Classification and Inverse Design of Metasurface Absorber in Visible Band. Issue 3 (7th December 2021)

• Record Type: Journal Article
• Authors: Lu, Xuehua; Li, Wenbin; Zhu, Zhihui; Hu, Yongqiang; Tang, Ziyi; Zhang, Wenpeng; Liu, Ke; Su, Yarong; Zheng, Jie; Chen, Weidong; Tang, Mingjun; Xie, Zhengwei; Huang, Yijia; Li, Ling
• Is Part Of: Advanced theory and simulations. Volume 5:Issue 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-07
• Subjects: classification network -- deep learning -- inverse design -- metasurface absorber -- prediction network; Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.202100338
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract: Metasurface absorber (MA) has been a research hotspot in the field of artificial electromagnetic structural material due to its dual advantages of high performance and compact design. Usually, the design of MA depends on the designer's professional knowledge, experience and physical inspiration. The desired optical response can be obtained by using electromagnetic simulation software to carry out hundreds or thousands of numerical calculations. Thus, it is still a challenge to quickly retrieve the optimal structure according to the desired optical response and realize the on‐demand inverse design. Besides, limited by the inner physics of the MA, it is not always possible to find the structural parameters corresponding to the desired spectrum. This paper not only takes the planar geometry and thickness of the structures into account but also realizes the probability classification of the desired spectra through the classification network. According to the classification results, the prediction network of the corresponding is selected to realize the on‐demand inverse design of MA. The proposed network model can design MA rapidly and accurately in a data‐driven way and can be flexibly applied to the design of other data‐enabled photonic devices, which is promising to become a comprehensive and effective design tool. Abstract : We have shortened the TOC text as "In this paper, a hybrid deep learning model is proposed for the efficient design of metasurface absorber (MA) that can realize the probability detection of the desired spectrum through the classification network and the on‐demand inverse design of the MAs through the prediction network. The predicted spectra for the hand‐drawn input are in good agreement with the desired ones.