Jun Guan

Multifunctional lasers are critical for enhancing system versatility and information capacity. However, realizing multiplexed lasing across multiple degrees of freedom requires unconventional physical mechanisms. Here, we demonstrate how twisted moiré photonic superlattices can enable symmetry-encoded, multiplexed lasing characteristics. By constructing moiré lasers using perovskite nanocrystals, we demonstrate polarization-vortex beams from symmetry-protected bound states in the continuum (BICs) and linearly polarized beams from symmetry-broken radiative modes simultaneously at distinct spatial channels. We reveal that although the superlattice symmetry generates multiple BICs from photonic band folding, the asymmetric interference between the superlattice and sublattice modes turns the BICs at moiré-Γ points into radiative modes, which enables symmetry-broken polarization characters. Using twist angles as a tuning approach, we achieved independent modulation of the laser emission directionality, with the multichannel laser polarizations remaining invariant. Our discoveries of moiré-coupling-induced, multiplexed light manipulation offer opportunities for multichannel visible-light communication, holographic displays, and augmented reality.

Arithmetic competence is a unique feature of biological intelligence that combines abilities including perception, computation, and result‐presentation, yet emerging artificial intelligence still lacks these capabilities. This paper demonstrates diffractive visual processors that can perform arithmetic operations based on all‐optical and hybrid optoelectronic computing. Taking handwritten digit images as input, the diffractive networks functioned as conditional optical generative models that output adaptive arithmetic results within a single, compact design for all four basic arithmetic operations. Such optical processors were designed via knowledge distillation, where (1) a generative adversarial network (GAN) was trained as the teacher model to generate the arithmetic results based on input digits and (2) a diffractive processor was trained as the student model to replicate the transformation of the digital generator in the previous step. Compared to an arithmetic processor that generated computing output with fixed writing style, the adaptive processor achieved 60.5% operational accuracy in addition, significantly exceeding the 42.7% accuracy of the fixed‐style approach. The addition of a nonlinear encoding mechanism to the network further boosted this accuracy to 89.7%. This study represents the first analysis for the numerical competence of free‐space optical processors via their combined capabilities of visual sensation, analog computing, and optical image generation.

Bound states in the continuum (BICs) enable low‐threshold on‐chip lasing, but current implementations are restricted to emission at discrete wavelengths or in discrete directions, limiting their use in scenarios that require broadband spectra or continuous angular dispersion. Here, we exploit the topological characteristics of BICs in a hexagonal photonic lattice to engineer a split‐BIC cavity, in which a higher‐order at‐Γ BIC is deliberately split into two off‐Γ fundamental BICs. This design supports a continuous band of high‑ Q resonances over an extended region of k ‐space, yielding an ultra‐compact laser source that intrinsically combines broadband emission with monotonic wavelength–angle mapping. We experimentally realize this concept by fabricating the split‐BIC microcavity in a TiO ₂ photonic crystal and integrating it with organic dye molecules as the gain medium. Harnessing this built‐in spectral–angular correlation, we demonstrate spectrally encoded imaging, wherein one spatial dimension of an object is directly mapped onto the optical wavelength axis. Our work establishes a pathway toward fully integrated, diffractive‐optics‐free laser systems for miniaturized spectroscopic and imaging applications.

Topological photonic crystals provide a powerful platform for manipulating light. However, their flexibility in realizing diverse far-field beam profiles and polarization states is limited by the number of spatial symmetries lattices can provide. Here, we demonstrate plasmonic Dirac-vortex lasers with controlled polarization and intensity distributions by engineering photonic mass vortices in a three-dimensional parameter space. We design plasmonic Dirac-vortex cavities consisting of honeycomb lattices of aluminum nanoparticles, where photonic mass vortices are achieved by arranging distorted unit cells in an angular winding configuration. By manipulating the radial and azimuthal displacements of the nanoparticles as well as their size, taken as the third dimension of the system, we predict far-field radiation with spatially programmable polarization states and asymmetric intensity distributions. Experimentally, this is achieved by integrating organic dye molecules within the plasmonic Dirac-vortex cavities. Our work establishes a paradigm for multi-dimensional mass-enabled cavity engineering, which offers flexibility in sculpting exotic photonic states with broad implications for photonic circuits, quantum devices, and bosonic systems.

Morphological transformations are playing a key role in visual information processing with diverse applications ranging from bioimaging to video surveillance and environmental monitoring. However, these operations are becoming increasingly computationally intensive, requiring substantial memory and processing power as the size of image datasets expands. This paper describes a fast, highly parallel approach to perform morphological transformations by diffractive computing. These all‐optical processors consist of successive diffractive surfaces designed to perform dilation and erosion operations by learning the relations between input and transformed images via a deep learning‐based optimization process. Unlike existing digital methods, our free‐space diffractive devices implement these transformations in a computer‐free manner by directly processing the optical wavefront. The cascaded diffractive architecture further enables image denoising and flexible tuning of the extent and directionality of erosion/dilation through the same training process by adjusting target image datasets, realizing the synthesis of diverse transformation kernels on demand. We also demonstrate that the optical process is scalable and can process large volumes of visual information in a highly parallel manner. Experimentally, we realize such a diffractive network in a reflection configuration using a phase‐only spatial light modulator (SLM) and perform morphological transformations on both amplitude‐ and phase‐encoded images.

Morphological transformations are playing a key role in visual information processing with diverse applications ranging from bioimaging to video surveillance and environmental monitoring. However, these operations are becoming increasingly computationally intensive, requiring substantial memory and processing power as the size of image datasets expands. This paper describes a fast, highly parallel approach to perform morphological transformations by diffractive computing. These all‐optical processors consist of successive diffractive surfaces designed to perform dilation and erosion operations by learning the relations between input and transformed images via a deep learning‐based optimization process. Unlike existing digital methods, our free‐space diffractive devices implement these transformations in a computer‐free manner by directly processing the optical wavefront. The cascaded diffractive architecture further enables image denoising and flexible tuning of the extent and directionality of erosion/dilation through the same training process by adjusting target image datasets, realizing the synthesis of diverse transformation kernels on demand. We also demonstrate that the optical process is scalable and can process large volumes of visual information in a highly parallel manner. Experimentally, we realize such a diffractive network in a reflection configuration using a phase‐only spatial light modulator (SLM) and perform morphological transformations on both amplitude‐ and phase‐encoded images.

Manipulation of laser emission characteristics plays a key role in diverse technologies. However, compact lasers with internal beam steering functionalities remain a challenge. Here, we demonstrate how gradient moiré perovskite superlattices can facilitate dynamic laser beam steering. By combining a two-dimensional (2D) square lattice with a globally curved 1D perovskite grating, we fabricated gradient moiré photonic superlattices with continuously variable twist angles. We revealed how the moiré interlayer coupling at the high-symmetry points on the Brillouin zone boundaries can produce additional cavity modes with twist angle–controlled wave vectors. We showed that the moiré-induced band-edge modes can support lasing with low thresholds. By sweeping the pump area to access different twist angles, we achieved tuning of laser emission angles in a continuous and wide range (>30°) from a single device. Our work provides a highly tunable platform for moiré-induced light-matter interactions, which opens prospects for programmable and intelligent photonic technologies.

The synergistic development of nanophotonics and machine learning has inspired tremendous innovations in both fields in the past decade. In diverse photonics research, deep-learning methods using artificial neural networks become the key game changer that greatly facilitates rapid nanophotonics design and the versatile processing of optical information. Moreover, optical computing platforms that perform calculations through light propagation are receiving tremendous interest as next-generation machine-learning hardware with advantages in computing speed, energy efficiency, and parallelism. This review summarizes the current state-of-the-art nanophotonic devices enabled by machine learning and analyzes the longstanding challenges that must be overcome to make an impact on technology. We also discuss the opportunities of intelligent photonics in applications such as computational imaging/sensing and machine vision. The intersection of nanophotonics with deep learning holds tremendous implications for transformative technologies ranging from internet of things to smart health. Lastly, we provide our perspective on the pressing challenges in intelligent photonics that must be tackled to advance this field to the next level and the vast opportunities for multidisciplinary collaboration.

Emergent collective modes in lattices give birth to many intriguing physical phenomena in condensed matter physics. Among these collective modes, large-area modes typically feature small-level spacings, while a mode with stable frequency tends to be spatially tightly confined. In this Letter, we theoretically propose and experimentally demonstrate a symmetry-related large-area topological corner mode with a tunable mode area and stable frequency. This mode emerges from the hybridization of the homogeneous Dirac point mode and in-gap topological corner modes. Remarkably, this hybridized mode possesses unique chirality related to the chiral symmetry. We experimentally observe such hybridized mode in a two-dimensional (2D) photonic system and demonstrate its robustness by introducing disorders in the structure. Our findings advance the frontier of higher-order topology research, transitioning it from single-lattice systems to hybridized multilattice systems. These results may support promising potential applications, particularly in vertical-cavity surface-emitting lasers.