Artificial Intelligence in Meta-optics

Deep Learning Empowered Parallelized Metasurface Computed Tomography Snapshot Spectral Imaging

Snapshot spectral imaging is an emerging technology for fast data acquisition in dynamic environments, capturing high-volume spatial-spectral information in a single snapshot. However, it suffers from bulky cascading optics and cannot be directly used in space-restricted scenarios such as endoscope-assisted brain microsurgery and real-time cellular tissue imaging. In this work, an ultracompact strategy of parallelized metasurface computed tomography empowered by generative deep learning is proposed, which can effectively reduce the optics volume in snapshot spectral imaging from cm3 scale to sub-mm3 scale while retaining high resolution and speed of imaging so that the above-mentioned pain point problem is well addressed. The system comprises seven multifunctional sub-metasurfaces simultaneously acquiring multi-angle spectral projection and integration information of the target, uses the system-calibrated point spread functions as wavelength and spatial position distributions, and incorporates a generative adversarial deep neural network for fast reconstruction of spatial-spectral multiplexed images. Experimental results show that single snapshot imaging can be achieved in 38 ms with a spectral resolution of 10 nm in the spectral range of 450-650 nm. This technique paves the way for snapshot spectral imaging integration into various highly miniaturized microscopy and endoscopic imaging systems in applications such as advanced medical diagnosis.

Algorithm in chemistry: molecular logic gate-based data protection

Data security is crucial for safeguarding the integrity, authenticity, and confidentiality of documents, currency, merchant labels, and other paper-based assets, which sequentially has a profound impact on personal privacy and even national security. High-security-level logic data protection paradigms are typically limited to software (digital circuits) and rarely applied to physical devices using stimuli-responsive materials (SRMs). The main reason is that most SRMs lack programmable and controllable switching behaviors. Traditional SRMs usually produce static, singular, and highly predictable signals in response to stimuli, restricting them to simple "BUFFER" or "INVERT" logic operations with a low security level. However, recent advancements in SRMs have collectively enabled dynamic, multidimensional, and less predictable output signals under external stimuli. This breakthrough paves the way for sophisticated encryption and anti-counterfeiting hardware based on SRMs with complicated logic operations and algorithms. This review focuses on SRM-based data protection, emphasizing the integration of intricate logic and algorithms in SRM-constructed hardware, rather than chemical or material structural evolutions. It also discusses current challenges and explores the future directions of the field-such as combining SRMs with artificial intelligence (AI). This review fills a gap in the existing literature and represents a pioneering step into the uncharted territory of SRM-based encryption and anti-counterfeiting technologies.

Inverse Design of an All-Dielectric Nonlinear Polaritonic Metasurface

Nonlinear metasurfaces offer the ability to realize optical nonlinear devices with unparalleled properties compared to nonlinear crystals, due to the interplay between photonic resonances and materials properties. The complicated interdependency between efficiency and emission directionality of the nonlinear optical signal on the existence, localization, and lifetimes of photonic resonances, as well as on the nonlinear susceptibility, makes it extremely difficult to design optimal metasurfaces using conventional materials and geometries. Inverse design using topology optimization is a powerful design tool for photonic structures, but traditional approaches developed for linear photonics are not suitable for such high dimensional nonlinear problems. Here, we use a topology optimization approach to inverse-design a fabrication-robust nonlinear metasurface that includes quantum-engineered resonant nonlinearities in semiconductor heterostructures for efficient and directional second harmonic generation. Furthermore, we also demonstrate that under practical constraints, among all the parameters, the nonlinear modal overlap emerges as the dominant parameter that enhances conversion efficiency, a finding that contrasts with intuition-driven studies that often emphasize Purcell enhancement. Our results demonstrate an efficient approach for optimizing nonlinear processes in nanophotonic structures for classical and quantum light sources, quantum information applications, and communication.

All-optical analog differential operation and information processing empowered by meta-devices

The burgeoning demand for high-performance computing, robust data processing, and rapid growth of big data necessitates the emergence of novel optical devices to efficiently execute demanding computational processes. The field of meta-devices, such as metamaterial or metasurface, has experienced unprecedented growth over the past two decades. By manipulating the amplitude, phase, polarization, and dispersion of light wavefronts in spatial, spectral, and temporal domains, viable solutions for the implementation of all-optical analog computation and information processing have been provided. In this review, we summarize the latest developments and emerging trends of computational meta-devices as innovative platforms for spatial optical analog differentiators and information processing. Based on the general concepts of spatial Fourier transform and Green's function, we analyze the physical mechanisms of meta-devices in the application of amplitude differentiation, phase differentiation, and temporal differentiation and summarize their applications in image edge detection, image edge enhancement, and beam shaping. Finally, we explore the current challenges and potential solutions in optical analog differentiators and provide perspectives on future research directions and possible developments.

Dielectric metasurfaces based on a phase singularity in the region of high reflectance

Metasurfaces, two-dimensional planar optical devices based on subwavelength-scale structures, have garnered significant attention for their potential to replace conventional optical components in various fields. These devices can manipulate the amplitude, phase, and polarization of light in versatile ways, offering complex functionalities within a single, space-efficient device. However, enhancing their functionality remains a challenge, requiring an expansion in the design flexibility of the structural elements, known as meta-atoms. In this study, we revealed that by varying the two independent lengths of the cross-shaped structure at a wavelength of 980 nm, a phase singularity exists in the region of high reflection. In addition, we found that the phase of transmitted light can be modulated from 0 to 2π by encircling this singularity. Based on the identified phase singularity, we designed and fabricated a polarization-independent metalens with varying numerical apertures to experimentally validate the feasibility of high-reflectivity transmissive wavefront engineering metasurfaces. The introduced meta-atoms based on a phase singularity are expected to open new avenues for applications, such as those requiring light attenuation and concentration simultaneously or the development of resonant cavity structures capable of beam modulation.

Beating spectral bandwidth limits for large aperture broadband nano-optics

Flat optics have been proposed as an attractive approach for the implementation of new imaging and sensing modalities to replace and augment refractive optics. However, chromatic aberrations impose fundamental limitations on diffractive flat optics. As such, true broadband high-quality imaging has thus far been out of reach for fast f-numbers, large aperture, flat optics. In this work, we overcome intrinsic spectral bandwidth limitations, achieving broadband imaging in the visible wavelength range with a flat meta-optic, co-designed with computational reconstruction. We derive the necessary conditions for a broadband, 1 cm aperture, f/2 flat optic, with a diagonal field of view of 30° and average system MTF contrast of 20% or larger for a spatial frequency of 100 lp/mm in the visible band (>30% for <70 lp/mm). Finally, we use a coaxial, dual-aperture system to train the broadband imaging meta-optic with a learned reconstruction method operating on pair-wise captured imaging data. Fundamentally, our work challenges the entrenched belief of the inability of capturing high-quality, full-color images using a single large aperture meta-optic.

Enabling beam-scanning antenna technologies for terahertz wireless systems: A review

Due to the exponentially growing global mobile data of wireless communications evolving from 5 G to 6 G in recent years, research activities of leveraging terahertz (THz) waves to obtain larger channel capacities have shown an ever-increasing pace and reached an unprecedented height than before. Historically, the past few decades have already witnessed much progress in THz generation and detection technologies, which have been recognized for a long time as the bottleneck preventing the THz waves from being tamed by human beings. However, the importance of developing advanced components such as antennas, transmission lines, filters, power amplimers, etc., which constitute the basic building blocks of a THz wireless system, should not be overlooked for the sake of exploiting the THz spectra for future advanced wireless communications, sensing and imaging applications. While producing a scannable highly-directive antenna beam proves to be indispensable in the period of microwaves, the significance of such functionality is more critical in the THz era, considering that THz waves have more intractable challenges such as the severity of free-space propagation losses, the susceptibility to atmospheric environments, and the unavailability of efficient signal sources. This article is structured under this background, which is dedicated to reviewing several enabling beam-scanning antenna concepts, structures, and architectures that have been developed for THz wireless systems. Specifically, we divide these THz beam-scanning solutions into four basic groups based on different mechanisms, i.e., mechanical motion, phased array, frequency beam-scanning, and reconfigurable metasurfaces.

Deep-Ultraviolet AlN Metalens with Imaging and Ultrafast Laser Microfabrication Applications

Deep-ultraviolet (DUV) light is essential for applications including fabrication, molecular research, and biomedical imaging. Compact metalenses have the potential to drive further innovation in these fields, provided they utilize a material platform that is cost-effective, durable, and scalable. In this work, we present aluminum nitride (AlN) metalenses as an efficient solution for DUV applications. These metalenses, with a thickness of only 380 nm, deliver DUV focusing and imaging capabilities close to the theoretical diffraction limit. Leveraging their robustness to intense ultrafast laser irradiation, we demonstrate successful DUV ultrafast laser direct writing of microstructures on a polymer film and silicon substrate. These results underscore the significant promise of advancing photonic technologies in this critical wavelength regime.

Progress on intelligent metasurfaces for signal relay, transmitter, and processor

Pursuing higher data rate with limited spectral resources is a longstanding topic that has triggered the fast growth of modern wireless communication techniques. However, the massive deployment of active nodes to compensate for propagation loss necessitates high hardware expenditure, energy consumption, and maintenance cost, as well as complicated network interference issues. Intelligent metasurfaces, composed of a number of subwavelength passive or active meta-atoms, have recently found to be a new paradigm to actively reshape wireless communication environment in a green way, distinct from conventional works that passively adapt to the surrounding. In this review, we offer a unified perspective on how intelligent metasurfaces can facilitate wireless communication in three manners: signal relay, signal transmitter, and signal processor. We start by the basic modeling of wireless channel and the evolution of metasurfaces from passive, active to intelligent metasurfaces. Integrated with various deep learning algorithms, intelligent metasurfaces adapt to cater for the ever-changing environments without human intervention. Then, we overview specific experimental advancements using intelligent metasurfaces. We conclude by identifying key issues in the practical implementations of intelligent metasurfaces, and surveying new directions, such as gain metasurfaces and knowledge migration.

Advanced Quantitative Phase Microscopy Achieved with Spatial Multiplexing and a Metasurface

Quantitative optical phase information provides an alternative method to observe biomedical properties, where conventional phase imaging fails. Phase retrieval typically requires multiple intensity measurements and iterative computations to ensure uniqueness and robustness against detection noise. To increase the measurement speed, we propose a single-shot quantitative phase imaging method with metasurface optics that can be conveniently integrated into conventional imaging systems with minimal modification. The improvement of the measurement speed is simultaneously made possible by combining deep learning with the transport-of-intensity equation. As a proof-of-concept, we demonstrate phase retrieval on both calibrated phase objects and biological specimens by using an imaging system integrated with our metasurface. When combined with the matched neural network, the system yields result with errors as low as 5% and increased space-bandwidth-product. A multitude of commercial applications can benefit from the compactness and rapid implementation of our proposed method.

Cornu-Spiral-Like Organic Crystal Waveguide Providing Discriminatory Optical Pathway for Smart Organic Photonic Circuit

In the era of artificial intelligence, developing advanced and intelligent photonic circuits has become essential. In this work, the fabrication of a smart organic photonic circuit (OPC), is illustrated which utilizes a Cornu-spiral-like waveguide (CSW) to produce discriminating optical pathways in the circuit. The mechanical flexibility of Schiff base, (E)-1-(((5-iodopyridin-2-yl)imino)methyl)naphthalen-2-ol (IPyIN) facilitates the fabrication of a first-of-its-kind, two-ring-based CSW via the atomic force microscopy cantilever tip-assisted mechanophotonics approach. The photonic studies suggest that the CSW structure routes optical signals in discriminating trajectories. To capitalize on the discriminatory properties of the CSW, two linear waveguides are integrated onto both rings of the CSW to create a smart OPC. This smart OPC can selectively route photons depending on the pathways determined by the CSW to switch it ON or OFF completely depending on partial or complete flow of optical signals in the circuit. Such intelligent photonic circuits are essential for advancing smart technologies.

Nonlocal Huygens' meta-lens for high-quality-factor spin-multiplexing imaging

Combining bright-field and edge-enhanced imaging affords an effective avenue for extracting complex morphological information from objects, which is particularly beneficial for biological imaging. Multiplexing meta-lenses present promising candidates for achieving this functionality. However, current multiplexing meta-lenses lack spectral modulation, and crosstalk between different wavelengths hampers the imaging quality, especially for biological samples requiring precise wavelength specificity. Here, we experimentally demonstrate the nonlocal Huygens' meta-lens for high-quality-factor spin-multiplexing imaging. Quasi-bound states in the continuum (q-BICs) are excited to provide a high quality factor of 90 and incident-angle dependence. The generalized Kerker condition, driven by Fano-like interactions between q-BIC and in-plane Mie resonances, breaks the radiation symmetry, resulting in a transmission peak with a geometric phase for polarization-converted light, while unconverted light exhibits a transmission dip without a geometric phase. Enhanced polarization conversion efficiency of 65% is achieved, accompanied by a minimal unconverted value, surpassing the theoretical limit of traditional thin nonlocal metasurfaces. Leveraging these effects, the output polarization-converted state exhibits an efficient wavelength-selective focusing phase profile. The unconverted counterpart serves as an effective spatial frequency filter based on incident-angular dispersion, passing high-frequency edge details. Bright-field imaging and edge detection are thus presented under two output spin states. This work provides a versatile framework for nonlocal metasurfaces, boosting biomedical imaging and sensing applications.

A guidance to intelligent metamaterials and metamaterials intelligence

The bidirectional interactions between metamaterials and artificial intelligence have recently attracted immense interest to motivate scientists to revisit respective communities, giving rise to the proliferation of intelligent metamaterials and metamaterials intelligence. Owning to the strong nonlinear fitting and generalization ability, artificial intelligence is poised to serve as a materials-savvy surrogate electromagnetic simulator and a high-speed computing nucleus that drives numerous self-driving metamaterial applications, such as invisibility cloak, imaging, detection, and wireless communication. In turn, metamaterials create a versatile electromagnetic manipulator for wave-based analogue computing to be complementary with conventional electronic computing. In this Review, we stand from a unified perspective to review the recent advancements in these two nascent fields. For intelligent metamaterials, we discuss how artificial intelligence, exemplified by deep learning, streamline the photonic design, foster independent working manner, and unearth latent physics. For metamaterials intelligence, we particularly unfold three canonical categories, i.e., wave-based neural network, mathematical operation, and logic operation, all of which directly execute computation, detection, and inference task in physical space. Finally, future challenges and perspectives are pinpointed, including data curation, knowledge migration, and imminent practice-oriented issues, with a great vision of ushering in the free management of entire electromagnetic space.

Optical Metasurfaces for Biomedical Imaging and Sensing

Optical metasurfaces, arrays of nanostructures engineered to manipulate light, have emerged as a transformative technology in both research and industry due to their compact design and exceptional light control capabilities. Their strong light-matter interactions enable precise wavefront modulation, polarization control, and significant near-field enhancements. These unique properties have recently driven their application in biomedical fields. In particular, metasurfaces have led to breakthroughs in biomedical imaging technologies, such as achromatic imaging, phase imaging, and extended depth-of-focus imaging. They have also advanced cutting-edge biosensing technologies, featuring high-quality factor resonators and near-field enhancements. As the demand for device miniaturization and system integration increases, metasurfaces are expected to play a pivotal role in the development of next-generation biomedical devices. In this review, we explore the latest advancements in the use of metasurfaces for biomedical applications, with a particular focus on imaging and sensing. Additionally, we discuss future directions aimed at transforming the biomedical field by leveraging the full potential of metasurfaces to provide compact, high-performance solutions for a wide range of applications.

Programmable nonlinear optical neuromorphic computing with bare 2D material MoS2

Nonlinear optical responses in two-dimensional (2D) materials can build free-space optical neuromorphic computing systems. Ensuring the high performance and the tunability of the system is essential to encode diverse functions. However, common strategies, including the integration of external electrode arrays or photonic structures with 2D materials, and barely patterned 2D materials, exhibit a contradiction between performance and tunability. Because the unique band dispersions of 2D materials can provide hidden paths to boost nonlinear responses independently, here we introduced a new free-space optical computing concept within a bare molybdenum disulfide array. This system can preserve high modulation performance with fast speed, low energy consumption, and high signal-to-noise ratio. Due to the freedom from the restrictions of fixed photonic structures, the tunability is also enhanced through the synergistic encodings of the 2D cells and the excitation pulses. The computing mechanism of transition from two-photon absorption to synergistic excited states absorption intrinsically improved the modulation capability of nonlinear optical responses, revealed from the relative transmittance modulated by a pump-probe-control strategy. Optical artificial neural network (ANN) and digital processing were demonstrated, revealing the feasibility of the free-space optical computing based on bare 2D materials toward neuromorphic applications.

Nanomaterials for Flexible Neuromorphics

The quest to imbue machines with intelligence akin to that of humans, through the development of adaptable neuromorphic devices and the creation of artificial neural systems, has long stood as a pivotal goal in both scientific inquiry and industrial advancement. Recent advancements in flexible neuromorphic electronics primarily rely on nanomaterials and polymers owing to their inherent uniformity, superior mechanical and electrical capabilities, and versatile functionalities. However, this field is still in its nascent stage, necessitating continuous efforts in materials innovation and device/system design. Therefore, it is imperative to conduct an extensive and comprehensive analysis to summarize current progress. This review highlights the advancements and applications of flexible neuromorphics, involving inorganic nanomaterials (zero-/one-/two-dimensional, and heterostructure), carbon-based nanomaterials such as carbon nanotubes (CNTs) and graphene, and polymers. Additionally, a comprehensive comparison and summary of the structural compositions, design strategies, key performance, and significant applications of these devices are provided. Furthermore, the challenges and future directions pertaining to materials/devices/systems associated with flexible neuromorphics are also addressed. The aim of this review is to shed light on the rapidly growing field of flexible neuromorphics, attract experts from diverse disciplines (e.g., electronics, materials science, neurobiology), and foster further innovation for its accelerated development.

On-Demand Design of Metasurfaces through Multineural Network Fusion

In this paper, a multineural network fusion freestyle metasurface on-demand design method is proposed. The on-demand design method involves rapidly generating corresponding metasurface patterns based on the user-defined spectrum. The generated patterns are then input into a simulator to predict their corresponding S-parameter spectrogram, which is subsequently analyzed against the real S-parameter spectrogram to verify whether the generated metasurface patterns meet the desired requirements. The methodology is based on three neural network models: a Wasserstein Generative Adversarial Network model with a U-net architecture (U-WGAN) for inverse structural design, a Variational Autoencoder (VAE) model for compression, and an LSTM + Attention model for forward S-parameter spectrum prediction validation. The U-WGAN is utilized for on-demand reverse structural design, aiming to rapidly discover high-fidelity metasurface patterns that meet specific electromagnetic spectrum responses. The VAE, as a probabilistic generation model, serves as a bridge, mapping input data to latent space and transforming it into latent variable data, providing crucial input for a forward S-parameter spectrum prediction model. The LSTM + Attention network, acting as a forward S-parameter spectrum prediction model, can accurately and efficiently predict the S-parameter spectrum corresponding to the latent variable data and compare it with the real spectrum. In addition, the digits "0" and "1" are used in the design to represent vacuum and metallic materials, respectively, and a 10 × 10 cell array of freestyle metasurface patterns is constructed. The significance of the research method proposed in this paper lies in the following: (1) The freestyle metasurface design significantly expands the possibility of metamaterial design, enabling the creation of diverse metasurface structures that are difficult to achieve with traditional methods. (2) The on-demand design approach can generate high-fidelity metasurface patterns that meet the expected electromagnetic characteristics and responses. (3) The fusion of multiple neural networks demonstrates high flexibility, allowing for the adjustment of network structures and training methods based on specific design requirements and data characteristics, thus better accommodating different design problems and optimization objectives.

Radiationless optical modes in metasurfaces: recent progress and applications

Non-radiative optical modes attracted enormous attention in optics due to strong light confinement and giant Q-factor at its spectral position. The destructive interference of multipoles leads to zero net-radiation and strong field trapping. Such radiationless states disappear in the far-field, localize enhanced near-field and can be excited in nano-structures. On the other hand, the optical modes turn out to be completely confined due to no losses at discrete point in the radiation continuum, such states result in infinite Q-factor and lifetime. The radiationless states provide a suitable platform for enhanced light matter interaction, lasing, and boost nonlinear processes at the state regime. These modes are widely investigated in different material configurations for various applications in both linear and nonlinear metasurfaces which are briefly discussed in this review.

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.

Nonlocal meta-lens with Huygens' bound states in the continuum

Meta-lenses composed of artificial meta-atoms have stimulated substantial interest due to their compact and flexible wavefront shaping capabilities, outperforming bulk optical devices. The operating bandwidth is a critical factor determining the meta-lens' performance across various wavelengths. Meta-lenses that operate in a narrowband manner relying on nonlocal effects can effectively reduce disturbance and crosstalk from non-resonant wavelengths, making them well-suitable for specialized applications such as nonlinear generation and augmented reality/virtual reality display. However, nonlocal meta-lenses require striking a balance between local phase manipulation and nonlocal resonance excitation, which involves trade-offs among factors like quality-factor, efficiency, manipulation dimensions, and footprint. In this work, we experimentally demonstrate the nonlocal meta-lens featuring Huygens' bound states in the continuum (BICs) and its near-infrared imaging application. All-dielectric integrated-resonant unit is particularly optimized to efficiently induce both the quasi-BIC and generalized Kerker effect, while ensuring the rotation-angle robustness for generating geometric phase. The experimental results show that the single-layer nonlocal Huygens' meta-lens possesses a high quality-factor of 104 and achieves a transmission polarization conversion efficiency of 55%, exceeding the theoretical limit of 25%. The wavelength-selective two-dimensional focusing and imaging are demonstrated as well. This work will pave the way for efficient nonlocal wavefront shaping and meta-devices.

Large-scale photonic inverse design: computational challenges and breakthroughs

Recent advancements in inverse design approaches, exemplified by their large-scale optimization of all geometrical degrees of freedom, have provided a significant paradigm shift in photonic design. However, these innovative strategies still require full-wave Maxwell solutions to compute the gradients concerning the desired figure of merit, imposing, prohibitive computational demands on conventional computing platforms. This review analyzes the computational challenges associated with the design of large-scale photonic structures. It delves into the adequacy of various electromagnetic solvers for large-scale designs, from conventional to neural network-based solvers, and discusses their suitability and limitations. Furthermore, this review evaluates the research on optimization techniques, analyzes their advantages and disadvantages in large-scale applications, and sheds light on cutting-edge studies that combine neural networks with inverse design for large-scale applications. Through this comprehensive examination, this review aims to provide insights into navigating the landscape of large-scale design and advocate for strategic advancements in optimization methods, solver selection, and the integration of neural networks to overcome computational barriers, thereby guiding future advancements in large-scale photonic design.

Monocular Metasurface for Structured Light Generation and 3D Imaging with a Large Field-of-View

Structured light three-dimensional (3D) imaging technology captures the geometric information on 3D objects by recording waves reflected from the objects' surface. The projection angle and point number of the laser dots directly determine the field-of-view (FOV) and the resolution of the reconstructed image. Conventionally, diffractive optical elements with micrometer-scale pixel size have been used to generate laser dot arrays, leading to limited FOV and point number within the projection optical path. Here, we theoretically put forward and experimentally demonstrate a monocular geometric phase metasurface composed of deep subwavelength meta-atoms to generate a 10 798 dot array within an FOV of 163°. Attributed to the vast number and high-density point cloud generated by the metasurface, the 3D reconstructed results showcase a maximum relative error in depth of 5.3 mm and a reconstruction error of 6.07%. Additionally, we propose a spin-multiplexed metasurface design method capable of doubling the number of lattice points. We demonstrate its application in the field of 3D imaging through experiments, where the 3D reconstructed results show a maximum relative depth error of 0.44 cm and a reconstruction error of 2.78%. Our proposed metasurface featuring advanced point cloud generation holds substantial potential for various applications such as facial recognition, autonomous driving, virtual reality, and beyond.

A perspective on the artificial intelligence's transformative role in advancing diffractive optics

Artificial intelligence (AI) is transforming diffractive optics development through its advanced capabilities in design optimization, pattern generation, fabrication enhancement, performance forecasting, and customization. Utilizing AI algorithms like machine learning, generative models, and transformers, researchers can analyze extensive datasets to refine the design of diffractive optical elements (DOEs) tailored to specific applications and performance requirements. AI-driven pattern generation methods enable the creation of intricate and efficient optical structures that manipulate light with exceptional precision. Furthermore, AI optimizes manufacturing processes by fine-tuning fabrication parameters, resulting in higher quality and productivity. AI models also simulate diffractive optics behavior, accelerating design iterations and facilitating rapid prototyping. This integration of AI into diffractive optics holds tremendous potential to revolutionize optical technology applications across diverse sectors, spanning from imaging and sensing to telecommunications and beyond.

Enhancing metasurface fabricability through minimum feature size enforcement

The metasurfaces have shown great potential for miniaturizing conventional optics while offering extended flexibility. Recently, there has been considerable interest in using algorithms to generate meta-atom shapes for these metasurfaces, as they offer vast design freedom and not biased by the human intuition. However, these complex designs significantly increase the difficulty of fabrication. To address this, we introduce a design process that rigorously enforces the fabricability of both the material-filled (fill) and empty (void) regions in a metasurface design. This process takes into account specific constraints regarding the minimum feature size for each region. Additionally, it corrects any violations of these constraints across the entire device, ensuring only minimal impact on performance. Our method provides a practical way to create metasurface designs that are easy to fabricate, even with complex shapes, hence improving the overall production yield of these advanced meta-optical components.

AI-Based Metamaterial Design

The use of metamaterials in various devices has revolutionized applications in optics, healthcare, acoustics, and power systems. Advancements in these fields demand novel or superior metamaterials that can demonstrate targeted control of electromagnetic, mechanical, and thermal properties of matter. Traditional design systems and methods often require manual manipulations which is time-consuming and resource intensive. The integration of artificial intelligence (AI) in optimizing metamaterial design can be employed to explore variant disciplines and address bottlenecks in design. AI-based metamaterial design can also enable the development of novel metamaterials by optimizing design parameters that cannot be achieved using traditional methods. The application of AI can be leveraged to accelerate the analysis of vast data sets as well as to better utilize limited data sets via generative models. This review covers the transformative impact of AI and AI-based metamaterial design for optics, acoustics, healthcare, and power systems. The current challenges, emerging fields, future directions, and bottlenecks within each domain are discussed.

Non-Invasive Self-Adaptive Information States' Acquisition inside Dynamic Scattering Spaces

Pushing the information states' acquisition efficiency has been a long-held goal to reach the measurement precision limit inside scattering spaces. Recent studies have indicated that maximal information states can be attained through engineered modes; however, partial intrusion is generally required. While non-invasive designs have been substantially explored across diverse physical scenarios, the non-invasive acquisition of information states inside dynamic scattering spaces remains challenging due to the intractable non-unique mapping problem, particularly in the context of multi-target scenarios. Here, we establish the feasibility of non-invasive information states' acquisition experimentally for the first time by introducing a tandem-generated adversarial network framework inside dynamic scattering spaces. To illustrate the framework's efficacy, we demonstrate that efficient information states' acquisition for multi-target scenarios can achieve the Fisher information limit solely through the utilization of the external scattering matrix of the system. Our work provides insightful perspectives for precise measurements inside dynamic complex systems.

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.

Research on underwater robot ranging technology based on semantic segmentation and binocular vision

Based on the principle of light refraction and binocular ranging, the underwater imaging model is obtained. It provides a theoretical basis for underwater camera calibration. In order to meet the requirement of underwater vehicle to identify and distance underwater target, a new underwater vehicle distance measurement system based on semantic segmentation and binocular vision is proposed. The system uses Deeplabv3 + to identify the underwater target captured by the binocular camera and generate the target map, which is then used for binocular ranging. Compared with the binocular ranging using the original drawing, the measurement accuracy of the proposed method has not changed, the measurement speed is increased by 30%, and the error rate is controlled within 5%, which meets the needs of underwater robot operations.

In Vivo Intelligent Fluorescence Endo-Microscopy by Varifocal Meta-Device and Deep Learning

Endo-microscopy is crucial for real-time 3D visualization of internal tissues and subcellular structures. Conventional methods rely on axial movement of optical components for precise focus adjustment, limiting miniaturization and complicating procedures. Meta-device, composed of artificial nanostructures, is an emerging optical flat device that can freely manipulate the phase and amplitude of light. Here, an intelligent fluorescence endo-microscope is developed based on varifocal meta-lens and deep learning (DL). The breakthrough enables in vivo 3D imaging of mouse brains, where varifocal meta-lens focal length adjusts through relative rotation angle. The system offers key advantages such as invariant magnification, a large field-of-view, and optical sectioning at a maximum focal length tuning range of ≈2 mm with 3 µm lateral resolution. Using a DL network, image acquisition time and system complexity are significantly reduced, and in vivo high-resolution brain images of detailed vessels and surrounding perivascular space are clearly observed within 0.1 s (≈50 times faster). The approach will benefit various surgical procedures, such as gastrointestinal biopsies, neural imaging, brain surgery, etc.

Nonlocal metasurface for dark-field edge emission

Nonlocal effects originating from interactions between neighboring meta-atoms introduce additional degrees of freedom for peculiar characteristics of metadevices, such as enhancement, selectivity, and spatial modulation. However, they are generally difficult to manipulate because of the collective responses of multiple meta-atoms. Here, we experimentally demonstrate the nonlocal metasurface to realize the spatial modulation of dark-field emission. Plasmonic asymmetric split rings (ASRs) are designed to simultaneously excite local dipole resonance and nonlocal quasi-bound states in the continuum and spatially extended modes. With one type of unit, nonlocal effects are tailored by varying array periods. ASRs at the metasurface's edge lack sufficient interactions, resulting in stronger dark-field scattering and thus edge emission properties of the metasurface. Pixel-level spatial control is demonstrated by simply erasing some units, providing more flexibility than conventional local metasurfaces. This work paves the way for manipulating nonlocal effects and facilitates applications in optical trapping and sorting at the nanoscale.

Machine Learning Aided Design and Optimization of Thermal Metamaterials

Artificial Intelligence (AI) has advanced material research that were previously intractable, for example, the machine learning (ML) has been able to predict some unprecedented thermal properties. In this review, we first elucidate the methodologies underpinning discriminative and generative models, as well as the paradigm of optimization approaches. Then, we present a series of case studies showcasing the application of machine learning in thermal metamaterial design. Finally, we give a brief discussion on the challenges and opportunities in this fast developing field. In particular, this review provides: (1) Optimization of thermal metamaterials using optimization algorithms to achieve specific target properties. (2) Integration of discriminative models with optimization algorithms to enhance computational efficiency. (3) Generative models for the structural design and optimization of thermal metamaterials.

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.

Programmable optical meta-holograms

The metaverse has captured significant attention as it provides a virtual realm that cannot be experienced in the physical world. Programmable optical holograms, integral components of the metaverse, allow users to access diverse information without needing external equipment. Meta-devices composed of artificially customized nano-antennas are excellent candidates for programmable optical holograms due to their compact footprint and flexible electromagnetic manipulation. Programmable optical meta-holograms can dynamically alter reconstructed images in real-time by directly modulating the optical properties of the metasurface or by modifying the incident light. Information can be encoded across multiple channels and freely selected through switchable functionality. These advantages will broaden the range of virtual scenarios in the metaverse, facilitating further development and practical applications. This review concentrates on recent advancements in the fundamentals and applications of programmable optical meta-holograms. We aim to provide readers with general knowledge and potential inspiration for applying programmable optical meta-holograms, both intrinsic and external ways, into the metaverse for better performance. An outlook and perspective on the challenges and prospects in these rapidly growing research areas are provided.

Matte surfaces with broadband transparency enabled by highly asymmetric diffusion of white light

The long-standing paradox between matte appearance and transparency has deprived traditional matte materials of optical transparency. Here, we present a solution to this centuries-old optical conundrum by harnessing the potential of disordered optical metasurfaces. Through the construction of a random array of meta-atoms tailored in asymmetric backgrounds, we have created transparent matte surfaces that maintain clear transparency regardless of the strength of disordered light scattering or their matte appearances. This remarkable property originates in the achievement of highly asymmetric light diffusion, exhibiting substantial diffusion in reflection and negligible diffusion in transmission across the entire visible spectrum. By fabricating macroscopic samples of such metasurfaces through industrial lithography, we have experimentally demonstrated transparent windows camouflaged as traditional matte materials, as well as transparent displays with high clarity, full color, and one-way visibility. Our work introduces an unprecedented frontier of transparent matte materials in optics, offering unprecedented opportunities and applications.

Tailoring full-Stokes thermal emission from twisted-gratings structures

Polarized thermal emission finds extensive applications in remote sensing, landmine detection, and target detection. In applications such as ellipsometry and biomedical analysis, the generation of emission with controllable polarization is preferred. It is desired to manipulate the polarization state over the full Stokes parameters. While numerous studies have demonstrated either linear or circular polarization control using metamaterials, full-Stokes thermal emission has not been explored. Here, a microstructure based on two layers of silicon carbide gratings is proposed to tailor the polarization state of thermal emission, covering the full-Stokes parameter range. The bilayer twisted-gratings structure breaks mirror symmetry. Wave interference at the interfaces and diffraction by the gratings enhance the emission dichroism, resulting in almost completely polarized emission. By adjusting the twist angle between the gratings, the polarization state can be continuously tuned from linear to circular, nearly covering the entire surface of Poincaré sphere. This study provides a design for tailoring full-Stokes emission with notable advantages over other plasmonic metasurfaces.

Broadband and large-aperture metasurface edge encoders for incoherent infrared radiation

The prevalence of computer vision systems necessitates hardware-based approaches to relieve the high computational demand of deep neural networks in resource-limited applications. One solution would be to off-load low-level image feature extraction, such as edge detection, from the digital network to the analog imaging system. To that end, this work demonstrates incoherent, broadband, low-noise optical edge detection of real-world scenes by combining the wavefront shaping of a 24-mm aperture metasurface with a refractive lens. An inverse design approach is used to optimize the metasurface for Laplacian-based edge detection across the 7.5- to 13.5-μm LWIR imaging band, allowing for facile integration with uncooled microbolometer-based LWIR imagers to encode edge information. A polarization multiplexed approach leveraging a birefringent metasurface is also demonstrated as a single-aperture implementation. This work could be applied to improve computer vision capabilities of resource-constrained systems by leveraging optical preprocessing to alleviate the computational requirements for high-accuracy image segmentation and classification.

Optical computing metasurfaces: applications and advances

Integrated photonic devices and artificial intelligence have presented a significant opportunity for the advancement of optical computing in practical applications. Optical computing technology is a unique computing system based on optical devices and computing functions, which significantly differs from the traditional electronic computing technology. On the other hand, optical computing technology offers the advantages such as fast speed, low energy consumption, and high parallelism. Yet there are still challenges such as device integration and portability. In the burgeoning development of micro-nano optics technology, especially the deeply ingrained concept of metasurface technique, it provides an advanced platform for optical computing applications, including edge detection, image or motion recognition, logic computation, and on-chip optical computing. With the aim of providing a comprehensive introduction and perspective for optical computing metasurface applications, we review the recent research advances of optical computing, from nanostructure and computing methods to practical applications. In this work, we review the challenges and analysis of optical computing metasurfaces in engineering field and look forward to the future development trends of optical computing.

Intelligent Phase Contrast Meta-Microscope System

Phase contrast imaging techniques enable the visualization of disparities in the refractive index among various materials. However, these techniques usually come with a cost: the need for bulky, inflexible, and complicated configurations. Here, we propose and experimentally demonstrate an ultracompact meta-microscope, a novel imaging platform designed to accomplish both optical and digital phase contrast imaging. The optical phase contrast imaging system is composed of a pair of metalenses and an intermediate spiral phase metasurface located at the Fourier plane. The performance of the system in generating edge-enhanced images is validated by imaging a variety of human cells, including lung cell lines BEAS-2B, CLY1, and H1299 and other types. Additionally, we integrate the ResNet deep learning model into the meta-microscope to transform bright-field images into edge-enhanced images with high contrast accuracy. This technology promises to aid in the development of innovative miniature optical systems for biomedical and clinical applications.

3D-printed multilayer structures for high-numerical aperture achromatic metalenses

Flat optics consisting of nanostructures of high-refractive index materials produce lenses with thin form factors that tend to operate only at specific wavelengths. Recent attempts to achieve achromatic lenses uncover a trade-off between the numerical aperture (NA) and bandwidth, which limits performance. Here, we propose a new approach to design high-NA, broadband, and polarization-insensitive multilayer achromatic metalenses (MAMs). We combine topology optimization and full-wave simulations to inversely design MAMs and fabricate the structures in low-refractive index materials by two-photon polymerization lithography. MAMs measuring 20 μm in diameter operating in the visible range of 400 to 800 nm with 0.5 and 0.7 NA were achieved with efficiencies of up to 42%. We demonstrate broadband imaging performance of the fabricated MAM under white light and RGB narrowband illuminations. These results highlight the potential of the 3D-printed multilayer structures for realizing broadband and multifunctional meta-devices with inverse design.

Flexible Thin Film Functionalized by Initiative Dust Removal and Anti-Fogging for Optical Device Applications

The deposition of dust and condensation of fog will block the scattering and transmission of light, thus affecting the performance of optical devices. In this work, flexible polyethylene terephthalate (PET) foil functionalized by active dust removal and anti-fogging characteristics is realized which combines electrodynamic screen (EDS) and electro-heating devices. In lieu of traditional measurement methods of dust removal efficiency, the PSNR is employed to characterize the dust removal efficiency of the film for the first time. The results show that both dust removal and anti-fogging improve the image quality, in which the dust removal increases the PSNR from 28.1 dB to 34.2 dB and the anti-fogging function realizes a film temperature rise of 16.7 ∘C in 5 min, reaching a maximum of 41.3 ∘C. According to the high sensitivity of the PSNR, we propose a fully automatic CIS film-driven algorithm, and its feasibility has been demonstrated.

Varifocal Metalenses: Harnessing Polarization-Dependent Superposition for Continuous Focal Length Control

Traditional varifocal lenses are bulky and mechanically complex. Emerging active metalenses promise compactness and design flexibility but face issues like mechanical tuning reliability and nonlinear focal length tuning due to additional medium requirements. In this work, we propose a varifocal metalens design based on superimposing light intensity distributions from two orthogonal polarization states. This approach enables continuous and precise focal length control within the visible spectrum, while maintaining relatively high focusing efficiencies (∼41% in simulation and ∼28% in measurement) and quality. In experimental validation, the metalens exhibited flexible tunability, with the focal length continuously adjustable between two spatial positions upon variation of the incident polarization angle. The MTF results showed high contrast reproduction and sharp imaging, with a Strehl ratio of >0.7 for all polarization angles. With compactness, design flexibility, and high focusing quality, the proposed varifocal metalens holds potential for diverse applications, advancing adaptive and versatile optical devices.

Deep learning enhanced achromatic imaging with a singlet flat lens

Correction of chromatic aberration is an important issue in color imaging and display. However, realizing broadband achromatic imaging by a singlet lens with high comprehensive performance still remains challenging, though many achromatic flat lenses have been reported recently. Here, we propose a deep-learning-enhanced singlet planar imaging system, implemented by a 3 mm-diameter achromatic flat lens, to achieve relatively high-quality achromatic imaging in the visible. By utilizing a multi-scale convolutional neural network (CNN) imposed to an achromatic multi-level diffractive lens (AMDL), the white light imaging qualities are significantly improved in both indoor and outdoor scenarios. Our experiments are fulfilled via a large paired imaging dataset with respect to a 3 mm-diameter AMDL, which guaranteed with achromatism in a broad wavelength range (400-1100 nm) but a relative low efficiency (∼45%). After our CNN enhancement, the imaging qualities are improved by ∼2 dB, showing competitive achromatic and high-quality imaging with a singlet lens for practical applications.

Meticulous research for design of plasmonics sensors for cancer detection and food contaminants analysis via machine learning and artificial intelligence

Cancer is one of the leading causes of death worldwide, making early detection and accurate diagnosis critical for effective treatment and improved patient outcomes. In recent years, machine learning (ML) has emerged as a powerful tool for cancer detection, enabling the development of innovative algorithms that can analyze vast amounts of data and provide accurate predictions. This review paper aims to provide a comprehensive overview of the various ML algorithms and techniques employed for cancer detection, highlighting recent advancements, challenges, and future directions in this field. The main challenge is finding a safe, auditable and reliable analysis method for fundamental scientific publication. Food contaminant analysis is a process of testing food products to identify and quantify the presence of harmful substances or contaminants. These substances can include bacteria, viruses, toxins, pesticides, heavy metals, allergens, and other chemical residues. Machine learning (ML) and artificial intelligence (A.I) proposed as a promising method that possesses excellent potential to extract information with high validity that may be overlooked with conventional analysis techniques and for its capability in a wide range of investigations. A.I technology used in meta-optics can develop optical devices and systems to a higher level in future. Furthermore (M.L.) and (A.I.) play key roles as a health Approach for nano materials NMs safety assessment in environment and human health research. Beside, benefits of ML in design of plasmonic sensors for different applications with improved resolution and detection are convinced.

Miniature Two-Photon Microscopic Imaging Using Dielectric Metalens

Miniature two-photon microscopy has emerged as a powerful technique for investigating brain activity in freely moving animals. Ongoing research objectives include reducing probe weight and minimizing animal behavior constraints caused by probe attachment. Employing dielectric metalenses, which enable the use of sizable optical components in flat device structures while maintaining imaging resolution, is a promising solution for addressing these challenges. In this study, we designed and fabricated a titanium dioxide metalens with a wavelength of 920 nm and a high aspect ratio. Furthermore, a meta-optic two-photon microscope weighing 1.36 g was developed. This meta-optic probe has a lateral resolution of 0.92 μm and an axial resolution of 18.08 μm. Experimentally, two-photon imaging of mouse brain structures in vivo was also demonstrated. The flat dielectric metalens technique holds promising opportunities for high-performance integrated miniature nonlinear microscopy and endomicroscopy platforms in the biomedical field.

Research Progress in Surface-Enhanced Infrared Absorption Spectroscopy: From Performance Optimization, Sensing Applications, to System Integration

Infrared absorption spectroscopy is an effective tool for the detection and identification of molecules. However, its application is limited by the low infrared absorption cross-section of the molecule, resulting in low sensitivity and a poor signal-to-noise ratio. Surface-Enhanced Infrared Absorption (SEIRA) spectroscopy is a breakthrough technique that exploits the field-enhancing properties of periodic nanostructures to amplify the vibrational signals of trace molecules. The fascinating properties of SEIRA technology have aroused great interest, driving diverse sensing applications. In this review, we first discuss three ways for SEIRA performance optimization, including material selection, sensitivity enhancement, and bandwidth improvement. Subsequently, we discuss the potential applications of SEIRA technology in fields such as biomedicine and environmental monitoring. In recent years, we have ushered in a new era characterized by the Internet of Things, sensor networks, and wearable devices. These new demands spurred the pursuit of miniaturized and consolidated infrared spectroscopy systems and chips. In addition, the rise of machine learning has injected new vitality into SEIRA, bringing smart device design and data analysis to the foreground. The final section of this review explores the anticipated trajectory that SEIRA technology might take, highlighting future trends and possibilities.

Bright-Field and Edge-Enhanced Imaging Using an Electrically Tunable Dual-Mode Metalens

The imaging of microscopic biological samples faces numerous difficulties due to their small feature sizes and low-amplitude contrast. Metalenses have shown great promise in bioimaging as they have access to the complete complex information, which, alongside their extremely small and compact footprint and potential to integrate multiple functionalities into a single device, allow for miniaturized microscopy with exceptional features. Here, we design and experimentally realize a dual-mode metalens integrated with a liquid crystal cell that can be electrically switched between bright-field and edge-enhanced imaging on the millisecond scale. We combine the concepts of geometric and propagation phase to design the dual-mode metalens and physically encode the required phase profiles using hydrogenated amorphous silicon for operation at visible wavelengths. The two distinct metalens phase profiles include (1) a conventional hyperbolic metalens for bright-field imaging and (2) a spiral metalens with a topological charge of +1 for edge-enhanced imaging. We demonstrate the focusing and vortex generation ability of the metalens under different states of circular polarization and prove its use for biological imaging. This work proves a method for in vivo observation and monitoring of the cell response and drug screening within a compact form factor.

Deep-learning-assisted reconfigurable metasurface antenna for real-time holographic beam steering

We propose a metasurface antenna capable of real-time holographic beam steering. An array of reconfigurable dipoles can generate on-demand far-field patterns of radiation through the specific encoding of meta-atomic states i.e., the configuration of each dipole. Suitable states for the generation of the desired patterns can be identified using iteration, but this is very slow and needs to be done for each far-field pattern. Here, we present a deep-learning-based method for the control of a metasurface antenna with point dipole elements that vary in their state using dipole polarizability. Instead of iteration, we adopt a deep learning algorithm that combines an autoencoder with an electromagnetic scattering equation to determine the states required for a target far-field pattern in real-time. The scattering equation from Born approximation is used as the decoder in training the neural network, and analytic Green's function calculation is used to check the validity of Born approximation. Our learning-based algorithm requires a computing time of within 200 μs to determine the meta-atomic states, thus enabling the real-time operation of a holographic antenna.

Metasurface designed with quantitative field distributions

A new method for designing metasurfaces has been proposed and demonstrated, which allows for the generation of precise quantitative field distributions. This unique approach involves combining a tandem neural network with an iterative algorithm to optimize the metasurface design, enabling accurate control over the intensity and polarization of the resulting field. This strategy is both efficient and robust and has the potential to accelerate the development of metasurface devices with complex functionalities.

Broadband Solar Metamaterial Absorbers Empowered by Transformer-Based Deep Learning

The research of metamaterial shows great potential in the field of solar energy harvesting. In the past decade, the design of broadband solar metamaterial absorber (SMA) has attracted a surge of interest. The conventional design typically requires brute-force optimizations with a huge sampling space of structure parameters. Very recently, deep learning (DL) has provided a promising way in metamaterial design, but its application on SMA development is barely reported due to the complicated features of broadband spectrum. Here, this work develops the DL model based on metamaterial spectrum transformer (MST) for the powerful design of high-performance SMAs. The MST divides the optical spectrum of metamaterial into N patches, which overcomes the severe problem of overfitting in traditional DL and boosts the learning capability significantly. A flexible design tool based on free customer definition is developed to facilitate the real-time on-demand design of metamaterials with various optical functions. The scheme is applied to the design and fabrication of SMAs with graded-refractive-index nanostructures. They demonstrate the high average absorptance of 94% in a broad solar spectrum and exhibit exceptional advantages over many state-of-the-art counterparts. The outdoor testing implies the high-efficiency energy collection of about 1061 kW h m^-2 from solar radiation annually. This work paves a way for the rapid smart design of SMA, and will also provide a real-time developing tool for many other metamaterials and metadevices.