framework for efficient wavelength demodulation of multi-peak FBGs in multipoint monitoring. Specifically, an arrayed waveguide grating (AWG) with multiplexing capability is employed at different monitoring points to convert spectral features into multi-channel transmission intensities, serving as training data for local models. Subsequently, the local model parameters, trained independently at each point, are uploaded to a central server to derive the optimal global model for demodulation across different monitoring points. The proposed demodulation framework is validated through stress demodulation experiments on multi-peak FBG sensors. Experimental results indicate strong multi-peak extraction performance with a demodulation error of ±0.64 pm. Additionally, the method demonstrates excellent applicability for generating high-precision global demodulation models through multi-node cooperative work under various scenarios.

Light detection and ranging (LiDAR) utilizes eye-safe laser beams to perceive the world in three-dimensional (3D) detail, offering machines and computers with an accurate representation of their surroundings. This technology is widely employed in metrology, environmental monitoring, archaeology, and robotics. However, the presence of scattering media in the optical path, such as fog, dust, or translucent plates, will cause light scattering and occlude direct observation of the scene. To address scattering distortions, conventional methods require the prior knowledge of the scattering media or the target location, limiting their applicability outside the laboratory. Leveraging single-photon sensitivity and time-gated technology, single photon LiDAR emerges as a promising solution for active scattering imaging. In this study, we construct a single-photon LiDAR prototype and demonstrate its capability to perform 3D imaging of a room-scale (1.1 m × 1.1 m × 4 m) hidden scene behind a ground glass diffuser located approximately 50 meters away from the imaging system. Incorporating phase function to construct the forward model and considering the system-induced temporal broadening, our method is capable of producing reliable results behind various scattering layers. The results indicate potential applications such as remote non-invasive testing and detection in challenging scenarios.

The two-point source longitudinal resolution of three-dimensional integral imaging depends on several factors including the number of sensors, sensor pixel size, pitch between sensors, and the lens point spread function. We assume the two-point sources to be resolved if their point spread functions can be resolved in any one of the sensors. Previous studies of integral imaging longitudinal resolution either rely on geometrical optics formulation or assume the point spread function to be of sub-pixel size, thus neglecting the effect of the lens. These studies also assume both point sources to be in focus in captured elemental images. More importantly, the previous analysis does not consider the effect of noise. In this manuscript, we use the Gaussian process-based two-point source resolution criterion to overcome these limitations. We compute the circle of confusion to model the out-of-focus blurring effect. The Gaussian process-based two-point source resolution criterion allows us to study the effect of noise on the longitudinal resolution. In the absence of noise, we also present a simple analytical expression for longitudinal resolution which approximately matches the Gaussian process-based formulation. Also, we investigate the dependence of the longitudinal resolution on the parallax of the integral imaging system. We present optical experiments to validate our results. The experiments demonstrate agreement with our Gaussian process-based two-point source resolution criteria.