Pixel-level Bayer-type colour router based on metasurfaces

Metasurface-assisted multimodal quantum imaging

Traditional quantum imaging is featured by remarkable sensitivity and signal-to-noise ratio, but limited by bulkiness and static function (either phase contrast imaging or edge detection). Our report synergizes a polarization-entangled source with a metasurface consisting of various sophisticatedly engineered spatial frequency segments. By tuning polarization, we demonstrate multiple "on"-state quantum imaging modes, enabling flexible switching between phase contrast, edge, and arbitrary superimposed imaging mode. Furthermore, the "off"-state, which characterizes the background noise, enables self-calibration of the system by subtracting this noise in "on"-state modes, resulting in self-enhanced edge detection. Our approach performs phase contrast imaging with a phase difference of π/4 present in the target object, and edge imaging capable of detecting tiny (radius about 2 μm) defects, maintaining high image contrast (phase contrast of 0.726, and enhanced edge contrast of 0.902). Our results provide insights into constructive duet between quantum imaging and metaoptics.

Angle-Insensitive Broadband Multispectro-Polarimetric Encoding Based on Inverse Design of Mosaic Metasurfaces

Multimodal optical information detection allows precise characterization of light-matter interactions. Current multimodal optical detection systems often encounter challenges such as low spectral resolution, limited polarization sensitivity, or bulky designs that impede seamless integration. Here, a broadband multispectro-polarimetric filter (BMSPF) metasurface array enables simultaneous spectral is proposed and full-Stokes polarimetric imaging in a single snapshot. By combining Metal-Insulator-Metal (MIM) metasurfaces with a mosaic optimization design, angle-robust multispectro-polarimetric encoding under a 30° field-of-view is achieved. The encoded grayscale information is captured via a monochrome image sensor and decoded using a neural network. Operating across the visible-to- near-infrared range (400-1100 nm), the system attains an impressive spectral resolution of 6 nm. This method expands the application scenarios of multispectro-polarimetric imaging and provides a pathway for the manufacturability of metasurface devices.

Programmable electron-induced color router array

The development of color routers (CRs) realizes the splitting of dichromatic components, contributing to the modulation of photon momentum that acts as the information carrier for optical information technology on the frequency and spatial domains. However, CRs with optical stimulation lack active control of photon momentum at deep subwavelength scale because of the optical diffraction limit. Here, we experimentally demonstrate an active manipulation of dichromatic photon momentum at a deep subwavelength scale via electron-induced CRs, where the CRs radiation patterns are manipulated by steering the electron impact position within 60 nm in a single nanoantenna unit. Moreover, an encrypted display device based on programmable modulation of the CR array is designed and implemented. This approach with enhanced security, large information capacity, and high-level integration at a deep subwavelength scale may find applications in photonic devices and emerging areas in quantum information technologies.

On-Chip Cascaded Metasurfaces for Visible Wavelength Division Multiplexing and Color-Routing Meta-Display

Integrating metasurfaces on-chip offers a promising strategy for modulating and extracting guided waves, suggesting tremendous applications in compact wearable devices. However, despite the full acquisition of on-chip manipulation of optical parameters, including phase, amplitude, and polarization, the functionality of on-chip metasurfaces remains limited by the lack of wavelength selectivity. Here, an on-chip approach to differentiate wavelength components is proposed in the visible regime for wavelength division multiplexing (WDM). Through horizontally cascading on-chip meta-atoms with structural dimension variation and optimization, different wavelength components propagating along the waveguide would be selectively extracted, realizing meta-demultiplexing functionality. More intriguingly, color nanoprinting images or holographic displays can be correspondingly enabled. This approach surpasses conventional free-space meta-devices in terms of exhibiting improved wavelength-selective allocation and eliminating the energy waste caused by spatial multiplexing. We envision that such an on-chip cascading strategy paves the way for next-generation WDM devices in photonic integrated circuits and wearable miniature meta-displays.

Trends in Snapshot Spectral Imaging: Systems, Processing, and Quality

Recent advances in spectral imaging have enabled snapshot acquisition, as a means to mitigate the impracticalities of spectral imaging, e.g., expert operators and cumbersome hardware. Snapshot spectral imaging, e.g., in technologies like spectral filter arrays, has also enabled higher temporal resolution at the expense of the spatio-spectral resolution, allowing for the observation of temporal events. Designing, realising, and deploying such technologies is yet challenging, particularly due to the lack of clear, user-meaningful quality criteria across diverse applications, sensor types, and workflows. Key research gaps include optimising raw image processing from snapshot spectral imagers and assessing spectral image and video quality in ways valuable to end-users, manufacturers, and developers. This paper identifies several challenges and current opportunities. It proposes considering them jointly and suggests creating a new unified snapshot spectral imaging paradigm that would combine new systems and standards, new algorithms, new cost functions, and quality indices.

Design of a polarization-insensitive broadband achromatic metalens for mid-wave infrared detector

Metalenses, boasting outstanding focusing efficiency and high-resolution imaging capabilities, have generated widespread usage in fields such as integrated optics, achromatic imaging, and optical holography. In this study, we have developed a broadband achromatic metalens within the detection range from 3 to 5 µm, and it has a numerical aperture (NA) of 0.71 with a remarkable maximum focusing efficiency of 63.8% at the focal plane within the specified bandwidth. We have further delved into the dispersion control mechanism that combines the geometric and transmission phases and optimized the constructed phase response simulation database using the particle swarm optimization (PSO) algorithm, ensuring a precise phase matching between the actual wavefront and the ideal focusing wavefront. This metalens with its ability to expand the array size has the potential to create a compact infrared imager, which holds significant importance in achieving efficient detection and integration within infrared detectors.

Metalenses phase characterization by multi-distance phase retrieval

Metalens, characterized by their unique functions and distinctive physical properties, have gained significant attention for their potential applications. To further optimize the performance of metalens, it is necessary to characterize the phase modulation of the metalens. In this study, we present a multi-distance phase retrieval system based on optical field scanning and discuss its convergence and robustness. Our findings indicate that the system is capable of retrieving the phase distribution of the metalens as long as the measurement noise is low and the total length of the scanned light field is sufficiently long. This system enables the analysis of focal length and aberration by utilizing the computed phase distribution. We extend our investigation to measure the phase distribution of the metalens operating in the near-infrared (NIR) spectrum and identify the impact of defects in the sample on the phase. Additionally, we conduct a comparative analysis of the phase distribution of the metalens in air and ethanol and observe the variations in the phase modulation of the metalens in different working mediums. Our system provides a straightforward method for the phase characterization of metalens, aiding in optimizing the metalens design and functionality.

Inverse design of color routers in CMOS image sensors: toward minimizing interpixel crosstalk

Over the past decade, significant advancements in high-resolution imaging technology have been driven by the miniaturization of pixels within image sensors. However, this reduction in pixel size to submicrometer dimensions has led to decreased efficiency in color filters and microlens arrays. The development of color routers that operate at visible wavelengths presents a promising avenue for further miniaturization. Despite this, existing color routers often encounter severe interpixel crosstalk, around 70 %, due to the reliance on periodic boundary conditions. Here, we present interpixel crosstalk-minimized color routers that achieve an unprecedented in-pixel optical efficiency of 87.2 % and significantly reduce interpixel crosstalk to 2.6 %. The color routers are designed through adjoint optimization, incorporating customized incident waves to minimize interpixel crosstalks. Our findings suggest that our color router design surpasses existing color routing techniques in terms of in-pixel optical efficiency, representing a crucial step forward in the push toward commercializing the next generation of solid-state image sensors.

Color arrestor pixels for high-fidelity, high-sensitivity imaging sensors

Silicon is the dominant material in complementary metal-oxide-semiconductor (CMOS) imaging devices because of its outstanding electrical and optical properties, well-established fabrication methods, and abundance in nature. However, with the ongoing trend toward electronic miniaturization, which demands smaller pixel sizes in CMOS image sensors, issues, such as crosstalk and reduced optical efficiency, have become critical. These problems stem from the intrinsic properties of Si, particularly its low absorption in the long wavelength range of the visible spectrum, which makes it difficult to devise effective solutions unless the material itself is changed. Recent advances in optical metasurfaces have offered new possibilities for solving these problems. In this study, we propose color arrestor pixels (CAPs) as a new class of color image sensors whose composite spectral responses directly mimic those of the human eye. The key idea is to employ linearly independent combinations of standardized color matching functions. These new basis functions allow our device to reproduce colors more accurately than the currently available image sensors with red-green-blue filters or other metasurface-based sensors, demonstrating an average CIEDE2000 color difference value of only 1.79 when evaluating 24 colors from the Gretag-Macbeth chart under standard illuminant D65. Owing to their high fidelity to the human eye response, CAPs consistently exhibit exceptional color reproduction accuracy under various spectral illumination compositions. With a small footprint of 860 nm height and 221 nm full-color pixel pitch, the CAPs demonstrated high absorption efficiencies of 79 %, 81 %, and 63 % at wavelengths of 452 nm, 544 nm, and 603 nm, respectively, and good angular tolerance. With such a high density of pixels efficiently capturing accurate colors, CAPs present a new direction for optical image sensor research and their applications.

Monolithic Perovskite-Silicon Dual-Band Photodetector for Efficient Spectral Light Discrimination

Selective spectral discrimination of visible and near-infrared light, which accurately distinguishes different light wavelengths, holds considerable promise in various fields, such as automobiles, defense, and environmental monitoring. However, conventional imaging technologies suffer from various issues, including insufficient spatial optimization, low definition, and optical loss. Herein, a groundbreaking advancement is demonstrated in the form of a dual-band photodiode with distinct near-infrared- and visible-light discrimination obtained via simple voltage control. The approach involves the monolithic stacking integration of methylammonium lead iodide (MAPbI3) and Si semiconductors, resulting in a p-Si/n-phenyl-C61-butyric acid methyl ester/i-MAPbI3/p-spiro-MeOTAD (PNIP) device. Remarkably, the PNIP configuration can independently detect the visible and near-infrared regions without traditional optical filters under a voltage range of 3 to -3 V. In addition, an imaging system for a prototype autonomous vehicle confirms the capability of the device to separate visible and near-infrared light via an electrical bias and practicality of this mechanism. Therefore, this study pushes the boundaries of image sensor development and sets the stage for fabricating compact and power-efficient photonic devices with superior performance and diverse functionality.

Freeform metasurface color router for deep submicron pixel image sensors

Advances in imaging technologies have led to a high demand for ultracompact, high-resolution image sensors. However, color filter-based image sensors, now miniaturized to deep submicron pixel sizes, face challenges such as low signal-to-noise ratio due to fewer photons per pixel and inherent efficiency limitations from color filter arrays. Here, we demonstrate a freeform metasurface color router that achieves ultracompact pixel sizes while overcoming the efficiency limitations of conventional architectures by splitting and focusing visible light instead of filtering. This development is enabled by a fully differentiable topology optimization framework to maximize the use of the design space while ensuring fabrication feasibility and robustness to fabrication errors. The metasurface can distribute an average of 85% of incident visible light according to the Bayer pattern with a pixel size of 0.6 μm. The device and design methodology enable the compact, high-sensitivity, and high-resolution image sensors for various modern technologies and pave the way for the advanced photonic device design.

Multi-Dimensional Multiplexed Metasurface Holography by Inverse Design

Multi-dimensional multiplexed metasurface holography extends holographic information capacity and promises revolutionary advancements for vivid imaging, information storage, and encryption. However, achieving multifunctional metasurface holography by forward design method is still difficult because it relies heavily on Jones matrix engineering, which places high demands on physical knowledge and processing technology. To break these limitations and simplify the design process, here, an end-to-end inverse design framework is proposed. By directly linking the metasurface to the reconstructed images and employing a loss function to guide the update of metasurface, the calculation of hologram can be omitted; thus, greatly simplifying the design process. In addition, the requirements on the completeness of meta-library can also be significantly reduced, allowing multi-channel hologram to be achieved using meta-atoms with only two degrees of freedom, which is very friendly to processing. By exploiting the proposed method, metasurface hologram containing up to 12 channels of multi-wavelength, multi-plane, and multi-polarization is designed and experimentally demonstrated, which exhibits the state-of-the-art information multiplexing capacity of the metasurface composed of simple meta-atoms. This method is conducive to promoting the intelligent design of multifunctional meta-devices, and it is expected to eventually accelerate the application of meta-devices in colorful display, imaging, storage and other fields.

Optical color routing enabled by deep learning

Nano-color routing has emerged as an immensely popular and widely discussed subject in the realms of light field manipulation, image sensing, and the integration of deep learning. The conventional dye filters employed in commercial applications have long been hampered by several limitations, including subpar signal-to-noise ratio, restricted upper bounds on optical efficiency, and challenges associated with miniaturization. Nonetheless, the advent of bandpass-free color routing has opened up unprecedented avenues for achieving remarkable optical spectral efficiency and operation at sub-wavelength scales within the area of image sensing applications. This has brought about a paradigm shift, fundamentally transforming the field by offering a promising solution to surmount the constraints encountered with traditional dye filters. This review presents a comprehensive exploration of representative deep learning-driven nano-color routing structure designs, encompassing forward simulation algorithms, photonic neural networks, and various global and local topology optimization methods. A thorough comparison is drawn between the exceptional light-splitting capabilities exhibited by these methods and those of traditional design approaches. Additionally, the existing research on color routing is summarized, highlighting a promising direction for forthcoming development, delivering valuable insights to advance the field of color routing and serving as a powerful reference for future endeavors.

Meta-Lens Particle Image Velocimetry

Fluid flow behavior is visualized through particle image velocimetry (PIV) for understanding and studying experimental fluid dynamics. However, traditional PIV methods require multiple cameras and conventional lens systems for image acquisition to resolve multi-dimensional velocity fields. In turn, it introduces complexity to the entire system. Meta-lenses are advanced flat optical devices composed of artificial nanoantenna arrays. It can manipulate the wavefront of light with the advantages of ultrathin, compact, and no spherical aberration. Meta-lenses offer novel functionalities and promise to replace traditional optical imaging systems. Here, a binocular meta-lens PIV technique is proposed, where a pair of GaN meta-lenses are fabricated on one substrate and integrated with a imaging sensor to form a compact binocular PIV system. The meta-lens weigh only 116 mg, much lighter than commercial lenses. The 3D velocity field can be obtained by the binocular disparity and particle image displacement information of fluid flow. The measurement error of vortex-ring diameter is ≈1.25% experimentally validates via a Reynolds-number (Re) 2000 vortex-ring. This work demonstrates a new development trend for the PIV technique for rejuvenating traditional flow diagnostic tools toward a more compact, easy-to-deploy technique. It enables further miniaturization and low-power systems for portable, field-use, and space-constrained PIV applications.

Unleashing the potential: AI empowered advanced metasurface research

In recent years, metasurface, as a representative of micro- and nano-optics, have demonstrated a powerful ability to manipulate light, which can modulate a variety of physical parameters, such as wavelength, phase, and amplitude, to achieve various functions and substantially improve the performance of conventional optical components and systems. Artificial Intelligence (AI) is an emerging strong and effective computational tool that has been rapidly integrated into the study of physical sciences over the decades and has played an important role in the study of metasurface. This review starts with a brief introduction to the basics and then describes cases where AI and metasurface research have converged: from AI-assisted design of metasurface elements up to advanced optical systems based on metasurface. We demonstrate the advanced computational power of AI, as well as its ability to extract and analyze a wide range of optical information, and analyze the limitations of the available research resources. Finally conclude by presenting the challenges posed by the convergence of disciplines.

A metasurface color router facilitating RGB-NIR sensing for an image sensor application

CMOS image sensor (CIS) plays a crucial role in diverse optical applications by facilitating the capture of images in the visible and near-infrared spectra. The enhancement of image resolution in CIS by an increase in pixel density is becoming more significant and realizable with the recent progress of nanofabrication. However, as pixel size decreases towards the diffraction limit, there is an inevitable trade-off between the scale-down of pixel size and the enhancement of optical sensitivity. Recently, to overcome this, an entirely new concept of spectral sensing using a nanophotonic-based color router has been proposed. In this work, we present a metasurface-based spectral router to effectively split the spectrum from visible to near-infrared and redirect through the four optical channels to the targeted pixel surfaces. We optimize the metasurface that simultaneously controls the phases of the transmitted light of targeted spectra, i.e. red (R), green (G), blue (B), and near-infrared (NIR), which is the largest number of channels reported based on a single layered metasurface and has an optical efficiency that surpasses the efficiency of conventional color filter systems.

Computational simulation of multi-wavelength light-field thermometry based on a chromatic meta-lens

This Letter proposes a light-field meta-lens multi-wavelength thermometry (MMT) system that is capable of modulating a full-spectrum incident radiation into four separate wavelength beams. The chromatic meta-lens is designed using finite-difference time-domain (FDTD) software to function as a filter, ensuring its ability to separate four wavelengths. The chromatic meta-lens is positioned on the back focus plane of the main lens to replace the microlens used in traditional light-field systems and simplify the overall system. After detecting the acquired wavelengths and intensities of the image on photodiodes, a raw multispectral image can be decoupled and processed using the Chameleon swarm algorithm (CSA). Four full-spectrum incident radiations corresponding to four temperature characteristic curves are detected. The high accuracy of the reverse temperature calculation enables the measurement of surface high-temperature distribution with precision.

Inverse design of a light nanorouter for a spatially multiplexed optical filter

It is attractive to use an optical nanorouter by artificial nanostructures to substitute the traditional Bayer filter for an image array sensor, which, however, poses great challenges in balancing the design strategy and the ease of fabrication. Here, we implement and compare two inverse design schemes for rapid optimization of RGGB Bayer-type optical nanorouter. One is based on the multiple Mie scattering theory and the adjoint gradient that is applicable to arrays of nanospheres with varying sizes, and the other is based on the rigorous coupled wave analysis and the genetic algorithm. In both cases, we study layered nanostructures that can be efficiently modeled respectively which greatly accelerates the inverse design. It is shown that the color-dependent peak collection efficiencies of nanorouters designed in the two methods for red, green, and blue wavelengths reach 37%, 44%, and 45% and 52%, 50%, and 66%, respectively. We further demonstrate color nanorouters that provide light focusing to four quadrants working in both the visible and infrared bands, which promises multispectral imaging applications.

Metasurface-empowered snapshot hyperspectral imaging with convex/deep (CODE) small-data learning theory

Hyperspectral imaging is vital for material identification but traditional systems are bulky, hindering the development of compact systems. While previous metasurfaces address volume issues, the requirements of complicated fabrication processes and significant footprint still limit their applications. This work reports a compact snapshot hyperspectral imager by incorporating the meta-optics with a small-data convex/deep (CODE) deep learning theory. Our snapshot hyperspectral imager comprises only one single multi-wavelength metasurface chip working in the visible window (500-650 nm), significantly reducing the device area. To demonstrate the high performance of our hyperspectral imager, a 4-band multispectral imaging dataset is used as the input. Through the CODE-driven imaging system, it efficiently generates an 18-band hyperspectral data cube with high fidelity using only 18 training data points. We expect the elegant integration of multi-resonant metasurfaces with small-data learning theory will enable low-profile advanced instruments for fundamental science studies and real-world applications.

Asymptotic dispersion engineering for ultra-broadband meta-optics

Dispersion decomposes compound light into its monochromatic components, which is detrimental to broadband imaging but advantageous for spectroscopic applications. Metasurfaces provide a unique path to modulate the dispersion by adjusting structural parameters on a two-dimensional plane. However, conventional linear phase compensation does not adequately match the meta-unit's dispersion characteristics with required complex dispersion, hindering at-will dispersion engineering over a very wide bandwidth particularly. Here, we propose an asymptotic phase compensation strategy for ultra-broadband dispersion-controlled metalenses. Metasurfaces with extraordinarily high aspect ratio nanostructures have been fabricated for arbitrary dispersion control in ultra-broad bandwidth, and we experimentally demonstrate the single-layer achromatic metalenses in the visible to infrared spectrum (400 nm~1000 nm, NA = 0.164). Our proposed scheme provides a comprehensive theoretical framework for single-layer meta-optics, allowing for arbitrary dispersion manipulation without bandwidth restrictions. This development is expected to have significant applications in ultra-broadband imaging and chromatography detection, among others.

Design parameters of free-form color splitters for subwavelength pixelated image sensors

Metasurface-based color splitters are emerging as next-generation optical components for image sensors, replacing classical color filters and microlens arrays. In this work, we report how the design parameters such as the device dimensions and refractive indices of the dielectrics affect the optical efficiency of the color splitters. Also, we report how the design grid resolution parameters affect the optical efficiency and discover that the fabrication of a color splitter is possible even in legacy fabrication facilities with low structure resolutions.

Deep Learning in Precision Agriculture: Artificially Generated VNIR Images Segmentation for Early Postharvest Decay Prediction in Apples

Food quality control is an important task in the agricultural domain at the postharvest stage for avoiding food losses. The latest achievements in image processing with deep learning (DL) and computer vision (CV) approaches provide a number of effective tools based on the image colorization and image-to-image translation for plant quality control at the postharvest stage. In this article, we propose the approach based on Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) techniques to use synthesized and segmented VNIR imaging data for early postharvest decay and fungal zone predictions as well as the quality assessment of stored apples. The Pix2PixHD model achieved higher results in terms of VNIR images translation from RGB (SSIM = 0.972). Mask R-CNN model was selected as a CNN technique for VNIR images segmentation and achieved 58.861 for postharvest decay zones, 40.968 for fungal zones and 94.800 for both the decayed and fungal zones detection and prediction in stored apples in terms of F1-score metric. In order to verify the effectiveness of this approach, a unique paired dataset containing 1305 RGB and VNIR images of apples of four varieties was obtained. It is further utilized for a GAN model selection. Additionally, we acquired 1029 VNIR images of apples for training and testing a CNN model. We conducted validation on an embedded system equipped with a graphical processing unit. Using Pix2PixHD, 100 VNIR images from RGB images were generated at a rate of 17 frames per second (FPS). Subsequently, these images were segmented using Mask R-CNN at a rate of 0.42 FPS. The achieved results are promising for enhancing the food study and control during the postharvest stage.

Integrated metasurfaces for re-envisioning a near-future disruptive optical platform

Metasurfaces have been continuously garnering attention in both scientific and industrial fields, owing to their unprecedented wavefront manipulation capabilities using arranged subwavelength artificial structures. To date, research has mainly focused on the full control of electromagnetic characteristics, including polarization, phase, amplitude, and even frequencies. Consequently, versatile possibilities of electromagnetic wave control have been achieved, yielding practical optical components such as metalenses, beam-steerers, metaholograms, and sensors. Current research is now focused on integrating the aforementioned metasurfaces with other standard optical components (e.g., light-emitting diodes, charged-coupled devices, micro-electro-mechanical systems, liquid crystals, heaters, refractive optical elements, planar waveguides, optical fibers, etc.) for commercialization with miniaturization trends of optical devices. Herein, this review describes and classifies metasurface-integrated optical components, and subsequently discusses their promising applications with metasurface-integrated optical platforms including those of augmented/virtual reality, light detection and ranging, and sensors. In conclusion, this review presents several challenges and prospects that are prevalent in the field in order to accelerate the commercialization of metasurfaces-integrated optical platforms.

Recent advanced applications of metasurfaces in multi-dimensions

Unlike traditional optical components, which rely on the gradual accumulation of light along the optical path over a distance much larger than the wavelength to form a wavefront, metasurfaces manipulate light field properties on the wavelength thickness by specially arranging various meta-atoms. Due to the ease of integration and compact planar structure, metasurfaces play a key role in the light field manipulations. Here, we review the recent advances of metasurfaces in multi-dimensions, including light wavelength, polarization, orbital angular momentum(OAM), and angular response. Progress in these fields has brought new applications in areas such as imaging, display, communication, and information encryption, etc. Finally, we also discuss the challenges and prospects of metasurfaces applications.

Snapshot multispectral imaging using a diffractive optical network

Multispectral imaging has been used for numerous applications in e.g., environmental monitoring, aerospace, defense, and biomedicine. Here, we present a diffractive optical network-based multispectral imaging system trained using deep learning to create a virtual spectral filter array at the output image field-of-view. This diffractive multispectral imager performs spatially-coherent imaging over a large spectrum, and at the same time, routes a pre-determined set of spectral channels onto an array of pixels at the output plane, converting a monochrome focal-plane array or image sensor into a multispectral imaging device without any spectral filters or image recovery algorithms. Furthermore, the spectral responsivity of this diffractive multispectral imager is not sensitive to input polarization states. Through numerical simulations, we present different diffractive network designs that achieve snapshot multispectral imaging with 4, 9 and 16 unique spectral bands within the visible spectrum, based on passive spatially-structured diffractive surfaces, with a compact design that axially spans ~72λ_m, where λ_m is the mean wavelength of the spectral band of interest. Moreover, we experimentally demonstrate a diffractive multispectral imager based on a 3D-printed diffractive network that creates at its output image plane a spatially repeating virtual spectral filter array with 2 × 2 = 4 unique bands at terahertz spectrum. Due to their compact form factor and computation-free, power-efficient and polarization-insensitive forward operation, diffractive multispectral imagers can be transformative for various imaging and sensing applications and be used at different parts of the electromagnetic spectrum where high-density and wide-area multispectral pixel arrays are not widely available.

Axicon metalens for broadband light harvesting

In this study, an axicon metalens comprising a large central disc surrounded by nanoposts for energy harvesting in composite metal-oxide semiconductor sensors was designed, fabricated, and experimentally characterized. The main role of the central disc is focusing light; the nanoposts of various diameters deflect light to form a Bessel-like beam. The spatial distribution of the optical transmission was measured using micro-hyperspectral imaging. The axicon metalens concentrates the light to the sensitive area of the sensor and also harvests light from adjacent pixels. After adding an axicon metalens, the normalized peak transmission is up to 250% at λ = 700 nm as compared to a blank TiO2 film. The experimental results had fair agreement with the finite-difference-time-domain simulation. The ultra-broadband energy-harvesting performance of the sensor suggests that it could be applied in surveillance and Internet of Things applications.

mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics

Hyperspectral imaging acquires data in both the spatial and frequency domains to offer abundant physical or biological information. However, conventional hyperspectral imaging has intrinsic limitations of bulky instruments, slow data acquisition rate, and spatiospectral trade-off. Here we introduce hyperspectral learning for snapshot hyperspectral imaging in which sampled hyperspectral data in a small subarea are incorporated into a learning algorithm to recover the hypercube. Hyperspectral learning exploits the idea that a photograph is more than merely a picture and contains detailed spectral information. A small sampling of hyperspectral data enables spectrally informed learning to recover a hypercube from a red-green-blue (RGB) image without complete hyperspectral measurements. Hyperspectral learning is capable of recovering full spectroscopic resolution in the hypercube, comparable to high spectral resolutions of scientific spectrometers. Hyperspectral learning also enables ultrafast dynamic imaging, leveraging ultraslow video recording in an off-the-shelf smartphone, given that a video comprises a time series of multiple RGB images. To demonstrate its versatility, an experimental model of vascular development is used to extract hemodynamic parameters via statistical and deep learning approaches. Subsequently, the hemodynamics of peripheral microcirculation is assessed at an ultrafast temporal resolution up to a millisecond, using a conventional smartphone camera. This spectrally informed learning method is analogous to compressed sensing; however, it further allows for reliable hypercube recovery and key feature extractions with a transparent learning algorithm. This learning-powered snapshot hyperspectral imaging method yields high spectral and temporal resolutions and eliminates the spatiospectral trade-off, offering simple hardware requirements and potential applications of various machine learning techniques.

Metasurface-based triple-band beam splitter with large spatial separation at visible wavelengths

The dual-function of a wavelength beam splitter and a power beam splitter is desired in both classical optics and quantum optics. We propose a triple-band large-spatial-separation beam splitter at visible wavelengths using a phase-gradient metasurface in both the x- and y-directions. Under x-polarized normal incidence, the blue light is split in the y-direction into two equal-intensity beams owing to the resonance inside a single meta-atom, the green light is split in the x-direction into another two equal-intensity beams owing to the size variation between adjacent meta-atoms, while the red light passes directly without splitting. The size of the meta-atoms was optimized based on their phase response and transmittance. The simulated working efficiencies under normal incidence are 68.1%, 85.0%, and 81.9% at the wavelengths of 420 nm, 530 nm, and 730 nm, respectively. The sensitivities of the oblique incidence and polarization angle are also discussed.

Metasurfaces enabled polarization-multiplexing heralded single photon imaging

Quantum imaging has non-negligible advantages in terms of sensitivity, signal-to-noise ratio, and novel imaging schemes. Based on metasurfaces, the information density and stability of the quantum imaging system can be further improved. Here we experimentally demonstrate that two patterns, simultaneously and independently superimposed on a high-efficiency dielectric metasurface, can be remotely switched via polarization-entangled photon pairs. Furthermore, using the time-correlated property of entangled photon pairs, the information carried by quantum light can be remarkably discriminated from background noise. This work confirms that the phase manipulation of quantum light with metasurfaces has a huge potential in the field of quantum imaging, quantum state tomography, and also promises real-world quantum metasurface devices.

NIR band-pass filters for CMOS image sensors constructed with NIR absorbing dyes and plasmonic nanoparticles

Two NIR band-pass filters for CMOS image sensors are developed by incorporating NIR absorption dye and silver nanodisks simultaneously in a transparent polymer, one of which blocks the NIR near the wavelength of 750 nm and the other near 950 nm. They offer low NIR transmittance while maintaining high visible light transparency even at a thin film thickness of 500 nm. By superimposing the proposed NIR band-pass filters, an NIR cutoff filter with a thickness of 1 µm is formed that shields the NIR at wavelengths longer than 680 nm while remaining transparent in the visible range.

Broadband Achromatic Metalens in the Visible Light Spectrum Based on Fresnel Zone Spatial Multiplexing

Metalenses composed of a large number of subwavelength nanostructures provide the possibility for the miniaturization and integration of the optical system. Broadband polarization-insensitive achromatic metalenses in the visible light spectrum have attracted researchers because of their wide applications in optical integrated imaging. This paper proposes a polarization-insensitive achromatic metalens operating over a continuous bandwidth from 470 nm to 700 nm. The silicon nitride nanopillars of 488 nm and 632.8 nm are interleaved by Fresnel zone spatial multiplexing method, and the particle swarm algorithm is used to optimize the phase compensation. The maximum time-bandwidth product in the phase library is 17.63. The designed focal length can be maintained in the visible light range from 470 nm to 700 nm. The average focusing efficiency reaches 31.71%. The metalens can achieve broadband achromatization using only one shape of nanopillar, which is simple in design and easy to fabricate. The proposed metalens is expected to play an important role in microscopic imaging, cameras, and other fields.