Broadband Solar Metamaterial Absorbers Empowered by Transformer-Based Deep 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.

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.

Wide-incident-angle, polarization-independent broadband-absorption metastructure without external resistive elements by using a trapezoidal structure

The absorption of electromagnetic waves in a broadband frequency range with polarization insensitivity and incidence-angle independence is greatly needed in modern technology applications. Many structures based on metamaterials have been suggested for addressing these requirements; these structures were complex multilayer structures or used special materials or external electric components, such as resistive ones. In this paper, we present a metasurface structure that was fabricated simply by employing the standard printed-circuit-board technique but provides a high absorption above 90% in a broadband frequency range from 12.35 to 14.65 GHz. The metasurface consisted of structural unit cells of 4 symmetric substructures assembled with a metallic bar pattern, which induced broadband absorption by using a planar resistive interaction in the pattern without a real resistive component. The analysis, simulation, and measurement results showed that the metasurface was also polarization insensitive and still maintained an absorption above 90% at incident angles up to 45°. The suggested metasurface plays a role in the fundamental design and can also be used to design absorbers at different frequency ranges. Furthermore, further enhancement of the absorption performance is achieved by improved design and fabrication.

Metasurface inverse designed by deep learning for quasi-entire terahertz wave absorption

Ultra-broadband and efficient terahertz (THz) absorption is of paramount importance for the development of high-performance detectors. These detectors find applications in next-generation wireless communications, military radar systems, security detection, medical imaging, and various other domains. In this study, we present an ultra-wideband THz wave metasurface absorber (UTWMA) featuring a composite surface microstructure and a multilayer absorbing material (graphene). This UTWMA demonstrates remarkable capabilities by achieving highly efficient absorption levels, reaching 96.33%, within the 0.5-10 THz frequency range. To enhance the efficiency and precision of the design process, we have incorporated artificial neural networks, which enable rapid and accurate parameter selection. Moreover, we have conducted a comprehensive analysis of the absorption mechanism exhibited by the UTWMA at different frequencies. This analysis combines insights from the electric field distribution and effective medium theory. The findings presented in this paper are expected to catalyze further research in the domain of broadband THz technology, particularly in the context of metasurfaces and related fields. Additionally, this work paves the way for the development of compact, supercontinuous THz photovoltaic or photothermal electrical 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.