The mode sorter is the crucial component of the communication systems based on orbital angular momentum (OAM). However, schemes proposed so far can only effectively sort integer OAM (IOAM) modes. Here, we demonstrate the effective sorting of fractional OAM (FOAM) modes by utilizing the coordinate transformation method, which can convert FOAM modes to IOAM modes. The transformed IOAM modes are subsequently sorted using a mode conversion method called topological charge matching. The validation of our scheme is verified by implementing two FOAM sorting processes and corresponding mode purity analyses, both theoretically and experimentally. This new sorting method exhibits great potential for implementing a highly confidential and high-capacity FOAM-based communication and data storage system, which may inspire further applications in both classical and quantum regimes.

Many technologies, including dot projectors and lidar systems, benefit greatly from using polarized illumination. However, conventional polarizers and polarizing beam splitters have a fundamental limit of 50% efficiency when converting unpolarized light into one specific polarization. Here, we overcome this restriction and achieve near-complete conversion of unpolarized light to a spatially uniform polarization state over several output directions with our topology-optimized metasurfaces. Our results provide a path toward greatly improving the efficiency of common unpolarized light sources, such as LEDs, for a variety of applications requiring uniformly polarized illumination. Our fabricated metasurface realizes a 70% conversion efficiency, surpassing the aforementioned limit, and achieves a polarization extinction ratio exceeding 20, when characterized with laboratory measurements. We further demonstrate that arbitrary power splitting can be achieved between three or more polarized outputs, offering flexibility in target illumination.

Frequency-modulated continuous-wave light detection and ranging (FMCW lidar) is a powerful high-precision ranging and three-dimensional (3D) imaging technology with inherent immunity to ambient light and the ability to simultaneously yield distance and velocity information. However, the current withdraws of the traditional FMCW lidar systems are the poor resistance to environmental disturbance and high requirements for echo power, which greatly restrict their applications for high-precision ranging of noncooperative targets in dynamic measurement scenes. Here, we report an all-fiber anti-interference FMCW lidar system with high sensitivity and precision, employing a unique self-mixing stimulation radiation process for signal amplification, a special reversely chirped dual laser structure, and a common-path design for disturbance compensation. We evaluate the ranging accuracy, precision, and stability of the system completely. Finally, we demonstrate an ultralow echo detection limit of subpicowatts with a probe power of below 0.1 mW, a state-of-art localization accuracy of better than 50 μm, high stability with a standard deviation of 6.51 μm over 3 h, and high-quality 3D imaging of noncooperative objects in a fluctuating environment. With the advantages of high precision and stability, weak signal detection capability, and anti-interference ability, the proposed system has potential applications in space exploration, autodriving, and high-precision manufacturing.

Recording and identifying faint objects through atmospheric scattering media by an optical system are fundamentally interesting and technologically important. We introduce a comprehensive model that incorporates contributions from target characteristics, atmospheric effects, imaging systems, digital processing, and visual perception to assess the ultimate perceptible limit of geometrical imaging, specifically the angular resolution at the boundary of visible distance. The model allows us to reevaluate the effectiveness of conventional imaging recording, processing, and perception and to analyze the limiting factors that constrain image recognition capabilities in atmospheric media. The simulations were compared with the experimental results measured in a fog chamber and outdoor settings. The results reveal good general agreement between analysis and experiment, pointing out the way to harnessing the physical limit for optical imaging in scattering media. An immediate application of the study is the extension of the image range by an amount of 1.2 times with noise reduction via multiframe averaging, hence greatly enhancing the capability of optical imaging in the atmosphere.

Integrated electro-optic tuning devices are essential parts of optical communication, sensors, and optical machine learning. Among the available materials, silicon is the most promising for on-chip signal processing and networks. However, silicon is limited owing to the absence of efficient Pockels electro-optic tuning. Herein, we propose a new hybrid silicon-barium-titanate (Si-BTO) integrated photonic platform, in which the BTO thin film is deposited by the chemical solution deposition (CSD) method. A tunable racetrack resonator is demonstrated to confirm the Pockels electro-optic tuning potential of the BTO thin film. The hybrid racetrack resonator has a tuning efficiency of 6.5 pm/V and a high-power efficiency of 2.16 pm/nW. Moreover, the intrinsic quality factor of the fabricated racetrack resonator is 48,000, which is the highest in hybrid Si-BTO platforms, to the best of our knowledge. The high-speed test verifies the stability of the racetrack resonator. The hybrid Si-BTO technology based on the CSD method has the advantages of low equipment cost and simple fabrication process, which holds promise for low-power electro-optic tuning devices.

Optical reservoir computing (ORC) offers advantages, such as high computational speed, low power consumption, and high training speed, so it has become a competitive candidate for time series analysis in recent years. The current ORC employs single-dimensional encoding for computation, which limits input resolution and introduces extraneous information due to interactions between optical dimensions during propagation, thus constraining performance. Here, we propose complex-value encoding-based optoelectronic reservoir computing (CE-ORC), in which the amplitude and phase of the input optical field are both modulated to improve the input resolution and prevent the influence of extraneous information on computation. In addition, scale factors in the amplitude encoding can fine-tune the optical reservoir dynamics for better performance. We built a CE-ORC processing unit with an iteration rate of up to ∼1.2 kHz using high-speed communication interfaces and field programmable gate arrays (FPGAs) and demonstrated the excellent performance of CE-ORC in two time series prediction tasks. In comparison with the conventional ORC for the Mackey–Glass task, CE-ORC showed a decrease in normalized mean square error by ∼75%. Furthermore, we applied this method in a weather time series analysis and effectively predicted the temperature and humidity within a range of 24 h.

Distributed fiber-optic sensing (DFOS) can turn the worldwide fiber network into a sensing array, which may immensely extend the sensing range and approaches for hazard assessment, earth observation, and human activity measurement. However, most existing DFOS schemes cannot simultaneously give dual attention to the detection ability (for example, sensing distance) and multipoint localizing function. A mirror-image correlation method is proposed and can precisely extract the time delay between two original signals from their composite detected signal. This method enables the distributed vibration sensing function of the laser interferometer and maintains its high detection ability. We demonstrate its feasibility by simultaneously localizing multiple knocking vibrations on a 250-km round-trip fiber and distinguishing traffic vibrations at two urban positions in a field test. The localizing precision is analyzed and satisfies the requirements for fiber network sensing.

Unidirectional imagers form images of input objects only in one direction, e.g., from field-of-view (FOV) A to FOV B, while blocking the image formation in the reverse direction, from FOV B to FOV A. Here, we report unidirectional imaging under spatially partially coherent light and demonstrate high-quality imaging only in the forward direction (A → B) with high power efficiency while distorting the image formation in the backward direction (B → A) along with low power efficiency. Our reciprocal design features a set of spatially engineered linear diffractive layers that are statistically optimized for partially coherent illumination with a given phase correlation length. Our analyses reveal that when illuminated by a partially coherent beam with a correlation length of ≥∼1.5λ, where λ is the wavelength of light, diffractive unidirectional imagers achieve robust performance, exhibiting asymmetric imaging performance between the forward and backward directions—as desired. A partially coherent unidirectional imager designed with a smaller correlation length of <1.5λ still supports unidirectional image transmission but with a reduced figure of merit. These partially coherent diffractive unidirectional imagers are compact (axially spanning <75λ), polarization-independent, and compatible with various types of illumination sources, making them well-suited for applications in asymmetric visual information processing and communication.

Thin-film lithium niobate (LN) has emerged as an ideal platform for efficient nonlinear wave-mixing processes due to its strong quadratic nonlinearity and high optical confinement. We demonstrate unprecedentedly efficient second-harmonic generation (SHG) in a double-layer thin-film LN waveguide. The modal overlap between fundamental and second-harmonic waves is significantly enhanced by the polarization-reversed double layers, leading to a normalized conversion efficiency higher than 10,000% W−1 cm−2 in theory. Under the low- and high-power pumping conditions, the measured normalized and absolute conversion efficiencies are 9600% W−1 cm−2 and 85%, respectively, substantially higher than state-of-the-art values among the reported SHGs in thin-film LN waveguides. Our results hold great promise for the development of efficient and scalable nonlinear photonic devices, with applications including metrology and quantum information processing.

Efficiently tracking and imaging interested moving targets is crucial across various applications, from autonomous systems to surveillance. However, persistent challenges remain in various fields, including environmental intricacies, limitations in perceptual technologies, and privacy considerations. We present a teacher-student learning model, the generative adversarial network (GAN)-guided diffractive neural network (DNN), which performs visual tracking and imaging of the interested moving target. The GAN, as a teacher model, empowers efficient acquisition of the skill to differentiate the specific target of interest in the domains of visual tracking and imaging. The DNN-based student model learns to master the skill to differentiate the interested target from the GAN. The process of obtaining a GAN-guided DNN starts with capturing moving objects effectively using an event camera with high temporal resolution and low latency. Then, the generative power of GAN is utilized to generate data with position-tracking capability for the interested moving target, subsequently serving as labels to the training of the DNN. The DNN learns to image the target during training while retaining the target’s positional information. Our experimental demonstration highlights the efficacy of the GAN-guided DNN in visual tracking and imaging of the interested moving target. We expect the GAN-guided DNN can significantly enhance autonomous systems and surveillance.

The microring resonator (MRR) plays an important role in signal processing because high-quality bandpass filtering can be obtained at its drop port. To promote the signal-to-noise ratio, a high rejection ratio is significantly demanded. However, it is still challenging to promote the rejection ratio of the MRR-based bandpass filter. To solve this problem, we propose to use an all-pass filter to enhance the rejection ratio of the MRR-based bandpass filter. Experimental results show that the improved rejection ratio is as high as 47.7 dB, which is improved by 23.6 dB compared with that of the MRR. Meanwhile, the bandwidth of the MRR-based bandpass filter is reduced from 2.61 to 1.14 GHz due to the constructive interference in the passband. In addition, the center frequency of this ultrahigh rejection MRR can be continuously tuned from 6.26 to 46.25 GHz. The quality factor (Q) of the MRR is improved from 7.4×104 to 1.7×105. During the adjustment, the rejection ratio of the bandpass filter exceeds 40 dB. The proposed approach can be used to achieve optical bandpass filters with high performance.

The physical mechanism of gain motivation is the main theoretical bottleneck that restricts the signal-to-noise ratio (SNR) and results in a mono-merit implementation for the existing stimulated Brillouin scattering-based fiber sensors. A phase-chaos laser (PCL) is proposed and introduced in the Brillouin optical correlation domain analysis (BOCDA) scheme to promote the SNR and achieve a high-accuracy measurement. The PCL characteristics are presented, and a theoretical model of chaos gain accumulation and extraction is perfected. Then, the simulation results reveal that the SNR is improved by 5.56 dB, and the signal-to-background noise ratio (SBR) of the Brillouin gain spectrum (BGS) is promoted by 8.28 dB with a 100-km sensing distance. Further, the PCL is experimentally generated. In the proof-of-concept experiment, the accuracy of the Brillouin frequency shift is upgraded to ±0.64 MHz, and the SBR of BGS is improved by 10.77 dB. The PCL provides a new research direction for optical chaos, and the PCL-BOCDA showcases a promising future for optimal-merit-coupling sensing and its application.

In the field of short-range optical interconnects, the development of low-power-consumption, ultrawideband on-chip optical waveguide amplifiers is of critical importance. Central to this advancement is the creation of host materials that require low pump power and provide ultrabroadband emission capabilities. We introduce a tri-doped lanthanum aluminate glass (composition: 5Er2O3-5Yb2O3-0.2Tm2O3-43.8La2O3-46Al2O3), which exhibits exceptional near-infrared (NIR) luminescence intensity, significantly outperforming other bands by 3 orders of magnitude. This glass can achieve an ultrawideband NIR gain spanning 478 nm, from 1510 to 1988 nm. Notably, the glass achieves positive optical gain with a low population inversion threshold (P>0.2), highlighting its efficiency and low-power consumption. The high glass transition temperature (Tg∼842°C) and large temperature difference (ΔT∼120°C) between Tg and the onset of crystallization (Tx) indicate excellent thermal stability, which is crucial for producing high-quality amorphous films for on-chip amplifiers. This research examines the unique energy levels and spectral properties of the Er3+-Yb3+-Tm3+ tri-doped glass, assessing its potential for use in ultrawideband on-chip optical waveguide amplifiers. This work lays the groundwork for low-power, ultrabroadband on-chip waveguide amplifiers, offering new avenues for short-range optical interconnect systems.

Spectroscopy, especially for plasma spectroscopy, provides a powerful platform for biological and material analysis with its elemental and molecular fingerprinting capability. Artificial intelligence (AI) has the tremendous potential to build a universal quantitative framework covering all branches of plasma spectroscopy based on its unmatched representation and generalization ability. Herein, we introduce an AI-based unified method called self-supervised image-spectrum twin information fusion detection (SISTIFD) to collect twin co-occurrence signals of the plasma and to intelligently predict the physical parameters for improving the performances of all plasma spectroscopic techniques. It can fuse the spectra and plasma images in synchronization, derive the plasma parameters (total number density, plasma temperature, electron density, and other implicit factors), and provide accurate results. The experimental data demonstrate their excellent utility and capacity, with a reduction of 98% in evaluation indices (root mean square error, relative standard deviation, etc.) and an analysis frequency of 143 Hz (much faster than the mainstream detection frame rate of 1 Hz). In addition, as a completely end-to-end and self-supervised framework, the SISTIFD enables automatic detection without manual preprocessing or intervention. With these advantages, it has remarkably enhanced various plasma spectroscopic techniques with state-of-the-art performance and unsealed their possibility in industry, especially in the regions that require both capability and efficiency. This scheme brings new inspiration to the whole field of plasma spectroscopy and enables in situ analysis with a real-world scenario of high throughput, cross-interference, various analyte complexity, and diverse applications.