Hasil untuk "Applied optics. Photonics"

M. Limonov, M. V. Rybin, A. Poddubny et al.

Di Zhu, Linbo Shao, Mengjie Yu et al.

Lithium niobate (LN), an outstanding and versatile material, has influenced our daily life for decades—from enabling high-speed optical communications that form the backbone of the Internet to realizing radio-frequency filtering used in our cell phones. This half-century-old material is currently embracing a revolution in thin-film LN integrated photonics. The successes of manufacturing wafer-scale, high-quality thin films of LN-on-insulator (LNOI) and breakthroughs in nanofabrication techniques have made high-performance integrated nanophotonic components possible. With rapid development in the past few years, some of these thin-film LN devices, such as optical modulators and nonlinear wavelength converters, have already outperformed their legacy counterparts realized in bulk LN crystals. Furthermore, the nanophotonic integration has enabled ultra-low-loss resonators in LN, which has unlocked many novel applications such as optical frequency combs and quantum transducers. In this review, we cover—from basic principles to the state of the art—the diverse aspects of integrated thin-film LN photonics, including the materials, basic passive components, and various active devices based on electro-optics, all-optical nonlinearities, and acousto-optics. We also identify challenges that this platform is currently facing and point out future opportunities. The field of integrated LNOI photonics is advancing rapidly and poised to make critical impacts on a broad range of applications in communication, signal processing, and quantum information.

A. Khanikaev, G. Shvets

Asaph Hall

X. Qiang, Xiaoqi Zhou, Jianwei Wang et al.

Photonics is a promising platform for implementing universal quantum information processing. Its main challenges include precise control of massive circuits of linear optical components and effective implementation of entangling operations on photons. By using large-scale silicon photonic circuits to implement an extension of the linear combination of quantum operators scheme, we realize a fully programmable two-qubit quantum processor, enabling universal two-qubit quantum information processing in optics. The quantum processor is fabricated with mature CMOS-compatible processing and comprises more than 200 photonic components. We programmed the device to implement 98 different two-qubit unitary operations (with an average quantum process fidelity of 93.2 ± 4.5%), a two-qubit quantum approximate optimization algorithm, and efficient simulation of Szegedy directed quantum walks. This fosters further use of the linear-combination architecture with silicon photonics for future photonic quantum processors. A fully programmable two-qubit quantum processor with more than 200 components is demonstrated by using silicon photonic circuits. A two-qubit quantum approximate optimization algorithm and simulation of Szegedy quantum walks are implemented.

A. Solntsev, G. Agarwal, Y. Kivshar

Rapid progress in the development of metamaterials and metaphotonics allowed bulky optical assemblies to be replaced with thin nanostructured films, often called metasurfaces, opening a broad range of novel and superior applications of flat optics to the generation, manipulation and detection of classical light. Recently, these developments started making headway in quantum photonics, where novel opportunities arose for the control of non-classical nature of light, including photon statistics, quantum state superposition, quantum entanglement and single-photon detection. In this Perspective, we review recent progress in the emerging field of quantum-photonics applications of metasurfaces, focusing on innovative and promising approaches to create, manipulate and detect non-classical light. Progress in the field of quantum-photonics applications of metasurfaces is reviewed. Cutting-edge research, including the development of optical chips supporting high-dimensional quantum entanglement and advanced quantum tomography, is summarized.

R. Marchetti, C. Lacava, L. Carroll et al.

Over the last 20 years, silicon photonics has revolutionized the field of integrated optics, providing a novel and powerful platform to build mass-producible optical circuits. One of the most attractive aspects of silicon photonics is its ability to provide extremely small optical components, whose typical dimensions are an order of magnitude smaller than those of optical fiber devices. This dimension difference makes the design of fiber-to-chip interfaces challenging and, over the years, has stimulated considerable technical and research efforts in the field. Fiber-to-silicon photonic chip interfaces can be broadly divided into two principle categories: in-plane and out-of-plane couplers. Devices falling into the first category typically offer relatively high coupling efficiency, broad coupling bandwidth (in wavelength), and low polarization dependence but require relatively complex fabrication and assembly procedures that are not directly compatible with wafer-scale testing. Conversely, out-of-plane coupling devices offer lower efficiency, narrower bandwidth, and are usually polarization dependent. However, they are often more compatible with high-volume fabrication and packaging processes and allow for on-wafer access to any part of the optical circuit. In this paper, we review the current state-of-the-art of optical couplers for photonic integrated circuits, aiming to give to the reader a comprehensive and broad view of the field, identifying advantages and disadvantages of each solution. As fiber-to-chip couplers are inherently related to packaging technologies and the co-design of optical packages has become essential, we also review the main solutions currently used to package and assemble optical fibers with silicon-photonic integrated circuits.

Tingzhao Fu, Yubin Zang, Yuyao Huang et al.

Machine learning technologies have been extensively applied in high-performance information-processing fields. However, the computation rate of existing hardware is severely circumscribed by conventional Von Neumann architecture. Photonic approaches have demonstrated extraordinary potential for executing deep learning processes that involve complex calculations. In this work, an on-chip diffractive optical neural network (DONN) based on a silicon-on-insulator platform is proposed to perform machine learning tasks with high integration and low power consumption characteristics. To validate the proposed DONN, we fabricated 1-hidden-layer and 3-hidden-layer on-chip DONNs with footprints of 0.15 mm^2 and 0.3 mm^2 and experimentally verified their performance on the classification task of the Iris plants dataset, yielding accuracies of 86.7% and 90%, respectively. Furthermore, a 3-hidden-layer on-chip DONN is fabricated to classify the Modified National Institute of Standards and Technology handwritten digit images. The proposed passive on-chip DONN provides a potential solution for accelerating future artificial intelligence hardware with enhanced performance. Integrating diffractive optical neural networks (DONN) would reduce errors due to bulky components and calibration. Here, the authors exploit integrated 1D dielectric metasurfaces to realise an on-chip DONN device with 90% classification accuracy, computing at 10^16 flops/mm^2 and consuming 10E-17 J/Flop.

F. Othman, F. Othman, P. Bartie et al.

Point cloud data from aerial LiDAR scan (“ALS”) are used for object detection and classification of energy industry facilities and assets. It is advantageous to be able to carry out point cloud classifications in near real time on secure hardware at the survey location and to be able to rapidly train the model on custom object classes. Such requirements create the need for efficient deep learning architectures which produce accurate predictions with low computational cost and time. This research presents a solution using Modified Point Voxel CNN (“MPVCNN”) which consists of feature-level fusion between voxel and point features for local feature extraction. In doing so, this architecture circumvents indexing operations and GPU memory limitations. The MPVCNN developed in this research was trialled using dense DALES datasets. Additionally, Aerial LiDAR scan datasets typically suffer from a class imbalance for rare objects and those which are physically small or thin-shaped, relative to other object classes. This research explores how a second classification pass can be used to improve the initial classification prediction for such imbalanced object classes, by using predicted class labels as a criterion to group points which are semantically homogeneous in computing geometric features. This paper demonstrates that the MPVCNN architecture is capable of high accuracy (>0.9 F1-score and OA) classifications, with short training times (approximately 1 hour), on dense ALS datasets using standard hardware (e.g. 8GB GPU).

Kyriakos Skarsoulis, Konstantinos Makris, Demetri Psaltis

We examine the dynamic response of a waveguide with a PT-symmetric complex potential to perturbations in its refractive index. The output transverse intensity profile is recorded as different index perturbations are imposed. The waveguide exhibits its highest sensitivity when it is operating near its exceptional point (EP). A similar behavior is observed when multiple waveguides are coupled and operated around the new EP. A neural network is deployed to decode the cross-sectional intensity images and classify the strengths and positions of the applied perturbations. The proposed waveguide’s behavior near the EP allows the neural network to characterize the perturbations with high accuracy despite noise augmentation, contrary to the Hermitian case. Beyond 1D profiles, this scheme can be readily extended to recover full 2D perturbation distributions. A 1D PT-symmetric lattice structure comprised of five coupled waveguides is able to train a deep neural network capable of reconstructing a 2D perturbation map within the lattice from noisy intensity data, contrary to the Hermitian system. The results show how PT symmetry can be utilized in waveguides to create efficient sensors and imaging devices.

Haoran Zhang, Yuxi Li

Spatiotemporal optical vortices (STOVs) exhibit transverse orbital angular momentum (OAM) that is perpendicular to the propagation direction of the pulse, presenting significant prospects for applications in optical manipulation, information transmission, and terahertz devices. However, existing STOV generation schemes utilizing photonic crystal plates are limited in their ability to independently and dynamically regulate both the transverse OAM and the topological structure of dark lines. Furthermore, the positions and orientations of these dark lines are typically constrained by the symmetry of the system. This paper proposes and validates a tunable STOV generator based on magneto-optic photonic crystal plates. By introducing the magneto-optical effect of magnetic materials and jointly adjusting the intensity and direction of the applied magnetic field, we have achieved multi-degree-of-freedom control over topological singularities and dark lines. The adjustment of the magneto-optical constant Q facilitates the continuous modulation of the generation frequency of singularities and dark lines. Furthermore, altering the direction of the magnetic field allows for the breaking of the in-plane mirror symmetry, thereby enabling directional inclination and overall offset of dark lines in momentum space. This work provides an effective solution for the active and dynamic control of spatiotemporal vortices, overcoming symmetry constraints on the generation of STOVs and demonstrating significant application potential in integrated terahertz vortex light sources, optical manipulation, and communication fields.

Sajid Ali, Shafiq Ahmad, A. Ullah et al.

G. Rodari, Tommaso Francalanci, E. Caruccio et al.

Over the past few years, various methods have been developed to engineeer and to exploit the dynamics of photonic quantum states as they evolve through linear optical networks. Recent theoretical works have shown that the underlying Lie algebraic structure plays a crucial role in the description of linear optical Hamiltonians, as such formalism identifies intrinsic symmetries within photonic systems subject to linear optical dynamics. Here, we experimentally investigate the role of Lie algebra applied to the context of Boson sampling, a pivotal model to the current understanding of computational complexity regimes in photonic quantum information. Performing experiments of increasing complexity, realized within a fully-reconfigurable photonic circuit, we show that sampling experiments do indeed fulfill the constraints implied by a Lie algebraic structure. In addition, we provide a comprehensive picture about how the concept of Lie algebraic invariant can be interpreted from the point of view of n-th order correlation functions in quantum optics. Our work shows how Lie algebraic invariants can be used as a benchmark tool for the correctness of an underlying linear optical dynamics and to verify the reliability of Boson Sampling experiments. This opens new avenues for the use of algebraic-inspired methods as verification tools for photon-based quantum computing protocols.

Siyu Lu, Z. Cao, Jinzhong Ling et al.

Liquid microlenses and their arrays (LMLAs) have emerged as a transformative platform in adaptive optics, offering superior reconfigurability, compactness, and fast response compared to conventional solid-state lenses. This review summarizes recent progress from an application-oriented perspective, focusing on actuation mechanisms, fabrication strategies, and functional performance. Among actuation mechanisms, electric-field-driven approaches are highlighted, including electrowetting for shape tuning and liquid crystal-based refractive-index tuning techniques. The former excels in tuning range and response speed, whereas the latter enables programmable wavefront control with lower optical aberrations but limited efficiency. Notably, double-emulsion configurations, with fast interfacial actuation and inherent structural stability, demonstrate great potential for highly integrated optical components. Fabrication methodologies—including semiconductor-derived processes, additive manufacturing, and dynamic molding—are evaluated, revealing trade-offs among scalability, structural complexity, and cost. Functionally, advances in focal length tuning, field-of-view expansion, depth-of-field extension, and aberration correction have been achieved, though strong coupling among these parameters still constrains system-level performance. Looking forward, innovations in functional materials, hybrid fabrication, and computational imaging are expected to mitigate these constraints. These developments will accelerate applications in microscopy, endoscopy, AR/VR displays, industrial inspection, and machine vision, while paving the way for intelligent photonic systems that integrate adaptive optics with machine learning for real-time control.

L. Leonforte, X. Sun, D. Valenti et al.

We present a general framework to tackle quantum optics problems with giant atoms, i.e. quantum emitters each coupled non-locally to a structured photonic bath (typically a lattice) of any dimension. The theory encompasses the calculation and general properties of Green’s functions, atom-photon bound states, collective master equations and decoherence-free Hamiltonians (DFHs), and is underpinned by a formalism where a giant atom is formally viewed as a normal atom lying at a fictitious location. As a major application, we provide for the first time a general criterion to predict/engineer DFHs of giant atoms, which can be applied both in and out of the photonic continuum and regardless of the structure or dimensionality of the photonic bath. This is used to show novel DFHs in 2D baths such as a square lattice, photonic graphene and an extended photonic Lieb lattice.

Binxiong Pan, Baoju Wang, Yue Ni et al.

Abstract Deterministic three-dimensional (3D) super-resolution microscopy can achieve light-matter interaction in a small volume, but usually with the axial extension distinctly more elongated than the lateral one. The isoSTED method combining two opposing objectives and multiple laser beams can offer high axial extension at λ/12 level, but at the cost of optical system complexity and inherent sidelobes. The high-order nonlinear effect by multiphoton excitation would benefit to achieve a sub-diffraction resolution as well as to suppress the sidelobes. Herein, to achieve an easy-to-use, sidelobe-free deterministic 3D nanoscopy with high axial resolution, we developed a purely physical deterministic strategy (UNEx-4Pi) by fusion of ultrahighly nonlinear excitation (UNEx) of photon avalanching nanoparticles and mirror-based bifocal vector field modulation (4Pi). The theoretical studies of UNEx-4Pi concept showed that the main peak of fluorescence spot became sharper and its large sidelobe height was suppressed with the increasing optical nonlinearity. In addition, the simplicity and robustness of UNEx-4Pi system were demonstrated utilizing a mirror-assisted single-objective bifocal self-interference strategy. Experimentally, UNEx-4Pi realized an extremely constringent focal spot without sidelobes observed, achieving an axial resolution up to λ/33 (26 nm) using one low-power CW beam. We also demonstrated the super-resolution ability of the UNEx-4Pi scheme to bioimaging and nuclear envelope of BSC-1 cells were stained and imaged at an axial resolution of 32 nm. The proposed UNEx-4Pi method will pave the way for achieving light-matter interaction in a highly confined space, thereby advancing cutting-edge technologies like deterministic super-resolution sensing, imaging, lithography, and data storage.

Nathan K. Long, Benjamin P. Dix-Matthews, Alex Frost et al.

In coherent optical communication across turbulent atmospheric channels, reference beacons can be multiplexed with information-encoded signals during transmission. In this case, it is commonly assumed that the wavefront distortion of the two is equivalent. In contrast to this assumption, we present experimental evidence of relative wavefront errors (WFEs) between polarization-multiplexed reference beacons and signals, after passing through a 2.4 km atmospheric link. We develop machine learning-based wavefront correction algorithms to compensate for observed WFEs, via phase retrieval, resulting in up to a 2/3 reduction in the relative phase error variance. Further, we analyze the excess noise contributions from relative WFEs in the context of continuous-variable quantum key distribution (CV-QKD), where our findings suggest that if future CV-QKD implementations employ wavefront correction algorithms similar to those reported here, an order of magnitude increase in secure key rates may be forthcoming.

Jonathan Christie, James R. Henderson, Edward W. Snedden et al.

We present a two-colour fully fibre-coupled balanced optical cross-correlator (BOXC) based on sum-frequency generation (SFG) between 1560 nm and 800 nm laser pulses using waveguides implemented in type-0 phase-matched periodically poled LiNbO$_{3}$ (PPLN) crystals. The interaction has an effective nonlinear coefficient of $d_{eff}$ = 16.1 pm/V, many times higher than comparable nonlinear crystals used for this SFG interaction such as barium borate (BBO). The resulting sensitivity of the cross-correlator is measured to be 5.11 mV/fs, five times greater than current bulk-optic BOXCs after accounting for differences in transimpedance gain and photodetector responsivity, with the potential for significantly higher sensitivity after optimisations to the cross-correlator design.

M. Vazimali, S. Fathpour

Abstract. Photonics on thin-film lithium niobate (TFLN) has emerged as one of the most pursued disciplines within integrated optics. Ultracompact and low-loss optical waveguides and related devices on this modern material platform have rejuvenated the traditional and commercial applications of lithium niobate for optical modulators based on the electro-optic effect, as well as optical wavelength converters based on second-order nonlinear effects, e.g., second-harmonic, sum-, and difference-frequency generations. TFLN has also created vast opportunities for applications and integrated solutions for optical parametric amplification and oscillation, cascaded nonlinear effects, such as low-harmonic generation; third-order nonlinear effects, such as supercontinuum generation; optical frequency comb generation and stabilization; and nonclassical nonlinear effects, such as spontaneous parametric downconversion for quantum optics. Recent progress in nonlinear integrated photonics on TFLN for all these applications, their current trends, and future opportunities and challenges are reviewed.

P. Vabishchevich, Y. Kivshar

Nonlinear optics is a well-established field of research that traditionally relies on the interaction of light with macroscopic nonlinear media over distances significantly greater than the wavelength of light. However, the recently emerged field of optical metasurfaces provides a novel platform for studying nonlinear phenomena in planar geometries. Nonlinear optical metasurfaces introduce new functionalities to the field of nonlinear optics extending them beyond perturbative regimes of harmonic generation and parametric frequency conversion, being driven by mode-matching, resonances, and relaxed phase-matching conditions. Here we review the very recent advances in the rapidly developing field of nonlinear metasurface photonics, emphasizing multi-frequency and cascading effects, asymmetric and chiral frequency conversion, nonperturbative nonlinear regimes, and nonlinear quantum photonics, empowered by the physics of Mie resonances and optical bound states in the continuum.