Building Multifunctional Metasystems via Algorithmic Construction

Bidirectional Vectorial Holography Using Bi-Layer Metasurfaces and Its Application to Optical Encryption

The field of optical systems with asymmetric responses has grown significantly due to their various potential applications. Janus metasurfaces are noteworthy for their ability to control light asymmetrically at the pixel level within thin films. However, previous demonstrations are restricted to the partial control of asymmetric transmission for a limited set of input polarizations, focusing primarily on scalar functionalities. Here, optical bi-layer metasurfaces that achieve a fully generalized form of asymmetric transmission for any input polarization are presented. The designs owe much to the theoretical model of asymmetric transmission in reciprocal systems, which elucidates the relationship between front- and back-side Jones matrices in general cases. This model reveals a fundamental correlation between the polarization-direction channels of opposing sides. To circumvent this constraint, partitioning the transmission space is utilized to realize four distinct vector functionalities within the target volume. As a proof of concept, polarization-direction-multiplexed Janus vectorial holograms generating four vectorial holographic images are experimentally demonstrated. When integrated with computational vector polarizer arrays, this approach enables optical encryption with a high level of obscurity. The proposed mathematical framework and novel material systems for generalized asymmetric transmission may pave the way for applications such as optical computation, sensing, and imaging.

Localized Plasmonic Structured Illumination Microscopy Using Hybrid Inverse Design

Super-resolution fluorescence imaging has offered unprecedented insights and revolutionized our understanding of biology. In particular, localized plasmonic structured illumination microscopy (LPSIM) achieves video-rate super-resolution imaging with ∼50 nm spatial resolution by leveraging subdiffraction-limited nearfield patterns generated by plasmonic nanoantenna arrays. However, the conventional trial-and-error design process for LPSIM arrays is time-consuming and computationally intensive, limiting the exploration of optimal designs. Here, we propose a hybrid inverse design framework combining deep learning and genetic algorithms to refine LPSIM arrays. A population of designs is evaluated using a trained convolutional neural network, and a multiobjective optimization method optimizes them through iteration and evolution. Simulations demonstrate that the optimized LPSIM substrate surpasses traditional substrates, exhibiting higher reconstruction accuracy, robustness against noise, and increased tolerance for fewer measurements. This framework not only proves the efficacy of inverse design for tailoring LPSIM substrates but also opens avenues for exploring new plasmonic nanostructures in imaging applications.

Agile Inverse Design of Polarization-Independent Multi-Functional Reconfiguration Metamaterials Based on Doped VO2

Increasing attention is being paid to the application potential of multi-functional reconfigurable metamaterials in intelligent communication, sensor networks, homeland security, and other fields. A polarization-independent multi-functional reconfigurable metasurface based on doped vanadium dioxide (VO2) is proposed in this paper. It can be controlled to switch its function among three working modes: electromagnetically induced absorption (EIA), electromagnetically induced transparency (EIT), and asymmetrical absorption. In addition, deep learning tools have greatly accelerated the design of relevant devices. Such devices and the method proposed in this paper have important value in the field of intelligent reconfigurable metamaterials, communication, and sensing.

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.

Inverse design of metasurfaces with customized transmission characteristics of frequency band based on generative adversarial networks

In recent years, deep learning (DL) has demonstrated significant potential in the inverse design of metasurfaces, and the generation of metasurfaces with customized transmission characteristics of frequency band remains a challenging and underexplored area. In this study, we propose a DL-assisted method for the inverse design of transmissive metasurfaces. The method consists of a generative adversarial network (GAN)-based graph generator, an electromagnetic response predictor, and a genetic algorithm optimizer. By integrating these components, we can obtain customized metasurfaces with desired transmission characteristics of frequency band. We demonstrate the effectiveness of the proposed method through examples of inverse-designed three-layer cascaded transmissive metasurfaces with wideband, dual-band, and stopband responses in the 8∼12 GHz frequency range. Specifically, we realize three different types of dual-band metasurfaces, namely double-wide, front-wide and rear-narrow, and front-narrow and rear-wide configurations. Additionally, we analyze the accuracy and reliability of the inverse design method by employing data from the training dataset, self-defined objectives, and bandwidth-reduced target responses scaled from the wideband type as design inputs. Quantitative evaluation is performed using metrics such as mean absolute error and average precision. The proposed method successfully achieves the desired effect as intended.

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.

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.

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.

Phase-to-pattern inverse design for a fast realization of a functional metasurface by combining a deep neural network and a genetic algorithm

Metasurface provides an unprecedented means to manipulate electromagnetic waves within a two-dimensional planar structure. Traditionally, the design of meta-atom follows the pattern-to-phase paradigm, which requires a time-consuming brute-forcing process. In this work, we present a fast inverse meta-atom design method for the phase-to-pattern mapping by combining the deep neural network (DNN) and genetic algorithm (GA). The trained classification DNN with an accuracy of 92% controls the population generated by the GA within an arbitrary preset small phase range, which could greatly enhance the optimization efficiency with less iterations and a higher accuracy. As proof-of-concept demonstrations, two reflective functional metasurfaces including an orbital angular momentum generator and a metalens have been numerically investigated. The simulated results agree very well with the design goals. In addition, the metalens is also experimentally validated. The proposed method could pave a new avenue for the fast design of the meta-atoms and functional meta-devices.

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.

Multifunctional resonant wavefront-shaping meta-optics based on multilayer and multi-perturbation nonlocal metasurfaces

Photonic devices rarely provide both elaborate spatial control and sharp spectral control over an incoming wavefront. In optical metasurfaces, for example, the localized modes of individual meta-units govern the wavefront shape over a broad bandwidth, while nonlocal lattice modes extended over many unit cells support high quality-factor resonances. Here, we experimentally demonstrate nonlocal dielectric metasurfaces in the near-infrared that offer both spatial and spectral control of light, realizing metalenses focusing light exclusively over a narrowband resonance while leaving off-resonant frequencies unaffected. Our devices attain this functionality by supporting a quasi-bound state in the continuum encoded with a spatially varying geometric phase. We leverage this capability to experimentally realize a versatile platform for multispectral wavefront shaping where a stack of metasurfaces, each supporting multiple independently controlled quasi-bound states in the continuum, molds the optical wavefront distinctively at multiple wavelengths and yet stay transparent over the rest of the spectrum. Such a platform is scalable to the visible for applications in augmented reality and transparent displays.

Ultra-dense moving cascaded metasurface holography by using a physics-driven neural network

Metasurfaces are promising platforms for integrated compact optical systems. Traditional metasurface holography design algorithms are limited to information capacity due to finite spatial bandwidth production, which is insufficient for the growing demand for big data storage and encryption. Here, we propose and demonstrate deep learning empowered ultra-dense complex-amplitude holography using step-moving cascaded metasurfaces. Using deep learning artificial intelligence optimization strategy, the barriers of traditional algorithms can be conquered to meet diverse practical requirements. Two metasurfaces are cascaded to form the desired holography. One of them can move to switch the reconstruction images due to diffraction propagation accumulated during the cascaded path. The diffraction pattern from the first metasurface propagates at a different distance and meets with the second metasurface, reconstructing the target holographic reconstructions in the far-field. Such a technique can provide a new solution for multi-dimensional beam shaping, optical encryption, camouflage, integrated on-chip ultra-high-density storage, etc.

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.

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

As 2D metamaterials, metasurfaces provide an unprecedented means to manipulate light with the ability to multiplex different functionalities in a single planar device. Currently, most pursuits of multifunctional metasurfaces resort to empirically accommodating more functionalities at the cost of increasing structural complexity, with little effort to investigate the intrinsic restrictions of given meta-atoms and thus the ultimate limits in the design. In this work, it is proposed to embed machine-learning models in both gradient-based and nongradient optimization loops for the automatic implementation of multifunctional metasurfaces. Fundamentally different from the traditional two-step approach that separates phase retrieval and meta-atom structural design, the proposed end-to-end framework facilitates full exploitation of the prescribed design space and pushes the multifunctional design capacity to its physical limit. With a single-layer structure that can be readily fabricated, metasurface focusing lenses and holograms are experimentally demonstrated in the near-infrared region. They show up to eight controllable responses subjected to different combinations of working frequencies and linear polarization states, which are unachievable by the conventional physics-guided approaches. These results manifest the superior capability of the data-driven scheme for photonic design, and will accelerate the development of complex devices and systems for optical display, communication, and computing.

Maximized Frequency Doubling through the Inverse Design of Nonlinear Metamaterials

The conventional process for developing an optimal design for nonlinear optical responses is based on a trial-and-error approach that is largely inefficient and does not necessarily lead to an ideal result. Deep learning can automate this process and widen the realm of nonlinear geometries and devices. This research illustrates a deep learning framework used to create an optimal plasmonic design for a nonlinear metamaterial. The algorithm produces a plasmonic pattern that can maximize the second-order nonlinear effect of a nonlinear metamaterial. A nanolaminate metamaterial is used as a nonlinear material, and plasmonic patterns are fabricated on the prepared nanolaminate to demonstrate the validity and efficacy of the deep learning algorithm. The optimal pattern produced yielded second-harmonic generation from the nanolaminate with normal incident fundamental light. The deep learning architecture applied in this research can be expanded to other optical responses and light-matter interaction processes.

Analog optical deconvolution computing for wavefront coding based on nanoantennas metasurfaces

Analog optical computing based on metasurfaces has attracted much attention for achieving high-speed calculating without the electronic processing unit. Wavefront coding imaging systems involve the joint design of an encoded image-capturing module and decoding postprocessing algorithms to obtain a required final image. The decoding postprocessing algorithms, as a typical deconvolution computation, require an additional electronic processing unit to yield a high-quality decoded image. We demonstrate an analog optical deconvolution computing kernel based on nanoantennas metasurfaces for the postprocessing calculation of wavefront coding systems. Numerical simulations are presented to prove that the encoded point spread function can be refocused through a designed optical computing metasurface. The proposed approach opens an opportunity for real-time recovering images in wavefront coding optical systems.

Design of multilayer optical thin-films based on light scattering properties and using deep neural networks

Despite limiting the performance of multilayer optical thin-films, light scattering properties are not as yet controllable by current design methods. These methods usually consider only specular properties: transmittance and reflectance. Among other techniques, design of thin-film components assisted by deep neural networks have seen growing interest over the last few years. This paper presents an implementation of a deep neural network model for light scattering design and proposes an optimization process for complex multilayer thin-film components to comply with expectations on both specular and scattering spectral responses.