Pushing the Limits of Functionality-Multiplexing Capability in Metasurface Design Based on Statistical Machine Learning

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.

Unidirectional Transmission Metasurfaces with Topological Continuity Generated from High-dimensional Design Space

With the growing demand for nanodevices, there is a concerted effort to improve the design flexibility of nanostructures, thereby expanding the capabilities of nanophotonic devices. In this work, a Laplacian-weighted binary search (LBS) algorithm is proposed to generate a unidirectional transmission metasurface from a high-dimensional design space, offering an increased degree of design freedom. The LBS algorithm incorporates topological continuity based on the Laplacian, effectively circumventing the common issue of high structural complexity in designing high-dimensional nanostructures. As a result, metasurfaces developed using the LBS algorithm in a high-dimensional design space exhibit reduced complexity, which is advantageous for experimental fabrication. An all-dielectric metasurface with unidirectional transmission, designed from the high-dimensional space using the LBS method, demonstrated the successful application of these design principles in experiments. The metasurface exhibits high optical performance on unidirectional transmission in measurements by a high-resolution angle-resolved micro-spectra system, achieving forward transmissivity above 90% (400-700 nm) and back transmissivity below 20% (400-500 nm) within the targeted wavelength range. This work provides a feasible approach for advancing high-dimensional metasurface applications, as the LBS design method takes into account topological continuity during experimental processing. Compared to traditional direct binary search (DBS) methods, the LBS method not only improves information processing efficiency but also maintains the topological continuity of structures. Beyond unidirectional transmission, the LBS-based design method has generality and flexibility to accommodate almost all physical scenarios in metasurface design, enabling a multitude of complex functions and applications.

Dynamic Hyperspectral Holography Enabled by Inverse-Designed Metasurfaces with Oblique Helicoidal Cholesterics

Metasurfaces are flat arrays of nanostructures that allow exquisite control of phase and amplitude of incident light. Although metasurfaces offer new active element for both fundamental science and applications, the challenge still remains to overcome their low information capacity and passive nature. Here, by integrating an inverse-designed-metasurface with oblique helicoidal cholesteric liquid crystal (ChOH), simultaneous spatial and spectral tunable metasurfaces with a high information capacity for dynamic hyperspectral holography, are demonstrated. The inverse design facilitates a single-phase map encoding of ten independent holographic images at different wavelengths. ChOH provides precise spectral modulation with narrow bandwidth and wide tunable regime in response to programmed stimuli, thus enabling dynamic switching of the multicolor holography. The results provide simple and generalizable principles for the rational design of interactive metasurfaces that will find numerous applications, including security platform.

Multitask Learning Deep Neural Networks Enable Embedded Design of Active Metamaterials

In this study, we propose and implement a deep neural network framework based on multitask learning aimed at simplifying the forward modeling and inverse design process of photonic devices integrating active metasurfaces. We demonstrate and validate our approach by constructing a continuously tunable bandpass filter that is effective in the midwave infrared region. The key to this filter is the combination of a metasurface and Fabry-Perot (F-P) cavity structure of the tunable phase-change material Ge2Sb2Se4Te (GSST) and the precise control of the crystallinity of the GSST by a silicon-based heater. With the help of a deep learning framework, we are able to independently model the crystallinity and geometric parameters of the filter to maximize the use of GSST tuning for bandpass filtering. Our model discusses the self-attention mechanism and the effect of noise and compares several existing popular algorithms, and the results show that a multitask deep learning strategy can better assist the on-demand reverse design of photonic structures with phase change materials. This opens up new possibilities for personalization and functional extension of optical devices.

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.

Schrödinger's Red Beyond 65,000 Pixel-Per-Inch by Multipolar Interaction in Freeform Meta-Atom through Efficient Neural Optimizer

Freeform nanostructures have the potential to support complex resonances and their interactions, which are crucial for achieving desired spectral responses. However, the design optimization of such structures is nontrivial and computationally intensive. Furthermore, the current "black box" design approaches for freeform nanostructures often neglect the underlying physics. Here, a hybrid data-efficient neural optimizer for resonant nanostructures by combining a reinforcement learning algorithm and Powell's local optimization technique is presented. As a case study, silicon nanostructures with a highly-saturated red color are designed and experimentally demonstrated. Specifically, color coordinates of (0.677, 0.304) in the International Commission on Illumination (CIE) chromaticity diagram - close to the ideal Schrödinger's red, with polarization independence, high reflectance (>85%), and a large viewing angle (i.e., up to ± 25°) is achieved. The remarkable performance is attributed to underlying generalized multipolar interferences within each nanostructure rather than the collective array effects. Based on that, pixel size down to ≈400 nm, corresponding to a printing resolution of 65000 pixels per inch is demonstrated. Moreover, the proposed design model requires only ≈300 iterations to effectively search a thirteen-dimensional (13D) design space - an order of magnitude more efficient than the previously reported approaches. The work significantly extends the free-form optical design toolbox for high-performance flat-optical components and metadevices.

Artificial neural network assisted the design of subwavelength-grating waveguides for nanoparticles optical trapping

In this work, we propose artificial neural networks (ANNs) to predict the optical forces on particles with a radius of 50 nm and inverse-design the subwavelength-grating (SWG) waveguides structure for trapping. The SWG waveguides are applied to particle trapping due to their superior bulk sensitivity and surface sensitivity, as well as longer working distance than conventional nanophotonic waveguides. To reduce the time consumption of the design, we train ANNs to predict the trapping forces and to inverse-design the geometric structure of SWG waveguides, and the low mean square errors (MSE) of the networks achieve 2.8 × 10-4. Based on the well-trained forward prediction and inverse-design network, an SWG waveguide with significant trapping performance is designed. The trapping forces in the y-direction achieve-40.39 pN when the center of the particle is placed 100 nm away from the side wall of the silicon segment, and the negative sign of the optical forces indicates the direction of the forces. The maximum trapping potential achieved to 838.16 kBT in the y-direction. The trapping performance in the x and z directions is also quite superior, and the neural network model has been further applied to design SWGs with a high trapping performance. The present work is of significance for further research on the application of artificial neural networks in other optical devices designed for particle trapping.

Chiral plasmonic sensing: From the perspective of light-matter interaction

Molecular chirality is represented as broken mirror symmetry in the structural orientation of constituent atoms and plays a pivotal role at every scale of nature. Since the discovery of the chiroptic property of chiral molecules, the characterization of molecular chirality is important in the fields of biology, physics, and chemistry. Over the centuries, the field of optical chiral sensing was based on chiral light-matter interactions between chiral molecules and polarized light. Starting from simple optics-based sensing, the utilization of plasmonic materials that could control local chiral light-matter interactions by squeezing light into molecules successfully facilitated chiral sensing into noninvasive, ultrasensitive, and accurate detection. In this Review, the importance of plasmonic materials and their engineering in chiral sensing are discussed based on the principle of chiral light-matter interactions and the theory of optical chirality and chiral perturbation; thus, this Review can serve as a milestone for the proper design and utilization of plasmonic nanostructures for improved chiral sensing.

Physiochemical Coupled Dynamic Nanosphere Lithography Enabling Multiple Metastructures from Single Mask

Metastructures are widely used in photonic devices, energy conversion, and biomedical applications. However, to fabricate multiple patterns continuously in single etching protocol with highly tunable photonic properties is challenging. Here, a simple and robust dynamic nanosphere lithography is proposed by inserting a spacer between the nanosphere assembly and the wafer. The nanosphere diameter decrease and uneven penetration of the spacer during etching lead to a dynamic masking process. Coupled anisotropic physical ion sputtering and ricocheting with isotropic chemical radical etching achieve highly tunable structures with various 3D patterns continuously forming through a single etching process. Specifically, the nanosphere diameters define the periodicity, the etched spacer forms the upper parts, and the wafer forms the lower parts. Each part of the structure is highly tunable through changing nanosphere diameter, spacer thickness, and etch conditions. Using this protocol, numerous structures of varying sizes including nanomushrooms, nanocones, nanopencils, and nanoneedles with diverse shapes are realized as proof of concepts. The broadband antireflection ability of the nanostructures and their use in surface-enhanced Raman spectroscopy are also demonstrated for practical application. This method substantially simplifies the fabrication procedure of various metastructures, paving the way for its application in multiple disciplines especially in photonic devices.

Designing Metasurfaces for Efficient Solar Energy Conversion

Metasurfaces have recently emerged as a promising technological platform, offering unprecedented control over light by structuring materials at the nanoscale using two-dimensional arrays of subwavelength nanoresonators. These metasurfaces possess exceptional optical properties, enabling a wide variety of applications in imaging, sensing, telecommunication, and energy-related fields. One significant advantage of metasurfaces lies in their ability to manipulate the optical spectrum by precisely engineering the geometry and material composition of the nanoresonators' array. Consequently, they hold tremendous potential for efficient solar light harvesting and conversion. In this Review, we delve into the current state-of-the-art in solar energy conversion devices based on metasurfaces. First, we provide an overview of the fundamental processes involved in solar energy conversion, alongside an introduction to the primary classes of metasurfaces, namely, plasmonic and dielectric metasurfaces. Subsequently, we explore the numerical tools used that guide the design of metasurfaces, focusing particularly on inverse design methods that facilitate an optimized optical response. To showcase the practical applications of metasurfaces, we present selected examples across various domains such as photovoltaics, photoelectrochemistry, photocatalysis, solar-thermal and photothermal routes, and radiative cooling. These examples highlight the ways in which metasurfaces can be leveraged to harness solar energy effectively. By tailoring the optical properties of metasurfaces, significant advancements can be expected in solar energy harvesting technologies, offering new practical solutions to support an emerging sustainable society.

Revisiting the Design Strategies for Metasurfaces: Fundamental Physics, Optimization, and Beyond

Over the last two decades, the capabilities of metasurfaces in light modulation with subwavelength thickness have been proven, and metasurfaces are expected to miniaturize conventional optical components and add various functionalities. Herein, various metasurface design strategies are reviewed thoroughly. First, the scalar diffraction theory is revisited to provide the basic principle of light propagation. Then, widely used design methods based on the unit-cell approach are discussed. The methods include a set of simplified steps, including the phase-map retrieval and meta-atom unit-cell design. Then, recently emerging metasurfaces that may not be accurately designed using unit-cell approach are introduced. Unconventional metasurfaces are examined where the conventional design methods fail and finally potential design methods for such metasurfaces are discussed.

End-to-End Diverse Metasurface Design and Evaluation Using an Invertible Neural Network

Employing deep learning models to design high-performance metasurfaces has garnered significant attention due to its potential benefits in terms of accuracy and efficiency. A deep learning-based metasurface design framework typically comprises a forward prediction path for predicting optical responses and a backward retrieval path for generating geometrical configurations. In the forward design path, a specific geometrical configuration corresponds to a unique optical response. However, in the inverse design path, a single performance metric can correspond to multiple potential designs. This one-to-many mapping poses a significant challenge for deep learning models and can potentially impede their performance. Although representing the inverse path as a probabilistic distribution is a widely adopted method for tackling this problem, accurately capturing the posterior distribution to encompass all potential solutions remains an ongoing challenge. Furthermore, in most pioneering works, the forward and backward paths are captured using separate models. However, the knowledge acquired from the forward path does not contribute to the training of the backward model. This separation of models adds complexity to the system and can hinder the overall efficiency and effectiveness of the design framework. Here, we utilized an invertible neural network (INN) to simultaneously model both the forward and inverse process. Unlike other frameworks, INN focuses on the forward process and implicitly captures a probabilistic model for the inverse process. Given a specific optical response, the INN enables the recovery of the complete posterior over the parameter space. This capability allows for the generation of novel designs that are not present in the training data. Through the integration of the INN with the angular spectrum method, we have developed an efficient and automated end-to-end metasurface design and evaluation framework. This novel approach eliminates the need for human intervention and significantly speeds up the design process. Utilizing this advanced framework, we have effectively designed high-efficiency metalenses and dual-polarization metasurface holograms. This approach extends beyond dielectric metasurface design, serving as a general method for modeling optical inverse design problems in diverse optical fields.

Recent advances in metasurface design and quantum optics applications with machine learning, physics-informed neural networks, and topology optimization methods

As a two-dimensional planar material with low depth profile, a metasurface can generate non-classical phase distributions for the transmitted and reflected electromagnetic waves at its interface. Thus, it offers more flexibility to control the wave front. A traditional metasurface design process mainly adopts the forward prediction algorithm, such as Finite Difference Time Domain, combined with manual parameter optimization. However, such methods are time-consuming, and it is difficult to keep the practical meta-atom spectrum being consistent with the ideal one. In addition, since the periodic boundary condition is used in the meta-atom design process, while the aperiodic condition is used in the array simulation, the coupling between neighboring meta-atoms leads to inevitable inaccuracy. In this review, representative intelligent methods for metasurface design are introduced and discussed, including machine learning, physics-information neural network, and topology optimization method. We elaborate on the principle of each approach, analyze their advantages and limitations, and discuss their potential applications. We also summarize recent advances in enabled metasurfaces for quantum optics applications. In short, this paper highlights a promising direction for intelligent metasurface designs and applications for future quantum optics research and serves as an up-to-date reference for researchers in the metasurface and metamaterial fields.

A reconfigurable graphene patch antenna inverse design at terahertz frequencies

This article investigates the inverse design of a reconfigurable multi-band patch antenna based on graphene for terahertz applications to operate frequency range (2-5THz). In the first step, this article evaluates the dependence of the antenna radiation characteristics on its geometric parameters and the graphene properties. The simulation results show that it is possible to achieve up to 8.8 dB gain, 13 frequency bands, and 360[Formula: see text] beam steering. Then and due to the complexity of the design of graphene antenna, a deep neural network (DNN) is used to predict the antenna parameters by given inputs like desired realized gain, main lobe direction, half power beam width, and return loss in each resonance frequency. The trained DNN model predicts almost with 93% accuracy and 3% mean square error in the shortest time. Then, this network was used to design five-band and three-band antennas, and it has been shown that the desired antenna parameters are achieved with negligible errors. Therefore, the proposed antenna finds many potential applications in the THz frequency band.

Exploring the limits of metasurface polarization multiplexing capability based on deep learning

Metasurfaces provide a new approach for planar optics and thus have realized multifunctional meta-devices with different multiplexing strategies, among which polarization multiplexing has received much attention due to its convenience. At present, a variety of design methods of polarization multiplexed metasurfaces have been developed based on different meta-atoms. However, as the number of polarization states increases, the response space of meta-atoms becomes more and more complex, and it is difficult for these methods to explore the limit of polarization multiplexing. Deep learning is one of the important routes to solve this problem because it can realize the effective exploration of huge data space. In this work, a design scheme for polarization multiplexed metasurfaces based on deep learning is proposed. The scheme uses a conditional variational autoencoder as an inverse network to generate structural designs and combines a forward network that can predict meta-atoms' responses to improve the accuracy of designs. The cross-shaped structure is used to establish a complicated response space containing different polarization state combinations of incident and outgoing light. The multiplexing effects of the combinations with different numbers of polarization states are tested by utilizing the proposed scheme to design nanoprinting and holographic images. The polarization multiplexing capability limit of four channels (a nanoprinting image and three holographic images) is determined. The proposed scheme lays the foundation for exploring the limits of metasurface polarization multiplexing capability.

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.

Imaging-based intelligent spectrometer on a plasmonic rainbow chip

Compact, lightweight, and on-chip spectrometers are required to develop portable and handheld sensing and analysis applications. However, the performance of these miniaturized systems is usually much lower than their benchtop laboratory counterparts due to oversimplified optical architectures. Here, we develop a compact plasmonic "rainbow" chip for rapid, accurate dual-functional spectroscopic sensing that can surpass conventional portable spectrometers under selected conditions. The nanostructure consists of one-dimensional or two-dimensional graded metallic gratings. By using a single image obtained by an ordinary camera, this compact system can accurately and precisely determine the spectroscopic and polarimetric information of the illumination spectrum. Assisted by suitably trained deep learning algorithms, we demonstrate the characterization of optical rotatory dispersion of glucose solutions at two-peak and three-peak narrowband illumination across the visible spectrum using just a single image. This system holds the potential for integration with smartphones and lab-on-a-chip systems to develop applications for in situ analysis.

Multicolor and 3D Holography Generated by Inverse-Designed Single-Cell Metasurfaces

Metasurface-generated holography has emerged as a promising route for fully reproducing vivid scenes by manipulating the optical properties of light using ultra-compact devices. However, achieving multiple holographic images using a single metasurface is still difficult due to the capacity limit of a single meta-atom. In this work, an inverse design method based on gradient-descent optimization is presented to encode multiple pieces of holographic information into a single metasurface. The proposed method allows the inverse design of single-cell metasurfaces without the need for complex meta-atom design strategies, facilitating high-throughput fabrication using broadband low-loss materials. By exploiting the proposed design method, both multiplane red-green-blue (RGB) color and three-dimensional (3D) holograms are designed and experimentally demonstrated. Multiplane RGB color holograms with nine distinct holograms are achieved, which demonstrate the state-of-the-art data capacity of a phase-only metasurface. The first experimental demonstration of metasurface-generated 3D holograms with completely independent and distinct images in each plane is also presented. The current research findings provide a viable route for practical metasurface-generated holography by demonstrating the high-density holography produced by a single metasurface. It is expected to ultimately lead to optical storage, display, and full-color imaging applications.

Inverse Design of Nanophotonic Devices Using Generative Adversarial Networks with the Sim-NN Model and Self-Attention Mechanism

The inverse design method based on a generative adversarial network (GAN) combined with a simulation neural network (sim-NN) and the self-attention mechanism is proposed in order to improve the efficiency of GAN for designing nanophotonic devices. The sim-NN can guide the model to produce more accurate device designs via the spectrum comparison, whereas the self-attention mechanism can help to extract detailed features of the spectrum by exploring their global interconnections. The nanopatterned power splitter with a 2 μm × 2 μm interference region is designed as an example to obtain the average high transmission (>94%) and low back-reflection (<0.5%) over the broad wavelength range of 1200~1650 nm. As compared to other models, this method can produce larger proportions of high figure-of-merit devices with various desired power-splitting ratios.

Universal Narrowband Wavefront Shaping with High Quality Factor Meta-Reflect-Arrays

Optical metasurfaces offer unprecedented flexibility in light wave manipulation but suffer weak resonant enhancement. Tackling this problem, we experimentally unveil a new phase gradient metasurface platform made entirely from individually addressable high quality factor (high-Q) silicon meta-atoms. Composed of pairs of nearly identical nanoblocks, these meta-atoms support dipolar-guided-mode resonances that, due to the controlled suppression of radiation loss, serve as highly sensitive phase pixels when placed above a mirror. A key novelty of this platform lies in the vanishingly small structural perturbations needed to produce universal phase fronts. Having fabricated elements with Q-factor ∼380 and spaced by λ/1.2, we achieve strong beam steering, up to 59% efficient, to angles 32.3°, 25.3°, and 20.9°, with variations in nanoantenna volume fractions across the metasurfaces of ≤2.6%, instead of >50% required by traditional versions. Aside from extreme sensitivity, the metasurfaces exhibit near-field intensity enhancement over 1000×. Taken together, these properties represent an exciting prospect for dynamic and nonlinear wave shaping.

Neural-Network-Enabled Design of a Chiral Plasmonic Nanodimer for Target-Specific Chirality Sensing

Quantitative analysis of chiral molecules in various solvents is essential. However, there are still many challenges to enhancing the sensitivity in precisely determining both concentration and chirality. Here, we built an algorithmic methodology to predict and optimally design the chiroptical response of chiral plasmonic sensors for a specific target chiral analyte with the aid of deep learning. Based upon the analytic and intuitive understanding of the Born-Kuhn type plasmonic nanodimer, we designed and trained the neural networks that can successfully predict the chiroptical properties and further inversely design the plasmonic structure to achieve the intended circular dichroism. The developed algorithm could identify the optimum structure exhibiting the maximum sensitivity for the given specific analytes. Surprisingly, we discovered that sensitivity strongly depends on the various conditions of analytes and can be finely tuned with the structural parameters of plasmonic nanodimers. We envision that this study can provide a general platform to develop ultrasensitive chiral plasmonic sensors whose structure and sensitivity have been evolved algorithmically for adoption in specific applications.

Breaking the limitation of polarization multiplexing in optical metasurfaces with engineered noise

Noise is usually undesired yet inevitable in science and engineering. However, by introducing the engineered noise to the precise solution of Jones matrix elements, we break the fundamental limit of polarization multiplexing capacity of metasurfaces that roots from the dimension constraints of the Jones matrix. We experimentally demonstrate up to 11 independent holographic images using a single metasurface illuminated by visible light with different polarizations. To the best of our knowledge, it is the highest capacity reported for polarization multiplexing. Combining the position multiplexing scheme, the metasurface can generate 36 distinct images, forming a holographic keyboard pattern. This discovery implies a new paradigm for high-capacity optical display, information encryption, and data storage.

Super-resolution image display using diffractive decoders

High-resolution image projection over a large field of view (FOV) is hindered by the restricted space-bandwidth product (SBP) of wavefront modulators. We report a deep learning-enabled diffractive display based on a jointly trained pair of an electronic encoder and a diffractive decoder to synthesize/project super-resolved images using low-resolution wavefront modulators. The digital encoder rapidly preprocesses the high-resolution images so that their spatial information is encoded into low-resolution patterns, projected via a low SBP wavefront modulator. The diffractive decoder processes these low-resolution patterns using transmissive layers structured using deep learning to all-optically synthesize/project super-resolved images at its output FOV. This diffractive image display can achieve a super-resolution factor of ~4, increasing the SBP by ~16-fold. We experimentally validate its success using 3D-printed diffractive decoders that operate at the terahertz spectrum. This diffractive image decoder can be scaled to operate at visible wavelengths and used to design large SBP displays that are compact, low power, and computationally efficient.

Fast Topology Optimization for Near-Field Focusing All-Dielectric Metasurfaces Using the Discrete Dipole Approximation

Using an efficient implementation of the discrete dipole approximation and topology optimization, we design all-dielectric metasurfaces capable of focusing light into intense deep subwavelength hotspots. The light focusing of these metasurfaces far outweighs conventional lenses and can provide dramatic enhancements of processes that depend superlinearly on light intensity, such as light-powered membrane distillation and photocatalysis. Our approach can easily be generalized to optimize metasurfaces for other functionalities, such as nonlinear optics or photothermal conversion.

Compact vertical grating coupler with an achromatic in-plane metalens on a 220-nm silicon-on-insulator platform

We proposed a new type of vertical grating couplers (VGCs) with a compact footprint on the 220-nm silicon-on-insulator platform. The overall size of the device containing the L-shaped coupling grating and the taper with achromatic in-plane metalens is only 45 × 15 µm², and the measured coupling efficiency at 1550 nm is -5.2 dB with a 1 dB bandwidth of 38 nm, around 1.6 dB higher than the VGC without metalens. The incidence angle mismatch has a 1 dB bandwidth of roughly 4°, whereas the displacement mismatch along the x-/y- axis has a bandwidth of around 3/4 µm. Furthermore, we experimentally show that such a design is compatible with VGCs operating in the S, C, and L bands.

Light-driven single-cell rotational adhesion frequency assay

The interaction between cell surface receptors and extracellular ligands is highly related to many physiological processes in living systems. Many techniques have been developed to measure the ligand-receptor binding kinetics at the single-cell level. However, few techniques can measure the physiologically relevant shear binding affinity over a single cell in the clinical environment. Here, we develop a new optical technique, termed single-cell rotational adhesion frequency assay (scRAFA), that mimics in vivo cell adhesion to achieve label-free determination of both homogeneous and heterogeneous binding kinetics of targeted cells at the subcellular level. Moreover, the scRAFA is also applicable to analyze the binding affinities on a single cell in native human biofluids. With its superior performance and general applicability, scRAFA is expected to find applications in study of the spatial organization of cell surface receptors and diagnosis of infectious diseases.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s43593-022-00020-4.

Homeostatic neuro-metasurfaces for dynamic wireless channel management

The physical basis of a smart city, the wireless channel, plays an important role in coordinating functions across a variety of systems and disordered environments, with numerous applications in wireless communication. However, conventional wireless channel typically necessitates high-complexity and energy-consuming hardware, and it is hindered by lengthy and iterative optimization strategies. Here, we introduce the concept of homeostatic neuro-metasurfaces to automatically and monolithically manage wireless channel in dynamics. These neuro-metasurfaces relieve the heavy reliance on traditional radio frequency components and embrace two iconic traits: They require no iterative computation and no human participation. In doing so, we develop a flexible deep learning paradigm for the global inverse design of large-scale metasurfaces, reaching an accuracy greater than 90%. In a full perception-decision-action experiment, our concept is demonstrated through a preliminary proof-of-concept verification and an on-demand wireless channel management. Our work provides a key advance for the next generation of electromagnetic smart cities.

Artificial neural networks assisting the design of a dual-mode photonic crystal nanobeam cavity for simultaneous sensing of the refractive index and temperature

We put forward a dual-mode photonic crystal nanobeam cavity for simultaneous sensing of the refractive index (RI) and temperature (T) designed with the assistance of artificial neural networks (ANNs). We choose the structure of quadratically tapered elliptical holes with a slot to improve the sensitivities of the two modes. To reduce the time consumption of the design, the ANNs are trained to predict the band structure and to inverse design the geometric structure. For the forward prediction and the inverse design neural networks, low mean square errors of 5.1×10^-4 and 1.4×10^-2 are achieved, respectively. Through a specific design of band properties by the well-trained neural networks, a dual-mode nanobeam sensor with high quality factors of 9.34×10⁴ and 1.55×10⁵ and a small footprint of 23.8×0.7µm² are designed. The RI and T sensitivities of the air mode are 405 nm/RIU and 40 pm/K, respectively, whereas those of the dielectric mode are 531 nm/RIU and 27 pm/K, respectively. The present work shows significance in further research on the design and applications for dual-mode cavities.

Physics-Guided Neural-Network-Based Inverse Design of a PhotonicPlasmonic Nanodevice for Superfocusing

Controlling the nanoscale light-matter interaction using superfocusing hybrid photonic-plasmonic devices has attracted significant research interest in tackling existing challenges, including converting efficiencies, working bandwidths, and manufacturing complexities. With the growth in demand for efficient photonic-plasmonic input-output interfaces to improve plasmonic device performances, sophisticated designs with multiple optimization parameters are required, which comes with an unaffordable computation cost. Machine learning methods can significantly reduce the cost of computations compared to numerical simulations, but the input-output dimension mismatch remains a challenging problem. Here, we introduce a physics-guided two-stage machine learning network that uses the improved coupled-mode theory for optical waveguides to guide the learning module and improve the accuracy of predictive engines to 98.5%. A near-unity coupling efficiency with symmetry-breaking selectivity is predicted by the inverse design. By fabricating photonic-plasmonic couplers using the predicted profiles, we demonstrate that the excitation efficiency of 83% on the radially polarized surface plasmon mode can be achieved, which paves the way for super-resolution optical imaging.