Volume 14, Issue 4

Empowering nanophotonic devices via artificial intelligence (AI) has revolutionized both scientific research methodologies and engineering practices, addressing critical challenges in the design and optimization of complex systems. Traditional methods for developing nanophotonic devices are often constrained by the high dimensionality of design spaces and computational inefficiencies. This review highlights how AI-driven techniques provide transformative solutions by enabling the efficient exploration of vast design spaces, optimizing intricate parameter systems, and predicting the performance of advanced nanophotonic materials and devices with high accuracy. By bridging the gap between computational complexity and practical implementation, AI accelerates the discovery of novel nanophotonic functionalities. Furthermore, we delve into emerging domains, such as diffractive neural networks and quantum machine learning, emphasizing their potential to exploit photonic properties for innovative strategies. The review also examines AI’s applications in advanced engineering areas, e.g., optical image recognition, showcasing its role in addressing complex challenges in device integration. By facilitating the development of highly efficient, compact optical devices, these AI-powered methodologies are paving the way for next-generation nanophotonic systems with enhanced functionalities and broader applications.

Dielectric metasurfaces supporting optical resonances have become a promising platform for quantum and nonlinear optics. However, resonant metasurfaces remain limited in their capacity to independently control the behavior of many distinct resonances despite efforts in computational optimization and inverse design. In this work, we overcome longstanding limitations by introducing a generalized rational design paradigm based on symmetry. Specifically, we use symmetry-broken metasurfaces with periodic “quadromer” lattices comprised of four nanostructures per unit cell to enable extensive control of multiple optical resonances. The rationally designed metasurfaces are readily fabricable, and we experimentally demonstrate metasurfaces that support up to four high Q-factor resonances with deliberately chosen free-space polarizations, spectral separations, and mode profiles. Our design paradigm may unlock new applications for multiresonant metasurfaces in quantum and nonlinear optics, optical sensing, and augmented reality displays.

A new concept of electrochemically modulated single-molecule localization super-resolution imaging is developed. Applications of single-molecule localization super-resolution microscopy have been limited due to insufficient availability of qualified fluorophores with favorable low duty cycles. The key for the new concept is that the “On” state of a redox-active fluorophore with unfavorable high duty cycle could be driven to “Off” state by electrochemical potential modulation and thus become available for single-molecule localization imaging. The new concept was carried out using redox-active cresyl violet with unfavorable high duty cycle as a model fluorophore by synchronizing electrochemical potential scanning with a single-molecule localization microscope. The two cytoskeletal protein structures, the microtubules from porcine brain and the actins from rabbit muscle, were selected as the model target structures for the conceptual imaging in vitro . The super-resolution images of microtubules and actins were obtained from precise single-molecule localizations determined by modulating the On/Off states of single fluorophore molecules on the cytoskeletal proteins via electrochemical potential scanning. Importantly, this method could allow more fluorophores even with unfavorable photophysical properties to become available for a wider and more extensive application of single-molecule localization microscopy.

Accurate and fast characterization of nanostructures using spectroscopic ellipsometry (SE) is required in both industrial and research fields. However, conventional methods used in SE data analysis often face challenges in balancing accuracy and speed, especially for the in situ monitoring on complex nanostructures. Additionally, optical constants are so crucial for accurately predicting structural parameters since SE data were strongly related to them. This study proposes a three-step algorithm designed for fast and accurate extraction of structural parameters from SE measurements. The method utilizes three neural networks, each trained on simulation data, to obtain optical constants and progressively refine the prediction on structural parameters at each step. When tested on both simulation and measurement data on the fabricated 1D SiO 2 nanograting specimen, the algorithm demonstrated both high accuracy and fast analysis speed, with average mean absolute error (MAE) of 0.103 nm and analysis speed of 132 ms. Also, the proposed algorithm shows more flexibility in accounting for any change of optical constants to serve as a more efficient solution in the real-time monitoring.

The quasi-bound state in the continuum (quasi-BIC) of dielectric metasurface provides a crucial platform for sensing, because its almost infinite Q -factor can greatly enhance the interactions between light waves and the analytes. In this work, we proposed an ultrasensitive all-dielectric metasurface sensor composed of periodic rectangular amorphous silicon pillars on a quartz substrate. By breaking symmetry of two pillars in unit cell, high Q quasi-BIC in the continuous near-infrared band can be excited. The magnetic toroidal dipole (MTD) is demonstrated to play a dominating role in the resonant modes by analyzing near-field distribution and multipole decomposition. The asymmetry degree has a significant impact on sensing performance of the proposed metasurface sensor, whose underlying physical mechanisms is analyzed by perturbation theory. The transmission spectrum and sensing performance of the fabricated metasurface sensor were measured. The experimental results show our designed metasurface sensor not only achieve a high sensitivity of 413/RIU, but also shows a high figure of merit (FOM) of 66 RIU −1 . This work provides excellent prospects for the excitation of strong MTD resonance quasi-BIC in sensing applications.

Optical encryption offers a powerful platform for secure information transfer, combining low power consumption, high-speed transmission, and intuitive visualization. Metasurfaces, with their unprecedented ability to manipulate light across multiple degrees of freedom within quasi-two-dimensional nanostructures, are emerging as promising devices for advanced encryption. However, encryption capacity remains constrained by limited information channels. Here, we present a visual secret sharing (VSS) scheme utilizing metasurfaces with multiple polarization-dependent channels and minimized crosstalk. Using a global optimization strategy for nanostructure geometries across the entire metasurface, we successfully realize eight independent polarization channels with negligible crosstalk. By encoding both the key and information into these channels with a modified VSS scheme, we demonstrate the complete recovery of seven plaintexts. This strategy supports scalable, high-capacity encryption, and can incorporate additional optical degrees of freedom, offering advanced solutions for advanced secure communication, information storage, and anti-counterfeiting.

Mid-infrared (Mid-IR) photodetection and imaging are pivotal across diverse applications, including remote sensing, communication, and spectral analysis. Among these, single-pixel imaging technology is distinguished by its exceptional sensitivity, high resolution attainable through the sampling system, and economic efficiency. The quality of single-pixel imaging primarily depends on the performance of the photodetector and the sampling system. Photodetectors based on black phosphorus (BP) exhibit low dark current, high specific detectivity ( D * ), and room-temperature operability. Artificial intelligence (AI)-assisted sampling systems feature efficient and intelligent data reconstruction capabilities. In this work, we demonstrate an AI-driven black phosphorus (BP)/molybdenum disulfide (MoS 2 )/hexagonal boron nitride (hBN) heterojunction for Mid-IR photodetection and imaging. By optimizing the thickness of the heterojunction, the quality of the interface, and the AI algorithm, we achieved high-performance Mid-IR photodetection and imaging. Specifically, the photodetector has a responsivity of 0.25 A/W at a wavelength of 3,390 nm, an extremely high D * of 3.7 × 10 9 Jones, a response speed as low as 7 ms, and after AI optimization, the image contrast ratio has been improved from 0.227 to 0.890. At the same time, the sampling rate requirement can be reduced to 25 %. Our research indicates that the efficient combination of BP heterojunction photodetectors and AI technology is expected to accelerate the development of Mid-IR photodetectors and imaging systems.

Photonic crystal surface-emitting lasers (PCSELs) are promising light sources with numerous advantages, including vertical emission, single-mode operation, and high output power. However, the fabrication of PCSEL devices requires advanced techniques, such as wafer bonding or epitaxial regrowth, to form a photonic crystal (PhC) structure close to the central waveguide layer. This process is not only complicated but also necessitates multiple semiconductor epitaxies, which reduces fabrication yield and increases manufacturing costs. In this study, we introduce a simpler method for fabricating PCSELs that requires only a single dry-etch run on any standard edge-emitting laser diode epistructure. The key challenge of creating an array of PhC air holes deep enough to reach the waveguide layer is addressed through high-temperature, high-plasma-density dry etching. PCSEL devices fabricated using this method lased in single mode at a threshold current density as low as ∼0.8 kA/cm 2 , which is comparable to or better than previously demonstrated devices. Our results offer a cost-effective, high-yield approach to PCSEL fabrication.

Advanced optical technologies, such as next-generation displays and holographic systems, demand high efficiency, lightweight designs, compact dimensions, and compatibility with curved and thin substrates. However, current optical devices for virtual and augmented reality displays, such as surface relief and holographic gratings, face challenges like light scattering, low optical efficiency, and limited scalability. Here, we present a geometric phase optical element (GPOE) fabricated using geometric-phase modulation. A pixelated nano grating system was employed, and reactive mesogens were aligned and transferred via a top-down imprinting process. The resulting GPOE exhibits a thin profile, compatibility with curved surfaces, and scalability for mass production. Integrating additional optical components, we realized a multi-focal GPOE and validated its optical performance. This innovation highlights the potential of GPOEs as compact, next-generation optical components for advanced curved-surface systems.

Among the approaches in three-dimensional (3D) single molecule localization microscopy, there are several point spread function (PSF) engineering approaches, in which depth information of molecules is encoded in 2D images. Usually, the molecules are excited sparsely in each raw image. The consequence is that the temporal resolution has to be sacrificed. In order to improve temporal resolution and ensure localization accuracy, we propose a method, SH-CS, based on light needle excitation, detection system with single helix-point spread function (SH-PSF), and compressed sensing (CS). Although the SH-CS method still has a limitation about the molecule density, it is suited for relatively dense molecules. For each light needle scanning position, an SH image of excited molecules is processed with CS algorithm to decode their axial information. Simulations demonstrated, for random distributed 1–15 molecules in depth range of 4 μm, the axial localization accuracy is 12.1–73.5 nm. The feasibility of this method is validated with a designed 3D sample composed of fluorescent beads.