A new publication from Opto-Electronic Advances, 10.29026/oea.2023.220148 discusses the direct field-to-pattern monolithic design of holographic metasurface via residual encoder-decoder convolutional neural network.
Metasurfaces, as two-dimensional metamaterials, display fascinating ability in electromagnetic (EM) modulation within a sub-wavelength scale, opening up a new way for manipulating the properties of EM wave in a plane. Owing to the flexible modulation of EM wave, metasurfaces have spawned a number of enchanting applications, such as perfect absorbers [6,7], cloaking devices, planar meta-lens and meta-hologram. The emergence of metasurface exhibits extraordinary capabilities in developing and improving optical and microwave devices. Many fascinating works are springing up, which has greatly enriched the design and application of holograms. According to the information modulation mode, metasurface holography can be roughly divided into three categories: phase-only, amplitude-only and complex-amplitude metasurface holography. In traditional hologram designs, the phase and amplitude distributions of the metasurface are derived based on Huygens-Fresnel theory, and then the target hologram distribution is filled by designing metasurface elements with a specified EM response to complete the design of the metasurface. However, due to the coupling between elements and the error of elements, the theoretical results of diffraction theory are slightly different from the actual ones. Therefore, further numerical simulation is needed to verify the hologram, which consumes computational resources and increases the complexity of the design. Therefore, simplifying the traditional design process will further improve the design efficiency of metasurface. The popularization of machine learning in the field of material design provides a new way to simplify the design of metasurface. In recent years, machine learning has been widely used in metasurface design and application. In metasurface design, the most common design methods are applied at the microstructure level, including forward design and inverse design of metasurface, that is, EM response can be deduced directly from the structure and geometric parameters can be deduced from the EM spectrum. Machine learning effectively speeds up the design efficiency of metasurfaces by replacing the simulation process. However, traditional methods are mainly concerned with the optimization of the EM response of the cells, and the optimized cells are arranged according to the specified distribution to achieve the specified function, which means that the coupling between the cells is ignored. Thus, metasurfaces can be designed from a macroscopic perspective. By analyzing the spatial distribution of the local field, the internal law of the local field can be accurately explored and the mapping relationship between the local field and the modulator can be reconstructed. Therefore, the design efficiency and precision of metasurface can be further improved by analyzing the electric field distribution from a macroscopic perspective and monolithically generating metasurface structure through machine learning method.
Complex-amplitude holographic metasurfaces (CAHMs) with the flexibility in modulating phase and amplitude profiles have been used to manipulate the propagation of wavefront with an unprecedented level, leading to higher image-reconstruction quality compared with their natural counterparts. However, prevailing design methods of CAHMs is based on Huygens-Fresnel theory, meta-atom optimization, numerical simulation, and experimental verification, which results in a consumption of computing resources. Here, the researchers Prof. Wang and Prof. Qu from Air Force Engineering University applied residual encoder-decoder convolutional neural network to directly map the electric field distributions and input images for monolithic metasurface design. A pretrained network is firstly trained by the electric field distributions calculated by diffraction theory, which is subsequently migrated as transfer learning framework to map the simulated electric field distributions and input images. The training results show that the normalized mean pixel error is about 3% on dataset. As verification, the metasurface prototypes are fabricated, simulated, and measured. The reconstructed electric field of reverse-engineered metasurface exhibits high similarity to the target electric field, which demonstrates the effectiveness of our design. Encouragingly, this work provides a monolithic field-to-pattern design method for CAHMs, which paves a new route for the direct reconstruction of metasurfaces.
Keywords: metasurface / holography / complex amplitude / deep learning / monolithic design
# # # # # #
The research team of Shaanxi Key Laboratory of Artificially Structured Functional Materials and Devices of Air Force Engineering University is composed of Shaanxi Provincial Major Basic Research Innovation Team, Shaanxi Provincial Efficient Youth Innovation Team, Subject Highland/high-end Platform construction team of Air Force Engineering University, which is oriented to the needs of military national defense construction. A scientific and technological innovation team whose core research direction and characteristic research direction are to solve the key problems of the design, preparation and application development of new artificial structural functional materials. The team has published more than 400 SCI papers with more than 10,000 citations, published the first domestic monographs on metamaterial stealth technology, and authorized 33 national invention patents. He has won one Natural Science Award and one second Prize of the Ministry of Education, one second Prize of Natural Science of Shaanxi Province and three Outstanding Academic paper awards of Natural Science of Shaanxi Province. In recent years, the team has undertaken various scientific research projects at the national, military and provincial levels, covering multiple research directions of artificial structural functional materials and their applications, and has achieved a series of research achievements.
# # # # # #
Opto-Electronic Advances (OEA) is a high-impact, open access, peer reviewed monthly SCI journal with an impact factor of 8.933 (Journal Citation Reports for IF2021). Since its launch in March 2018, OEA has been indexed in SCI, EI, DOAJ, Scopus, CA and ICI databases over the time and expanded its Editorial Board to 36 members from 17 countries and regions (average h-index 49).
The journal is published by The Institute of Optics and Electronics, Chinese Academy of Sciences, aiming at providing a platform for researchers, academicians, professionals, practitioners, and students to impart and share knowledge in the form of high quality empirical and theoretical research papers covering the topics of optics, photonics and optoelectronics.
# # # # # #
More information: http://www.oejournal.org/oea
Editorial Board: http://www.oejournal.org/oea/editorialboard/list
All issues available in the online archive (http://www.oejournal.org/oea/archive).
Submissions to OEA may be made using ScholarOne (https://mc03.manuscriptcentral.com/oea).
ISSN: 2096-4579
CN: 51-1781/TN
Contact Us: oea@ioe.ac.cn
Twitter: @OptoElectronAdv (https://twitter.com/OptoElectronAdv?lang=en)
WeChat: OE_Journal
# # # # # #
Zhu RC, Wang JF, Qiu TS, Yang DK, Feng B et al. Direct field-to-pattern monolithic design of holographic metasurface via residual encoder-decoder convolutional neural network. Opto-Electron Adv 6, 220148 (2023). doi: 10.29026/oea.2023.220148
# # # # # #