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1. Functional Metamaterial Devices Enabled by Microsystems

   https://doi.org/10.1109/IEDM13553.2020.9371924  

   Zhang, X. ; Zhao, X. ( December 2020 , 2020 IEEE International Electron Devices Meeting (IEDM))

   null (Ed.)

   Metamaterials represent a class of artificially engineered materials, which exhibit unprecedented properties enabled by their constituent subwavelength unit cells. The effective properties of metamaterials may be dynamically controlled by driving unit cells via different approaches, including photo-doping, electrical gating, or mechanical actuation. With such dynamical tuning mechanisms, the propagation modality of electromagnetic waves may be modulated to achieve functional devices for modulation, beam steering, focusing, and polarization control, among others. In addition, the perfect absorption and near field effect enabled by metamaterials may be used in electromagnetic detectors across the frequency spectrum. Microsystem technology provides a platform to achieve functional metamaterial devices by covering all requisite processes, including fabrication, packaging, and system integration. We report our progress in constructing functional devices by integrating metamaterials with microsystems technology and discuss remaining challenges and the future direction of metamaterial devices. 

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2. Machine learning–assisted global optimization of photonic devices

   https://doi.org/10.1515/nanoph-2020-0376  

   Kudyshev, Zhaxylyk A. ; Kildishev, Alexander V. ; Shalaev, Vladimir M. ; Boltasseva, Alexandra ( October 2020 , Nanophotonics)

   null (Ed.)

   Abstract Over the past decade, artificially engineered optical materials and nanostructured thin films have revolutionized the area of photonics by employing novel concepts of metamaterials and metasurfaces where spatially varying structures yield tailorable “by design” effective electromagnetic properties. The current state-of-the-art approach to designing and optimizing such structures relies heavily on simplistic, intuitive shapes for their unit cells or metaatoms. Such an approach cannot provide the global solution to a complex optimization problem where metaatom shape, in-plane geometry, out-of-plane architecture, and constituent materials have to be properly chosen to yield the maximum performance. In this work, we present a novel machine learning–assisted global optimization framework for photonic metadevice design. We demonstrate that using an adversarial autoencoder (AAE) coupled with a metaheuristic optimization framework significantly enhances the optimization search efficiency of the metadevice configurations with complex topologies. We showcase the concept of physics-driven compressed design space engineering that introduces advanced regularization into the compressed space of an AAE based on the optical responses of the devices. Beyond the significant advancement of the global optimization schemes, our approach can assist in gaining comprehensive design “intuition” by revealing the underlying physics of the optical performance of metadevices with complex topologies and material compositions. 

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3. Nonlocal elastic metasurfaces: Enabling broadband wave control via intentional nonlocality

   https://doi.org/10.1073/pnas.2004753117  

   Zhu, Hongfei ; Patnaik, Sansit ; Walsh, Timothy F. ; Jared, Bradley H. ; Semperlotti, Fabio ( October 2020 , Proceedings of the National Academy of Sciences)

   While elastic metasurfaces offer a remarkable and very effective approach to the subwavelength control of stress waves, their use in practical applications is severely hindered by intrinsically narrow band performance. In applications to electromagnetic and photonic metamaterials, some success in extending the operating dynamic range was obtained by using nonlocality. However, while electronic properties in natural materials can show significant nonlocal effects, even at the macroscales, in mechanics, nonlocality is a higher-order effect that becomes appreciable only at the microscales. This study introduces the concept of intentional nonlocality as a fundamental mechanism to design passive elastic metasurfaces capable of an exceptionally broadband operating range. The nonlocal behavior is achieved by exploiting nonlocal forces, conceptually akin to long-range interactions in nonlocal material microstructures, between subsets of resonant unit cells forming the metasurface. These long-range forces are obtained via carefully crafted flexible elements, whose specific geometry and local dynamics are designed to create remarkably complex transfer functions between multiple units. The resulting nonlocal coupling forces enable achieving phase-gradient profiles that are functions of the wavenumber of the incident wave. The identification of relevant design parameters and the assessment of their impact on performance are explored via a combination of semianalytical and numerical models. The nonlocal metasurface concept is tested, both numerically and experimentally, by embedding a total-internal-reflection design in a thin-plate waveguide. Results confirm the feasibility of the intentionally nonlocal design concept and its ability to achieve a fully passive and broadband wave control.

    

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4. 3D printed metamaterial absorbers for mid-infrared surface-enhanced spectroscopy

   https://doi.org/10.1063/5.0093332  

   Hendrickson-Stives, Albanie K. ; Kang, Lei ; Donahue, Nicole R. ; Keating, Christine D. ; Werner, Douglas H. ( May 2022 , Applied Physics Letters)

   The resonant nature and geometric scalability make metamaterials an ideal platform for an enhanced light–matter interaction over a broad frequency range. The mid-infrared (IR) spectral range is of great importance for vibrational spectroscopy of molecules, while IR metamaterials created from lithography-based planar nanostructures have been used to demonstrate enhanced molecular detection. Compared with visible and near-infrared, the relative long wavelengths of IR light make it possible to achieve three-dimensional (3D) IR metamaterials via the state-of-the-art 3D fabrication techniques. Here, we design and fabricate a 3D printed plasmonic metamaterial absorber (MMA), and by performing Fourier-transform IR spectroscopy, we demonstrate that a series of molecular fingerprint vibrations of glycine can be significantly enhanced by the high absorption mode supported by the 3D meta-atoms of the MMA. The observed enhanced IR detection can also be partially attributed to the improved accessibility offered by the 3D architecture of the MMA. In particular, due to capillary forces during the drying process, the microscale 3D printed features lead to selective analyte deposition in high-field regions, which provides another degree of freedom in the design of the 3D printed structures for surface-enhanced IR detection. Our study shows the flexibility of metastructures based on advanced 3D printing technology in tailoring the interaction between IR light and materials on a subwavelength scale. 

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5. Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi‐Supervised Learning Strategy

   https://doi.org/10.1002/adma.201901111  

   Ma, Wei ; Cheng, Feng ; Xu, Yihao ; Wen, Qinlong ; Liu, Yongmin ( July 2019 , Advanced Materials)

   The research of metamaterials has achieved enormous success in the manipulation of light in a prescribed manner using delicately designed subwavelength structures, so‐called meta‐atoms. Even though modern numerical methods allow for the accurate calculation of the optical response of complex structures, the inverse design of metamaterials, which aims to retrieve the optimal structure according to given requirements, is still a challenging task owing to the nonintuitive and nonunique relationship between physical structures and optical responses. To better unveil this implicit relationship and thus facilitate metamaterial designs, it is proposed to represent metamaterials and model the inverse design problem in a probabilistically generative manner, enabling to elegantly investigate the complex structure–performance relationship in an interpretable way, and solve the one‐to‐many mapping issue that is intractable in a deterministic model. Moreover, to alleviate the burden of numerical calculations when collecting data, a semisupervised learning strategy is developed that allows the model to utilize unlabeled data in addition to labeled data in an end‐to‐end training. On a data‐driven basis, the proposed deep generative model can serve as a comprehensive and efficient tool that accelerates the design, characterization, and even new discovery in the research domain of metamaterials, and photonics in general.

    

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