Probing 10 μK stability and residual drifts in the cross-polarized dual-mode stabilization of single-crystal ultrahigh-Q optical resonators

Evaluation of 10 current image reconstruction algorithms for linear array photoacoustic imaging

Various reconstruction algorithms have been implemented for linear array photoacoustic imaging systems with the goal of accurately reconstructing the strength absorbers within the tissue being imaged. Since the existing algorithms have been introduced by different research groups and the context of performance evaluation was not consistent, it is difficult to make a fair comparison between them. In this study, we systematically compared the performance of 10 published image reconstruction algorithms (DAS, UBP, pDAS, DMAS, MV, EIGMV, SLSC, GSC, TR, and FD) using in-vitro phantom data. Evaluations were conducted based on lateral resolution of the reconstructed images, computational time, target detectability, and noise sensitivity. We anticipate the outcome of this study will assist researchers in selecting appropriate algorithms for their linear array PA imaging applications.

AI-driven projection tomography with multicore fibre-optic cell rotation

Optical tomography has emerged as a non-invasive imaging method, providing three-dimensional insights into subcellular structures and thereby enabling a deeper understanding of cellular functions, interactions, and processes. Conventional optical tomography methods are constrained by a limited illumination scanning range, leading to anisotropic resolution and incomplete imaging of cellular structures. To overcome this problem, we employ a compact multi-core fibre-optic cell rotator system that facilitates precise optical manipulation of cells within a microfluidic chip, achieving full-angle projection tomography with isotropic resolution. Moreover, we demonstrate an AI-driven tomographic reconstruction workflow, which can be a paradigm shift from conventional computational methods, often demanding manual processing, to a fully autonomous process. The performance of the proposed cell rotation tomography approach is validated through the three-dimensional reconstruction of cell phantoms and HL60 human cancer cells. The versatility of this learning-based tomographic reconstruction workflow paves the way for its broad application across diverse tomographic imaging modalities, including but not limited to flow cytometry tomography and acoustic rotation tomography. Therefore, this AI-driven approach can propel advancements in cell biology, aiding in the inception of pioneering therapeutics, and augmenting early-stage cancer diagnostics.

IRS-assisted vehicular visible light communications systems: channel modeling and performance analysis

Visible light communications (VLC) is a promising solution as an alternative for the fully occupied radio frequency bands in the near future. The rear (tail) and front of vehicles have lamps that can be used for vehicular visible light communications (VVLC) systems. However, one of the main challenges of VLC systems is the line-of-sight (LoS) blockage issue. In this paper, we propose the installation of intelligent reflecting surfaces (IRSs) (i.e., smart mirrors) on the back of vehicles to overcome the issue in VVLC systems. We assume three different patterns of angular distribution for the radiation intensity: a commercially available LED with an asymmetrical pattern (Philips Luxeon Rebel), a symmetrical Lambertian pattern, and an asymmetrical Gaussian pattern. In the first section of this paper, we obtain the channel model for the IRS-assisted VVLC systems, then we investigate the path loss results versus link distance under different conditions such as weather type (clear, rainy, moderate fog, and thick fog) and radiation patterns. Moreover, the impact of system parameters such as the aperture size of the photodetector (PD), side-to-side and front-to-front distances, the number of IRS elements, and the IRS area are studied. In the second part, we derive a closed-form expression for the maximum achievable link distance versus the probability of error for the IRS-assisted VVLC systems. In addition, in this section we analyze the impact of the parameters in a single-photon avalanche diode (SPAD), background noise, and the system parameters for the path loss.

Training large-scale optoelectronic neural networks with dual-neuron optical-artificial learning

Optoelectronic neural networks (ONN) are a promising avenue in AI computing due to their potential for parallelization, power efficiency, and speed. Diffractive neural networks, which process information by propagating encoded light through trained optical elements, have garnered interest. However, training large-scale diffractive networks faces challenges due to the computational and memory costs of optical diffraction modeling. Here, we present DANTE, a dual-neuron optical-artificial learning architecture. Optical neurons model the optical diffraction, while artificial neurons approximate the intensive optical-diffraction computations with lightweight functions. DANTE also improves convergence by employing iterative global artificial-learning steps and local optical-learning steps. In simulation experiments, DANTE successfully trains large-scale ONNs with 150 million neurons on ImageNet, previously unattainable, and accelerates training speeds significantly on the CIFAR-10 benchmark compared to single-neuron learning. In physical experiments, we develop a two-layer ONN system based on DANTE, which can effectively extract features to improve the classification of natural images.

Label-Free Intracellular Multi-Specificity in Yeast Cells by Phase-Contrast Tomographic Flow Cytometry

In-flow phase-contrast tomography provides a 3D refractive index of label-free cells in cytometry systems. Its major limitation, as with any quantitative phase imaging approach, is the lack of specificity compared to fluorescence microscopy, thus restraining its huge potentialities in single-cell analysis and diagnostics. Remarkable results in introducing specificity are obtained through artificial intelligence (AI), but only for adherent cells. However, accessing the 3D fluorescence ground truth and obtaining accurate voxel-level co-registration of image pairs for AI training is not viable for high-throughput cytometry. The recent statistical inference approach is a significant step forward for label-free specificity but remains limited to cells' nuclei. Here, a generalized computational strategy based on a self-consistent statistical inference to achieve intracellular multi-specificity is shown. Various subcellular compartments (i.e., nuclei, cytoplasmic vacuoles, the peri-vacuolar membrane area, cytoplasm, vacuole-nucleus contact site) can be identified and characterized quantitatively at different phases of the cells life cycle by using yeast cells as a biological model. Moreover, for the first time, virtual reality is introduced for handling the information content of multi-specificity in single cells. Full fruition is proofed for exploring and interacting with 3D quantitative biophysical parameters of the identified compartments on demand, thus opening the route to a metaverse for 3D microscopy.

Augmenting cyan emission in vanadate garnets via Dy3+activation for light emitting devices and multi-mode optical thermometry

Developing cyan-based phosphors is inevitable to bridge the cyan gap to generate white light with a high color rendering index. Herein, the blue-green emission from the VO43- center in the Sr2NaMg2V3O12 host is augmented via activating with Dy3+ ions. Dual emission from the Sr2NaMg2V3O12:Dy3+ system under 335 nm excitation is due to the energy transfer from VO43- to the Dy3+ center. An increase in the ratio of yellow to blue bands (Y/B) is noted with the increase in the concentration of Dy3+. Apart from the higher activation energy of 0.41 eV, excellent color stability with a small thermochromic shift is noted at elevated temperatures. The light emitting device fabricated based on the Sr2NaMg2V3O12:Dy3+ phosphor presents bright cyan emission with CIE coordinates of (0.250, 0.352), a CCT of 9826 K, and a CRI of 53 with stable emission even at higher input currents. The application of Sr2NaMg2V3O12:Dy3+ in multi-mode temperature sensing is also discussed. The maximum relative temperature sensitivity of 1.32, 0.41, and 1.9% K-1 at 380, 300, and 460 K is obtained for the fluorescence, fluorescence intensity ratio and excitation intensity ratio methods. Thus, the present work details the capability of Dy3+-based vanadate garnet phosphors for solid-state lighting and temperature sensing.

Dielectric chiral metasurfaces for enhanced circular dichroism spectroscopy at near infrared regime

Numerous applications of chiro-optical effects can be found in nanophotonics, including imaging and spin-selective absorption, particularly in sensing for separating and detecting chiral enantiomers. Flat single-layer metasurfaces composed of chiral or achiral sub-wavelength structures offer unique properties to manipulate the light due to their extraordinary light-matter interaction. However, at optical wavelengths, the generation of strong chirality is found to be challenging via conventional chiral metasurface approaches. This work intends to design and optimize a dielectric chiral meta-nano-surface based on a diatomic design strategy to comprehend giant chiro-optical effects in the near-infrared (NIR) regime for potential application in circular dichroism (CD) spectroscopy. Instead of using a single chiral structure that limits the CD value at optical wavelengths, the proposed metasurface used a diatomic (two meta-atoms with distinct geometric parameters) chiral structure as a building block to significantly enhance the chiro-optical effect. Combining both meta-atoms in a single periodicity of the building block introduces constructive and destructive interferences to attain the maximum circular dichroism value exceeding 75%. Moreover, using multipolar resonance theory, the physics behind the generation of giant chiro-optical effects have also been investigated. The proposed dielectric chiral meta-platform based on the extra degree of freedom can find application in compact integrated optical setups for CD spectroscopy, enantiomer separation and detection, spin-dependent color filters, and beam splitters.

Panoramic volumetric clinical handheld photoacoustic and ultrasound imaging

Photoacoustic (PA) imaging has gained much attention, providing structural and functional information in combination with clinical ultrasound (US) imaging systems. 2D PA and US imaging is easily implemented, but its heavy dependence on operator skills makes 3D imaging preferable. In this study, we propose a panoramic volumetric clinical PA and US imaging system equipping a handheld imaging scanner weighing 600 g and measuring 70 × 62 × 110 mm³. Multiple PA/US scans were performed to cover a large field-of-view (FOV), and the acquired PA/US volumes were mosaic-stitched after manually correcting the positions and rotations in a total of 6 degrees of freedom. PA and US maximum amplitude projection images were visualized online, while spectral unmixed data was quantified offline. The performance of the system was tested via tissue-mimicking phantom experiments. The system's potential was confirmed in vivo by panoramically imaging vascular networks in human arms and necks, with FOVs of 331 × 38 and 129 × 120 mm², respectively. Further, we quantified hemoglobin oxygen saturation levels in the radial artery, brachial artery, carotid artery, and jugular vein. We hope that this system can be applied for various clinical fields such as cardiovascular imaging, dermatology, vascular surgery, internal medicine, and oncology.

Memristor-based neural networks: a bridge from device to artificial intelligence

Since the beginning of the 21st century, there is no doubt that the importance of artificial intelligence has been highlighted in many fields, among which the memristor-based artificial neural network technology is expected to break through the limitation of von Neumann so as to realize the replication of the human brain by enabling strong parallel computing ability and efficient data processing and become an important way towards the next generation of artificial intelligence. A new type of nanodevice, namely memristor, which is based on the variability of its resistance value, not only has very important applications in nonvolatile information storage, but also presents obsessive progressiveness in highly integrated circuits, making it one of the most promising circuit components in the post-Moore era. In particular, memristors can effectively simulate neural synapses and build neural networks; thus, they can be applied for the preparation of various artificial intelligence systems. This study reviews the research progress of memristors in artificial neural networks in detail and highlights the structural advantages and frontier applications of neural networks based on memristors. Finally, some urgent problems and challenges in current research are summarized and corresponding solutions and future development trends are put forward.

A Review of Photonic Sensors Based on Ring Resonator Structures: Three Widely Used Platforms and Implications of Sensing Applications

Optical ring resonators (RRs) are a novel sensing device that has recently been developed for several sensing applications. In this review, RR structures based on three widely explored platforms, namely silicon-on-insulator (SOI), polymers, and plasmonics, are reviewed. The adaptability of these platforms allows for compatibility with different fabrication processes and integration with other photonic components, providing flexibility in designing and implementing various photonic devices and systems. Optical RRs are typically small, making them suitable for integration into compact photonic circuits. Their compactness allows for high device density and integration with other optical components, enabling complex and multifunctional photonic systems. RR devices realized on the plasmonic platform are highly attractive, as they offer extremely high sensitivity and a small footprint. However, the biggest challenge to overcome is the high fabrication demand related to such nanoscale devices, which limits their commercialization.

Observation of Anomalous Orbital Angular Momentum Transfer in Parametric Nonlinearity

Orbital angular momentum (OAM) conservation plays an important role in shaping and controlling structured light with nonlinear optics. The OAM of a beam originating from three-wave mixing should be the sum or difference of the other two inputs because no light-matter OAM exchange occurs in parametric nonlinear interactions. Here, we report anomalous OAM transfer in parametric upconversion, in which a Hermite-Gauss mode signal interacts with a specially engineered pump capable of astigmatic transformation, resulting in Laguerre-Gaussian mode sum-frequency generation (SFG). The anomaly here refers to the fact that the pump and signal both carry no net OAM, while their SFG does. We reveal experimentally that there is also an OAM inflow to the residual pump, having the same amount of that to the SFG but with the opposite sign, and thus holds system OAM conservation. This unexpected OAM selection rule improves our understanding of OAM transfer among interacting waves and may inspire new ideas for controlling OAM states via nonlinear optics.

Grating waveguides by machine learning for augmented reality

We propose a machine-learning-based method for grating waveguides and augmented reality, significantly reducing the computation time compared with existing finite-element-based numerical simulation methods. Among the slanted, coated, interlayer, twin-pillar, U-shaped, and hybrid structure gratings, we exploit structural parameters such as grating slanted angle, grating depth, duty cycle, coating ratio, and interlayer thickness to construct the gratings. The multi-layer perceptron algorithm based on the Keras framework was used with a dataset comprised of 3000-14,000 samples. The training accuracy reached a coefficient of determination of more than 99.9% and an average absolute percentage error of 0.5%-2%. At the same time, the hybrid structure grating we built achieved a diffraction efficiency of 94.21% and a uniformity of 93.99%. This hybrid structure grating also achieved the best results in tolerance analysis. The high-efficiency artificial intelligence waveguide method proposed in this paper realizes the optimal design of a high-efficiency grating waveguide structure. It can provide theoretical guidance and technical reference for optical design based on artificial intelligence.

3D reconstruction of unstained weakly scattering cells from a single defocused hologram

We investigate the problem of 3D complex field reconstruction corresponding to unstained red blood cells (RBCs) with a single defocused off-axis digital hologram. The main challenge in this problem is the localization of cells to the correct axial range. While investigating the volume recovery problem for a continuous phase object like the RBC, we observe an interesting feature of the backpropagated field that it does not show a clear focusing effect. Therefore, sparsity enforcement within the iterative optimization framework using a single hologram data frame cannot effectively restrict the reconstruction to the true object volume. For phase objects, it is known that the amplitude contrast of the backpropagated object field at the focus plane is minimum. We use this information available in the recovered object field in the hologram plane to device depth-dependent weights that are proportional to the inverse of amplitude contrast. This weight function is employed in the iterative steps of the optimization algorithm to assist the object volume localization. The overall reconstruction process is performed using the mean gradient descent (MGD) framework. Experimental illustrations of 3D volume reconstruction of the healthy as well as malaria-infected RBCs are presented. A test sample of polystyrene microsphere bead is also used to validate the axial localization capability of the proposed iterative technique. The proposed methodology is simple to implement experimentally and provides an approximate tomographic solution, which is axially restricted and consistent with the object field data.

A highly efficient broadband multi-functional metaplate

Due to the considerable potential of ultra-compact and highly integrated meta-optics, multi-functional metasurfaces have attracted great attention. The mergence of nanoimprinting and holography is one of the fascinating study areas for image display and information masking in meta-devices. However, existing methods rely on layering and enclosing, where many resonators combine various functions effectively at the expense of efficiency, design complication, and complex fabrication. To overcome these limitations, a novel technique for a tri-operational metasurface has been suggested by merging PB phase-based helicity-multiplexing and Malus's law of intensity modulation. To the best of our knowledge, this technique resolves the extreme-mapping issue in a single-sized scheme without increasing the complexity of the nanostructures. For proof of concept, a multi-functional metasurface built of single-sized zinc sulfide (ZnS) nanobricks is developed to demonstrate the viability of simultaneous control of near and far-field operations. The proposed metasurface successfully verifies the implementation of a multi-functional design strategy with conventional single-resonator geometry by reproducing two high-fidelity images in the far field and projecting one nanoimprinting image in the near field. This makes the proposed information multiplexing technique a potential candidate for many high-end and multi-fold optical storage, information-switching, and anti-counterfeiting applications.

Optofluidic imaging meets deep learning: from merging to emerging

Propelled by the striking advances in optical microscopy and deep learning (DL), the role of imaging in lab-on-a-chip has dramatically been transformed from a silo inspection tool to a quantitative "smart" engine. A suite of advanced optical microscopes now enables imaging over a range of spatial scales (from molecules to organisms) and temporal window (from microseconds to hours). On the other hand, the staggering diversity of DL algorithms has revolutionized image processing and analysis at the scale and complexity that were once inconceivable. Recognizing these exciting but overwhelming developments, we provide a timely review of their latest trends in the context of lab-on-a-chip imaging, or coined optofluidic imaging. More importantly, here we discuss the strengths and caveats of how to adopt, reinvent, and integrate these imaging techniques and DL algorithms in order to tailor different lab-on-a-chip applications. In particular, we highlight three areas where the latest advances in lab-on-a-chip imaging and DL can form unique synergisms: image formation, image analytics and intelligent image-guided autonomous lab-on-a-chip. Despite the on-going challenges, we anticipate that they will represent the next frontiers in lab-on-a-chip imaging that will spearhead new capabilities in advancing analytical chemistry research, accelerating biological discovery, and empowering new intelligent clinical applications.

Development of NaY9Si6O26:Yb3+ phosphors with high thermal stability for NIR anti-counterfeiting: study of its crystal structure and luminescent properties

Near-infrared (NIR) radiation has generated considerable industrial and research interest. However, NIR phosphors for this are limited by low quantum efficiency and broad spectra. Rare-earth-containing compounds doped with activators as host systems for NIR phosphors may resolve these limitations. Yb³⁺-doped NaY₉Si₆O₂₆ phosphors were synthesized using a conventional solid-state reaction method. The main phase of the synthesized phosphor samples exhibited a hexagonal structure NaY₉Si₆O₂₆ phase, and had an angular-shape with an average grain size of 1-3 μm. The NaY₉Si₆O₂₆:Yb³⁺ phosphors showed a near-infrared emission from 950 to 1100 nm, which was attributed to the ²F_5/2 → ²F_7/2 transition of Yb³⁺ ions under 270 and 920 nm excitation. The excitation spectra, recorded by monitoring the emission at 985 nm, showed two bands in the ultraviolet and infrared regions, which correspond to the charge transfer transition and the ²F_7/2 → ²F_5/2 transition of Yb³⁺ ions. At 300 °C, the emission intensity of the NaY₉Si₆O₂₆:Yb³⁺ phosphor remained constant at 82%. Furthermore, the thermal degradation was negligible after cooling, suggesting the possibility of application in advanced anti-counterfeiting applications.

Multiple-scattering simulator-trained neural network for intensity diffraction tomography

Recovering 3D phase features of complex biological samples traditionally sacrifices computational efficiency and processing time for physical model accuracy and reconstruction quality. Here, we overcome this challenge using an approximant-guided deep learning framework in a high-speed intensity diffraction tomography system. Applying a physics model simulator-based learning strategy trained entirely on natural image datasets, we show our network can robustly reconstruct complex 3D biological samples. To achieve highly efficient training and prediction, we implement a lightweight 2D network structure that utilizes a multi-channel input for encoding the axial information. We demonstrate this framework on experimental measurements of weakly scattering epithelial buccal cells and strongly scattering C. elegans worms. We benchmark the network's performance against a state-of-the-art multiple-scattering model-based iterative reconstruction algorithm. We highlight the network's robustness by reconstructing dynamic samples from a living worm video. We further emphasize the network's generalization capabilities by recovering algae samples imaged from different experimental setups. To assess the prediction quality, we develop a quantitative evaluation metric to show that our predictions are consistent with both multiple-scattering physics and experimental measurements.

Diffraction of Gaussian and Laguerre-Gauss beams from a circular aperture using the moment expansion method

A method based on the distribution theory is introduced to compute the Fresnel diffraction integral. It is applied to the diffraction of Gaussian and Laguerre-Gauss beams by a circular aperture. Expressions of the diffracting field are recast into a perturbation series describing the near- and far-field regions.

Simplified superoscillatory lenses for super-resolution imaging

In recent years, superoscillations have become a new method for creating super-resolution imaging systems. The design of superoscillatory wavefronts and their corresponding lenses can, however, be a complicated process. In this study, we extend a recently developed method for designing complex superoscillatory filters to the creation of phase- and amplitude-only filters and compare their performance. These three types of filters can generate nearly identical superoscillatory fields at the image plane.

Wavelength-division multiplexing communications using integrated soliton microcomb laser source

In this Letter, we report an investigation of the feasibility and performance of wavelength-division multiplexed (WDM) optical communications using an integrated perfect soliton crystal as the multi-channel laser source. First, we confirm that perfect soliton crystals pumped directly by a distributed-feedback (DFB) laser self-injection locked to the host microcavity has sufficiently low frequency and amplitude noise to encode advanced data formats. Second, perfect soliton crystals are exploited to boost the power level of each microcomb line, so that it can be directly used for data modulation, excluding preamplification. Third, in a proof-of-concept experiment, we demonstrate seven-channel 16-quadrature amplitude modulation (16-QAM) and 4-level pulse amplitude modulation (PAM4) data transmissions using an integrated perfect soliton crystal as the laser carrier; excellent data receiving performance is obtained for various fiber link distances and amplifier configurations. Our study reveals that fully integrated Kerr soliton microcombs are viable and advantageous for optical data communications.

High-fidelity optical diffraction tomography of live organisms using iodixanol refractive index matching

Optical diffraction tomography (ODT) enables the three-dimensional (3D) refractive index (RI) reconstruction. However, when the RI difference between a sample and a medium increases, the effects of light scattering become significant, preventing the acquisition of high-quality and accurate RI reconstructions. Herein, we present a method for high-fidelity ODT by introducing non-toxic RI matching media. Optimally reducing the RI contrast enhances the fidelity and accuracy of 3D RI reconstruction, enabling visualization of the morphology and intra-organization of live biological samples without producing toxic effects. We validate our method using various biological organisms, including C. albicans and C. elegans.

Numerical refractive index correction for the stitching procedure in tomographic quantitative phase imaging

Tomographic quantitative phase imaging (QPI) lacks an absolute refractive index value baseline, which poses a problem when large dense objects extending over multiple fields of view are measured volume by volume and stitched together. Some of the measurements lack the natural baseline value that is provided by the mounting medium with a known refractive index. In this work, we discuss the problem of the refractive index (RI) baseline of individual reconstructed volumes that are deprived of access to mounting medium due to the extent of the object. The solution of this problem is provided by establishing the RI offsets based on the overlapping regions. We have proven that the process of finding the offset RI values may be justifiably reduced to the analogous procedure in the 2D baseline correction (2D-BC). Finally, we proposed the enhancement of the state-of-the-art 2D-BC procedure previously introduced in the context of 2D QPI. The processing is validated at the examples of a synthetic dataset and a liver organoid.

Touch-Programmable Metasurface for Various Electromagnetic Manipulations and Encryptions

Previous programmable metasurfaces integrated with diodes or varactors require external instructions for field programmable gate arrays (FPGAs), which usually rely on computer-inputs or pre-loaded algorithms. But the complicated external devices make the coding regulation process of the programmable metasurfaces cumbersome and difficult to use. To simplify the process and provide a new interaction manner, a touch-programmable metasurface (TPM) based on touch sensing modules is proposed to realize various electromagnetic (EM) manipulations and encryptions. By simply touching the meta-units of the TPM, the state of the diodes can be changed. Through the touch controls, the TPM can achieve independent and direct manipulations of meta-units and efficient inputs of coding patterns without using a FPGA or other control modules. Various coding patterns are demonstrated to achieve diverse scattering-field control and flexible near-field EM information encryptions, which verifies the feasibility of the TPM design. The presented TPM will have wide application prospects in imaging displays, wireless communications, and EM information encryptions.

Theoretical study of freely propagating high-spatial-frequency optical waves

When it comes to the high-spatial-frequency electromagnetic waves, we usually think of them as the evanescent waves which are bounded at the near-field surface and decay along with propagation distance. A conventional wisdom tells us that the high-spatial-frequency waves cannot exist in the far field. In this work, we show, however, that these high-spatial-frequency waves having wavenumbers larger than the incident one can propagate freely to the far-field regions. We demonstrate theoretically a technique, based on an abrupt truncation of the incident plane wave, to generate these intriguing waves. The truncation functions describing the slit and the complementary slit are considered as typical examples. Our results show that both the slit structures are able to produce the high-spatial-frequency wave phenomena in the far field, manifested by their interference fringes of the diffracted waves. This work introduces the high-spatial-frequency propagating waves. Therefore, it may trigger potential investigations on such an interesting subject, e.g., one may design delicate experiment to confirm this prediction. Besides, it would stimulate potential applications such as in superresolution and precise measurement.

Ultra-wideband antireflection assisted by a continuously varying temporal medium

We demonstrate that reflectionless propagation of electromagnetic waves between two different materials can be achieved by designing an intermediate temporal medium, which can work in an ultra-wide frequency band. Such a temporal medium is designed with consideration of a multi-stage variation of the material's permittivity in the time domain. The multi-stage temporal permittivity is formed by a cascaded quarter-wave temporal coating, which is an extension of the antireflection temporal coating by Pacheco-Peña et al. [Optica7, 323 (2020)10.1364/OPTICA.381175]. The strategy to render ultra-wideband antireflection temporal medium is discussed analytically and verified numerically. In-depth analysis shows that the multi-stage design of the temporal media implies a continuously temporal variation of the material's constitutive parameters, thus an ultra-wideband antireflection temporal medium is reasonably obtained. As an illustrative example for application, the proposed temporal medium is adopted to realize impedance matching between a dielectric slab and free space, which validates our new findings.

Addressing the imaging limitations of a microsphere-assisted nanoscope

In the past decade, microsphere-assisted nanoscopy has been developed rapidly to overcome the diffraction limit. However, due to the limited size and high surface curvature of microspheres, the magnified imaging still suffers from problems like limited view scope, imaging distortion, and low contrast. In this paper, we specialize in the imaging mechanism of microspheres and find irradiance as the key factor for microsphere imaging quality. Utilizing a modified optical tweezer system, we achieve precise manipulation of microspheres and further propose a high-quality large-field magnified imaging scheme. The results show that the imaging area of 5 µm microspheres can reach 16×12 µm² with the minimum identifiable feature of 137 nm. This scheme provides a new solution for extending the measuring scope of microsphere-assisted nanoscope, and will certainly promote the application of this technology in practice.

Nonseparable Polarization Wavefront Transformation

In this Letter, we investigate a new class of polarization wave front transformations which exhibit nonconventional far field interference behavior. We show that these can be realized by double-layer metasurfaces, which overcome the intrinsic limitations of single-layer metasurfaces. Holograms that encode four or more distinct patterns in nonorthogonal polarization states are theoretically demonstrated. This Letter clarifies and expands the possibilities enabled by a broad range of technologies which can spatially modulate light's polarization state and, for metasurfaces specifically, rigorously establishes when double-layer metasurfaces are-and are not-required.

Enhancing the efficiency of graphene-based THz modulator by optimizing the Brewster angle

The gate-controllable electronical property of graphene provides a possibility of active tuning property for THz modulator. However, the common modulation technology which only depends on voltage cannot solve the problem of power consumption limitation in communication applications. Here, we demonstrated a Brewster angle-controlled graphene-based THz modulator, which could achieve a relatively high modulation depth with low voltage. First, we explored the complex relationships among the Brewster angles, reflection coefficients and the conductivities of graphene. Then, we further investigated the optimal incident angle selection based on the unusual reflection effect which occurs at Brewster angle. Finally, an improved scheme by dynamically adjusting the incident angle was proposed in this paper. It would make it possible that the modulator could achieve a modulation depth of more than 90% with a Fermi level as low as 0.2eV at any specific frequency in the range of 0.4THz-2.2THz. This research will help to realize a THz modulator with high-performance and ultra-low-power in quantities of applications, such as sensing and communication.

Unified treatment of exact and approximate scalar electromagnetic wave scattering

Under conditions of strong scattering, a dilemma often arises regarding the best numerical method to use. Main competitors are the Born series, the beam propagation method, and direct solution of the Lippmann-Schwinger equation. However, analytical relationships between the three methods have not yet, to our knowledge, been explicitly stated. Here, we bridge this gap in the literature. In addition to overall insight about aspects of optical scattering that are best numerically captured by each method, our approach allows us to derive approximate error bounds to be expected under various scattering conditions.

Deep learning alignment of bidirectional raster scanning in high speed photoacoustic microscopy

Simultaneous point-by-point raster scanning of optical and acoustic beams has been widely adapted to high-speed photoacoustic microscopy (PAM) using a water-immersible microelectromechanical system or galvanometer scanner. However, when using high-speed water-immersible scanners, the two consecutively acquired bidirectional PAM images are misaligned with each other because of unstable performance, which causes a non-uniform time interval between scanning points. Therefore, only one unidirectionally acquired image is typically used; consequently, the imaging speed is reduced by half. Here, we demonstrate a scanning framework based on a deep neural network (DNN) to correct misaligned PAM images acquired via bidirectional raster scanning. The proposed method doubles the imaging speed compared to that of conventional methods by aligning nonlinear mismatched cross-sectional B-scan photoacoustic images during bidirectional raster scanning. Our DNN-assisted raster scanning framework can further potentially be applied to other raster scanning-based biomedical imaging tools, such as optical coherence tomography, ultrasound microscopy, and confocal microscopy.

Metal Electrode-Free Halide Perovskite-Based Flexible Ultraviolet-C Photodetector with Large Area

Ultraviolet-C (UVC) photodetector has appealed to a numerous number of research owing to its manifold applications in wireless communication, flame monitoring, and medicine. However, in addition to superior performance and high stability of recent studies, scalability and production cost are important factors for commercialization and practical implementation. In this study, a halide perovskite-based UVC photodetector was fabricated using spin-coating process and low-temperature annealing. Corning® Willow® Glass was selected as the substrate for the bottom-illuminated device due to its flexibility and exceptional optical transmission (approximately 60%) in the deep-UV region. The device had a vertical structure with a large active area (1 cm²) owing to the judicious utilization of electrodes. Under bent state with a curvature radius of 25 mm, the as-fabricated device exhibited high response and repeatability with an on/off ratio of 9.57 × 10³, a fast response speed of 45/46 ms (rise/fall times) at zero bias under the illumination of a 254-nm UV lamp. The results are based on a flexible and lightweight photodetector without the utilization of notable metal electrodes.

Phase noise of Kerr soliton dual microcombs

Dissipative Kerr soliton microcombs are believed to be a promising technique to build a dual-comb source for applications including precision laser metrology, fast laser spectroscopy, and high-speed optical signal processing. In this Letter, we conduct a detailed experimental investigation on the phase coherence between two on-chip Kerr soliton microcombs, where the underlying physical and technical origins that lead to the mutual phase noise between microcombs are analyzed. Moreover, the techniques of 2-point locking and optical frequency division are explored to enhance the dual-microcomb phase coherence, and we demonstrate the best phase noise down to -50 dBc/Hz at 1-Hz offset, -90 dBc/Hz at 1-kHz offset, and -120 dBc/Hz at 1-MHz offset. Our study provides a basic reference for both fundamental studies and practical applications of Kerr soliton dual microcombs that entail high mutual phase coherence.

Reflective metalens with an enhanced off-axis focusing performance

Metalenses are one of the most promising metasurface applications. However, all-dielectric reflective metalenses are rarely studied, especially regarding their off-axis focusing performance. After experimentally studying the material optical properties in this work, we propose reflective metalens based on titanium dioxide (TiO₂) and silicon dioxide (SiO₂), which operate at a visible wavelength of 0.633 µm. Unlike conventional reflective metalenses based on metallic mirrors, the proposed device was designed based on a modified parabolic phase profile and was integrated onto a dielectric distributed Bragg reflector periodic structure to achieve high reflectivity with five dielectric pairs. The focusing efficiency characteristics of the metalens were experimentally studied for beam angles of incidence between 0^∘ and 30^∘. The results reveal that the focusing efficiency for the modified metalens design remains higher than 54%, which is higher than 50%, making it promising for photonic miniaturization and integration.

Target ischemic stroke model creation method using photoacoustic microscopy with simultaneous vessel monitoring and dynamic photothrombosis induction

The ischemic stroke animal model evaluates the efficacy of reperfusion and neuroprotective strategies for ischemic injuries. Various conventional methods have been reported to induce the ischemic models; however, controlling specific neurological deficits, mortality rates, and the extent of the infarction is difficult as the size of the affected region is not precisely controlled. In this paper, we report a single laser-based localized target ischemic stroke model development method by simultaneous vessel monitoring and photothrombosis induction using photoacoustic microscopy (PAM), which has minimized the infarct size at precise location with high reproducibility. The proposed method has significantly reduced the infarcted region by illuminating the precise localization. The reproducibility and validity of suggested method have been demonstrated through repeated experiments and histological analyses. These results demonstrate that our method can provide the ischemic stroke model closest to the clinical pathology for brain ischemia research from inducement, occurrence mechanisms to the recovery process.

Controlled generation of mode-switchable nanosecond pulsed vector vortex beams from a Q-switched fiber laser

We reported and demonstrated a ring Q-switched Ytterbium-doped fiber laser that can generate mode-switchable nanosecond pulsed vector vortex beams between two different orders. In the spatial optical path of the fiber laser, several cascaded Q-plates, divided into two Q-plate groups, are applied for intracavity mode conversion between LP₀₁ mode and vector vortex beams. In one Q-plate group, two quarter-wave plates are inserted to achieve the addition and subtraction of the order of Q-plates. By tuning the polarization state in the cavity, mode-switchable vector vortex beams (VVBs), including cylindrical vector beams (CVBs), elliptically polarized cylindrical vector beams (EPCVBs), and vortex beams, of two different orders can be generated on demand. The experimental results show that by using the group of 1st and 3rd orders Q-plates, the 2nd and 4th orders mode-switchable VVBs (vortex beams with topological charges of ±2, ±4, CVBs and EPCVBs of 2nd- and 4th-order) can be obtained from the fiber laser. The slope efficiency, pulse width, and repetition rate are 33.4%, 360 ns, and 241kHz respectively. To the best of our knowledge, this is the first time to realize the direct generation of mode-switchable VVBs on the arbitrary position of the higher-order Poincaré sphere between two different orders from a fiber laser. This work lays the foundation for the flexible generation of arbitrary modes of VVBs with multiple different orders in the laser cavity.

Tunable directional emission from electrically driven nano-strip metal-insulator-metal tunnel junctions

Electrically driven nanoantennas for on-chip generation and manipulation of light have attracted significant attention in recent times. Metal-insulator-metal (MIM) tunnel junctions have been extensively used to electrically excite surface plasmons and photons via inelastic electron tunneling. However, the dynamic switching of light from MIM junctions into spatially separate channels has not been shown. Here, we numerically demonstrate switchable, highly directional light emission from electrically driven nano-strip Ag-SiO₂-Ag tunnel junctions. The top electrode of our Ag-SiO₂-Ag stack is divided into 16 nano-strips, with two of the tunnel junctions at the centre (S _L and S _R) acting as sources. Using full-wave electromagnetic simulations, we show that when S _L is excited, the emission is highly directional with an angle of emission of -30° and an angular spread of ∼11°. When the excitation is switched to S _R, the emission is redirected to an angle of 30° with an identical angular spread. A directivity of 29.4 is achieved in the forward direction, with a forward-to-backward ratio of 12. We also demonstrate wavelength-selective directional switching by changing the width, and thereby the resonance wavelength, of the sources. The emission can be tuned by varying the periodicity of the structure, paving the way for electrically driven, reconfigurable light sources.

A multichannel optical computing architecture for advanced machine vision

Endowed with the superior computing speed and energy efficiency, optical neural networks (ONNs) have attracted ever-growing attention in recent years. Existing optical computing architectures are mainly single-channel due to the lack of advanced optical connection and interaction operators, solving simple tasks such as hand-written digit classification, saliency detection, etc. The limited computing capacity and scalability of single-channel ONNs restrict the optical implementation of advanced machine vision. Herein, we develop Monet: a multichannel optical neural network architecture for a universal multiple-input multiple-channel optical computing based on a novel projection-interference-prediction framework where the inter- and intra- channel connections are mapped to optical interference and diffraction. In our Monet, optical interference patterns are generated by projecting and interfering the multichannel inputs in a shared domain. These patterns encoding the correspondences together with feature embeddings are iteratively produced through the projection-interference process to predict the final output optically. For the first time, Monet validates that multichannel processing properties can be optically implemented with high-efficiency, enabling real-world intelligent multichannel-processing tasks solved via optical computing, including 3D/motion detections. Extensive experiments on different scenarios demonstrate the effectiveness of Monet in handling advanced machine vision tasks with comparative accuracy as the electronic counterparts yet achieving a ten-fold improvement in computing efficiency. For intelligent computing, the trends of dealing with real-world advanced tasks are irreversible. Breaking the capacity and scalability limitations of single-channel ONN and further exploring the multichannel processing potential of wave optics, we anticipate that the proposed technique will accelerate the development of more powerful optical AI as critical support for modern advanced machine vision.

Relative-phase simulated annealing for time-efficient and large-scale inverse design of achromatic thin lenses

High-efficiency, broadband, wafer-size, and ultra-thin lenses are highly demanded, due to its great potential in abundant applications such as compact imaging modules. It is usually conceived that this target might be attainable given the advancement in nanofabrication, computation power and emerging algorithms, though challenging. Here, we reveal the inconvenient truth that for ultra-thin lenses, there actually exists intrinsic check-and-balance between size, broadband and performance. Unveiled by our inverse design algorithm, Relative-Phase Simulated Annealing (RPSA), focusing efficiency inevitably drops with refining wavelength intervals for better achromatic broadband features in optimized lens; and drops exponentially with increasing diameter and bandwidth, supported by our empirical formula. Meanwhile, with a slightly compromised goal, the powerfulness of RPSA is unlocked since it could provide a globally optimized design recipe whose time complexity relates to lens scale linearly rather than exponentially. This work, as a fast search engine for optimal solutions, paves the way towards practical large-scale achromatic ultra-thin lenses.

Quantum machine learning for chemistry and physics

Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.

Single-sized multifunctional metasurfaces for simultaneous nanoprinting and holography inspired by tri-redundancy

Multifunctional metasurfaces, where multiple functions can be integrated into a piece of metasurface, are preferably desired for compact systems with higher integration and subwavelength footprint. Particularly, metasurfaces for simultaneous nanoprinting and holography are one of the promising directions of development image display and information hiding in meta-devices. Here, inspired by tri-redundancy, a new, to the best of our knowledge, approach is proposed for generating a nanoprinting image in the near field and holographic image in the far field simultaneously, which can solve the extremum-mapping problem existing in single-sized scheme without increasing the complexity of the nanostructures. The tri-redundancy of image recognition, hologram designing and intensity modulation introduce an extra degree of freedom, which helps to find a balance between the two types of meta-images generated by utilizing the simulated annealing algorithm. A multifunctional metasurface composed of single-sized silver nanobricks with in-plane orientation has been fabricated to demonstrate the feasibility of encoding a binary image in the near field while reconstructing a 16-steps holographic image without twin-image in the far field. This multifunctional metasurface has flexible working modes, broadband working window and large robustness for fabrication errors, and it provides a simple design scheme for multifunctional integration. We expect it can empower advanced research and applications in high-end optical anticounterfeiting, image hiding and so on.

Polarization multiplexed dissipative Kerr solitons in an on-chip micro-resonator

We demonstrate polarization multiplexed dissipative Kerr solitons in an on-chip silicon nitride micro-resonator. In our experiment, TE- and TM-polarized solitons can be individually generated and controlled, thanks to their weak mutual interaction as the result of sufficiently different repetition rates and orthogonal polarization states. Furthermore, we find that TE- and TM-polarized solitons usually exhibit uncorrelated time jitters. Therefore, the frequency and phase coherence between the polarization multiplexed soliton microcombs change dramatically as a function of pump laser parameters, where by optimization, we achieve a narrow dual-microcomb beat note linewidth as small as 4.4 kHz. Potential applications of on-chip polarization multiplexed soliton microcombs include Kerr comb spectral expansion, dual-comb metrology, and measurement of quantum entanglements.

Single-Shot Light-Field Microscopy: An Emerging Tool for 3D Biomedical Imaging

3D microscopy is a useful tool to visualize the detailed structures and mechanisms of biomedical specimens. In particular, biophysical phenomena such as neural activity require fast 3D volumetric imaging because fluorescence signals degrade quickly. A light-field microscope (LFM) has recently attracted attention as a high-speed volumetric imaging technique by recording 3D information in a single-snapshot. This review highlighted recent progress in LFM techniques for 3D biomedical applications. In detail, various image reconstruction algorithms according to LFM configurations are explained, and several biomedical applications such as neuron activity localization, live-cell imaging, locomotion analysis, and single-molecule visualization are introduced. We also discuss deep learning-based LFMs to enhance image resolution and reduce reconstruction artifacts.

Recent advances in chiral nanomaterials with unique electric and magnetic properties

Research on chiral nanomaterials (NMs) has grown radically with a rapid increase in the number of publications over the past decade. It has attracted a large number of scientists in various fields predominantly because of the emergence of unprecedented electric, optical, and magnetic properties when chirality arises in NMs. For applications, it is particularly informative and fascinating to investigate how chiral NMs interact with electromagnetic waves and magnetic fields, depending on their intrinsic composition properties, atomic distortions, and assembled structures. This review provides an overview of recent advances in chiral NMs, such as semiconducting, metallic, and magnetic nanostructures.

An acoustofluidic scanning nanoscope using enhanced image stacking and processing

Nanoscale optical resolution with a large field of view is a critical feature for many research and industry areas, such as semiconductor fabrication, biomedical imaging, and nanoscale material identification. Several scanning microscopes have been developed to resolve the inverse relationship between the resolution and field of view; however, those scanning microscopes still rely upon fluorescence labeling and complex optical systems. To overcome these limitations, we developed a dual-camera acoustofluidic nanoscope with a seamless image merging algorithm (alpha-blending process). This design allows us to precisely image both the sample and the microspheres simultaneously and accurately track the particle path and location. Therefore, the number of images required to capture the entire field of view (200 × 200 μm) by using our acoustofluidic scanning nanoscope is reduced by 55-fold compared with previous designs. Moreover, the image quality is also greatly improved by applying an alpha-blending imaging technique, which is critical for accurately depicting and identifying nanoscale objects or processes. This dual-camera acoustofluidic nanoscope paves the way for enhanced nanoimaging with high resolution and a large field of view.

High-throughput digital pathology via a handheld, multiplexed, and AI-powered ptychographic whole slide scanner

The recent advent of whole slide imaging (WSI) systems has moved digital pathology closer to diagnostic applications and clinical practices. Integrating WSI with machine learning promises the growth of this field in upcoming years. Here we report the design and implementation of a handheld, colour-multiplexed, and AI-powered ptychographic whole slide scanner for digital pathology applications. This handheld scanner is built using low-cost and off-the-shelf components, including red, green, and blue laser diodes for sample illumination, a modified stage for programmable sample positioning, and a synchronized image sensor pair for data acquisition. We smear a monolayer of goat blood cells on the main sensor for high-resolution lensless coded ptychographic imaging. The synchronized secondary sensor acts as a non-contact encoder for precisely tracking the absolute object position for ptychographic reconstruction. For WSI, we introduce a new phase-contrast-based focus metric for post-acquisition autofocusing of both stained and unstained specimens. We show that the scanner can resolve the 388-nm linewidth on the resolution target and acquire gigapixel images with a 14 mm × 11 mm area in ∼70 seconds. The imaging performance is validated with regular stained pathology slides, unstained thyroid smears, and malaria-infected blood smears. The deep neural network developed in this study further enables high-throughput cytometric analysis using the recovered complex amplitude. The reported do-it-yourself scanner offers a portable solution to transform the high-end WSI system into one that can be made widely available at a low cost. The capability of high-throughput quantitative phase imaging may also find applications in rapid on-site evaluations.

HoloPhaseNet: fully automated deep-learning-based hologram reconstruction using a conditional generative adversarial model

Quantitative phase imaging with off-axis digital holography in a microscopic configuration provides insight into the cells' intracellular content and morphology. This imaging is conventionally achieved by numerical reconstruction of the recorded hologram, which requires the precise setting of the reconstruction parameters, including reconstruction distance, a proper phase unwrapping algorithm, and component of wave vectors. This paper shows that deep learning can perform the complex light propagation task independent of the reconstruction parameters. We also show that the super-imposed twin-image elimination technique is not required to retrieve the quantitative phase image. The hologram at the single-cell level is fed into a trained image generator (part of a conditional generative adversarial network model), which produces the phase image. Also, the model's generalization is demonstrated by training it with holograms of size 512×512 pixels, and the resulting quantitative analysis is shown.

Sub-diffraction-limit light sheet enabled by a super-oscillatory lens with an enlarged field of view and depth of focus

Static light sheets are widely used in various super-resolution three-dimensional (3D) imaging applications. Here, a multifocal diffraction-free optimized design method is proposed for super-oscillatory lenses (SOLs) owning an enlarged field of view (FOV) to generate sub-diffraction-limit light sheets with reduced divergence. Various propagation lengths of sub-diffraction-limit thickness for light sheets can be obtained by adopting corresponding numbers of discrete foci and spacing between them. In particular, the propagation lengths of 150.4λ and 118.9λ are obtained by SOLs with an enlarged FOV of 150λ and 820λ, respectively, which show the longest depth of focus (DOF), as far as we know, and are the first to realize the combination of enlarged DOF and FOV for SOLs. We show a way of using binary-amplitude modulation to generate static light sheets with sub-diffraction-limit thickness and reduced divergence, which is simple, easy to integrate, and sidelobe-suppressed.

Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network

The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections. Microbial infections are a major healthcare issue worldwide, as these widespread diseases often develop into deadly symptoms. While studies have shown that an early appropriate antibiotic treatment significantly reduces the mortality of an infection, this effective treatment is difficult to practice. The main obstacle to early appropriate antibiotic treatments is the long turnaround time of the routine microbial identification, which includes time-consuming sample growth. Here, we propose a microscopy-based framework that identifies the pathogen from single to few cells. Our framework obtains and exploits the morphology of the limited sample by incorporating three-dimensional quantitative phase imaging and an artificial neural network. We demonstrate the identification of 19 bacterial species that cause bloodstream infections, achieving an accuracy of 82.5% from an individual bacterial cell or cluster. This performance, comparable to that of the gold standard mass spectroscopy under a sufficient amount of sample, underpins the effectiveness of our framework in clinical applications. Furthermore, our accuracy increases with multiple measurements, reaching 99.9% with seven different measurements of cells or clusters. We believe that our framework can serve as a beneficial advisory tool for clinicians during the initial treatment of infections.

Cooling of an integrated Brillouin laser below the thermal limit

Photonically integrated resonators are promising as a platform for enabling ultranarrow linewidth lasers in a compact form factor. Owing to their small size, these integrated resonators suffer from thermal noise that limits the frequency stability of the optical mode to ∼100 kHz. Here, we demonstrate an integrated stimulated Brillouin scattering (SBS) laser based on a large mode-volume annulus resonator that realizes an ultranarrow thermal-noise-limited linewidth of 270 Hz. In practice, yet narrower linewidths are required before integrated lasers can be truly useful for applications such as optical atomic clocks, quantum computing, gravitational wave detection, and precision spectroscopy. To this end, we employ a thermorefractive noise suppression technique utilizing an auxiliary laser to reduce our SBS laser linewidth to 70 Hz. This demonstration showcases the possibility of stabilizing the thermal motion of even the narrowest linewidth chip lasers to below 100 Hz, thereby opening the door to making integrated microresonators practical for the most demanding future scientific endeavors.

Terahertz spatio-temporal deep learning computed tomography

Terahertz computed tomography (THz CT) has drawn significant attention because of its unique capability to bring multi-dimensional object information from invisible to visible. However, current physics-model-based THz CT modalities present low data use efficiency on time-resolved THz signals and low model fusion extensibility, limiting their application fields' practical use. In this paper, we propose a supervised THz deep learning computed tomography (THz DL-CT) framework based on time-domain information. THz DL-CT restores superior THz tomographic images of 3D objects by extracting features from spatio-temporal THz signals without any prior material information. Compared with conventional and machine learning based methods, THz DL-CT delivers at least 50.2%, and 52.6% superior in root mean square error (RMSE) and structural similarity index (SSIM), respectively. Additionally, we have experimentally demonstrated that the pretrained THz DL-CT model can generalize to reconstruct multi-material systems with no prerequisite information. THz CT through the DL data fusion approach provides a new pathway for non-invasive functional imaging in object investigation.