Micro-scaled light-emitting diode (LED) technology has emerged as a transformative tool in biomedical applications, offering innovative solutions across disease surveillance, treatment, and symptom rehabilitation. In disease surveillance, micro-scaled LEDs enable real-time, noninvasive monitoring of physiological parameters through wearable devices, such as skin-like health patches and wireless pulse oximeters; these systems leverage the miniaturization, low power consumption, and high precision of micro-scaled LEDs to track heart rate, blood oxygenation, and neural activity with exceptional accuracy. For disease treatment, micro-scaled LEDs play a pivotal role in optogenetic stimulation and phototherapy. By delivering specific light wavelengths, they enable precise cellular control for cardiac regeneration, neural modulation, and targeted cancer therapies, such as photodynamic therapy with reduced invasiveness. In addition, wireless micro-scaled LED systems facilitate localized and sustained treatments for conditions such as diabetic retinopathy. For symptom rehabilitation, micro-scaled LED-based devices enhance functional and aesthetic outcomes, exemplified by optical cochlear implants for high-resolution hearing restoration and flexible photostimulation patches for hair regrowth. The performance of micro-scale LEDs also brings new possibilities to the field of brain–computer interface. These applications highlight the versatility of micro-scaled LEDs in improving patient quality of life through minimally invasive, energy-efficient, and biocompatible solutions. Although there are still challenges in long-term stability and scalability, the integration of micro-scaled LEDs with advanced biomedical technologies promises to redefine personalized healthcare and therapeutic efficacy.

Thin-film lithium niobate (TFLN) possesses great potential because it enables high-speed modulation by voltage, which allows higher resolution and lower power consumption for laser beam scanning than direct laser modulation. To achieve these functions, a red, green, and blue (RGB) multiplexer using TFLN is required as an important building block for photonic integrated circuits. We fabricated an RGB multiplexer using TFLN and experimentally confirmed its operation. Three different laser lights of red (λ = 638 nm), green (λ = 520 nm), and blue (λ = 473 nm) were successfully coupled as a single laser beam by an RGB multiplexer composed of multimode interferometers. Furthermore, the TFLN was fabricated by sputter deposition, whereas conventionally, it is fabricated via bulk-lithium niobate adhesion to the substrate. The sputter-deposited TFLN is advantageous for large-volume mass production.

Multispectral imaging, which simultaneously captures the spatial and spectral information of a scene, is widely used across diverse fields, including remote sensing, biomedical imaging, and agricultural monitoring. We introduce a snapshot multispectral imaging approach employing a standard monochrome image sensor with no additional spectral filters or customized components. Our system leverages the inherent chromatic aberration of wavelength-dependent defocusing as a natural source of physical encoding of multispectral information; this encoded image information is rapidly decoded via a deep learning-based multispectral Fourier imager network (mFIN). We experimentally tested our method with six illumination bands and demonstrated an overall accuracy of 98.25% for predicting the illumination channels at the input and achieved a robust multispectral image reconstruction on various test objects. This deep learning-powered framework achieves high-quality multispectral image reconstruction using snapshot image acquisition with a monochrome image sensor and could be useful for applications in biomedicine, industrial quality control, and agriculture, among others.

Mid-infrared (MIR) spectral imaging enables precise target identification and analysis by capturing rich chemical fingerprints, which calls for high-sensitivity broadband MIR imagers at room temperature. Here, we devise and implement a continuous-wave pumping MIR upconversion imaging system based on external-cavity enhancement, which favors a large field of view, a low cavity loss, and a high spectral resolution. The involved optical cavity is constructed in an integrated fashion by utilizing one crystal facet as a cavity mirror, which allows a 43-fold power enhancement for the single-longitudinal-mode pump at 1064 nm. In combination with the chirped-poling crystal design, high-fidelity and wide-field spectral imaging mapping is permitted to facilitate an acceptance angle of up to 28.5 deg over a spectral coverage of 2.5 to 5 μm. Moreover, a thermal locking approach is used to stabilize the cavity at high-power operation, eliminating active feedback and ensuring long-term stability. A proof-of-principle demonstration is presented to showcase real-time observation of CO2 gas injection dynamics. The implemented MIR upconversion imager features wide-field operation, high detection sensitivity, and compact footprint, which would benefit subsequent applications, including environment monitoring, gas leakage inspection, and medical diagnostics.

Passive imaging through intense atmospheric scattering is a critical yet formidable challenge in optical imaging, with profound implications across various applications. Conventional cameras struggle under severe scattering conditions, fundamentally limiting their effectiveness. We propose a groundbreaking directional atmospheric scattering model that revolutionizes passive imaging capabilities, converting a conventional camera to a super-camera. The model precisely characterizes directional photon propagation through scattering media, transforming this historically ill-posed problem into a well-posed solution, based on which a 4D spatial-angular scattering reconstruction method is proposed, which leverages both ballistic photons and directionally resolved scattered light, without relying on any scene-specific priors, to achieve unprecedented passive imaging performance enabling color imaging through over 12 transport mean free paths at distances up to 1.76 km. Our system recovers targets contributing as little as 0.00016% of the total detected signal, enhancing a standard camera’s signal recovery capacity by nearly 200×. To validate our approach, we introduce the first-ever real-world multiperspective scattering dataset, providing a critical benchmark for future research. We mark a paradigm shift in passive imaging, offering transformative potential for real-world applications under extreme atmospheric scattering conditions.

Ising problems are critical for a wide range of applications. Solving these problems on a photonic platform takes advantage of the unique properties of photons, such as high speed, low power consumption, and large bandwidth. Recently, there has been growing interest in using photonic platforms to accelerate the optimization of Ising models, paving the way for the development of ultrafast hardware in machine learning. However, these proposed systems face challenges in simultaneously achieving high spin scalability, encoding flexibility, and low system complexity. We propose a wavelength-domain optical Ising machine that utilizes optical signals at different wavelengths to represent distinct Ising spins for Ising simulation. We design and experimentally validate a chip-scale Ising machine capable of solving classical non-deterministic polynomial-time problems. The proposed Ising machine supports 32 spins and features 2 distinct coupling encoding schemes. Furthermore, we demonstrate the feasibility of scaling the system to 256 spins. This approach verifies the viability of performing Ising simulations in the wavelength dimension, offering substantial advantages in scalability. These advancements lay the groundwork for future large-scale expansion and practical applications in cloud computing.

Silicon carbide (core third-generation wide-bandgap semiconductor) nanowires have superior characteristics and vital engineering potential in microelectric and photonic devices operating in harsh high-temperature and strong-irradiation environments. Herein, the dense monocrystalline forest-like 4H- and 6H-SiC nanowires (intrinsically bound as a single crystal) are fabricated using the top–down peeling method. They exhibit broadband light emissions spanning the red–green–blue spectral region. The naturally formed microcavity encapsulating the SiC nanowires yields discrete and multimodal emission lines; the luminescence lifetimes decrease to the order of picoseconds owing to improved photon density of states in the microcavity by the quantum electrodynamic Purcell effect. The measured Purcell factor of 8.35 agrees well with the theoretical value of 8.6. The low-temperature luminescence and work functions show significant dependence on the nanowire polytype. The luminescence exhibits peculiar staircase-function enhancement when the temperature is elevated to 200 K, owing to suppression of nonradiative transition channels.

Obtaining the ground truth for imaging through the scattering objects is always a challenging task. Furthermore, the scattering process caused by complex media is too intricate to be accurately modeled by either traditional physical models or neural networks. To address this issue, we present a learning from better simulation (LBS) method. Utilizing the physical information from a single experimentally captured image through an optimization-based approach, the LBS method bypasses the multiple-scattering process and directly creates highly realistic synthetic data. The data can then be used to train downstream models. As a proof of concept, we train a simple U-Net solely on the synthetic data and demonstrate that it generalizes well to experimental data without requiring any manual labeling. 3D holographic particle field monitoring is chosen as the testing bed, and simulation and experimental results are presented to demonstrate the effectiveness and robustness of the proposed technique for imaging of complex scattering media. The proposed method lays the groundwork for reliable particle field imaging in high concentration. The concept of utilizing realistic synthetic data for training can be significantly beneficial in various deep learning-based imaging tasks, especially those involving complex scattering media.

In vivo imaging of human iris vasculature remains a persistent challenge, limiting our understanding of its relationship with ocular disease pathogenesis. Conventional raster scan optical coherence tomography angiography (OCTA) suffers from angular-dependent contrast (including blind spots), limited field of view, and prolonged imaging time—challenges that restrict its clinical utility. We introduce a circular interleaving scan OCTA method that overcomes these barriers by enabling 360 deg high-contrast iris angiography with consistent spatiotemporal sampling and optimized motion contrast. The circular scan design enables direction-optimized sampling: we configured circumferential sampling density to approximately twice the radial density, enhancing detection of radially oriented iris vasculature. A Cartesian–polar coordinate transformation was implemented for eye-motion compensation, vessel realignment, and vasculature reconstruction. Compared with raster scan OCTA, our circular scan protocol demonstrates 1.55× higher efficiency in iris vascular imaging, featuring a superior duty cycle (99.95% versus 82.00%) and eliminating redundant data acquisition from rectangular field corners (27.3% of the circular area). This method improves vessel density measurement by 39.0% and vessel count quantification by 25.2% relative to raster scans. By eliminating angular-dependent blind spots, our method significantly enhances vascular quantification reliability, paving the way to a better understanding of ocular diseases and holding promising potential for future clinical applications.

We demonstrate few-cycle pulse generation based on double-stage all-fiber nonlinear pulse compression from a thulium-doped fiber laser at a repetition rate of ∼199.74 MHz. The homemade laser provides an average power of 130 mW, serving as the seed for subsequent amplification. After amplification, significant spectral broadening to an octave-spanning bandwidth (1.2 to 2.4 μm) is attained through self-phase modulation-dominated nonlinear effects in an ultrahigh numerical aperture fiber and a highly nonlinear fiber. Followed by a two-stage nonlinear compressor, the system directly delivers near transform-limited pulses with a pulse duration of 19.8 fs (2.9 cycles at a central wavelength of 2000 nm) and a pulse energy of 3.37 nJ. To the best of our knowledge, this result is the shortest pulse duration directly generated from a thulium-doped fiber laser. This robust and simplified all-fiber system provides a promising route toward practical mid-infrared frequency comb generation and mid-infrared spectroscopy.