Multi-functional switchable terahertz metasurface device prediction by K-nearest neighbor

被引:0
|
作者
Ye, Lipengan [1 ]
Su, Wei [1 ]
Hu, Kun [1 ]
Ding, Zhipeng [1 ]
Hu, Zongli [2 ]
Ren, Rui [1 ]
Tang, Bin [2 ]
Yao, Hongbing [1 ]
机构
[1] Hohai Univ, Coll Mech & Engn Sci, Nanjing 210098, Peoples R China
[2] Changzhou Univ, Sch Microelect & Control Engn, Changzhou 213164, Peoples R China
关键词
Metasurface; Multi-function device; Terahertz; Machine learning; Absorber; Polarization conversion; K -nearest neighbor; BROAD-BAND ABSORPTION; POLARIZATION CONVERSION; GRAPHENE; METAMATERIAL; ABSORBER;
D O I
10.1016/j.cjph.2024.07.016
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
In this paper, we present the design of a versatile terahertz (THz) device characterized by its multi-functional capabilities including broadband absorption and polarization modulation, achieved through leveraging the temperature-induced phase transition behavior of vanadium dioxide (VO2). Operating in the metallic state of VO2, the device exhibits broadband absorption properties, delivering a remarkable total effective absorption of 2.871 THz with absorption rates exceeding 90 % across the frequency ranges of 8.140 9.405 THz and 12.740 14.346 THz. Furthermore, it demonstrates excellent adaptability to large angle incidence. Conversely, in the dielectric state of VO2, the device transitions into a polarization modulation mode, facilitating linear-to-cross-polarization (LTX) and linear-to-circular-polarization (LTC) conversions. Notably, for LTX polarization, the conversion efficiency exceeds 90 % within the 8-11.5 THz range, while for LTC polarization, the ellipticity surpasses 0.8 within the 7.861 8 THz range. Additionally, we introduce a machine learning (ML) approach to optimize the device parameters, presenting a novel strategy for enhancing the design and optimization of future multifunctional devices.
引用
收藏
页码:734 / 742
页数:9
相关论文
共 50 条
  • [21] Weighted Functional K-Nearest Neighbor Method and Application in Close Price Prediction of Online Auction
    He, Zhoushanyue
    PROCEEDINGS OF THE 2010 INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND SCIENTIFIC MANAGEMENT, VOLS 1-2, 2010, : 815 - 818
  • [22] Validation of k-Nearest Neighbor Classifiers
    Bax, Eric
    IEEE TRANSACTIONS ON INFORMATION THEORY, 2012, 58 (05) : 3225 - 3234
  • [23] Quantum K-nearest neighbor algorithm
    Chen, Hanwu
    Gao, Yue
    Zhang, Jun
    Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition), 2015, 45 (04): : 647 - 651
  • [24] Analysis of the k-nearest neighbor classification
    Li, Jing
    Cheng, Ming
    INFORMATION SCIENCE AND MANAGEMENT ENGINEERING, VOLS 1-3, 2014, 46 : 1911 - 1917
  • [25] Weighted K-Nearest Neighbor Revisited
    Bicego, M.
    Loog, M.
    2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2016, : 1642 - 1647
  • [26] A FUZZY K-NEAREST NEIGHBOR ALGORITHM
    KELLER, JM
    GRAY, MR
    GIVENS, JA
    IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1985, 15 (04): : 580 - 585
  • [27] CHROMATIC K-NEAREST NEIGHBOR QUERIES
    van der Horst, Thijs
    Loffler, Maarten
    Staals, Frank
    JOURNAL OF COMPUTATIONAL GEOMETRY, 2025, 16 (01)
  • [28] Hybrid k-Nearest Neighbor Classifier
    Yu, Zhiwen
    Chen, Hantao
    Liu, Jiming
    You, Jane
    Leung, Hareton
    Han, Guoqiang
    IEEE TRANSACTIONS ON CYBERNETICS, 2016, 46 (06) : 1263 - 1275
  • [29] Solar Power Generation Prediction by using k-Nearest Neighbor Method
    Ramli, Nor Azuana
    Hamid, Mohd Fairuz Abdul
    Azhan, Nurul Hanis
    Ishak, Muhammad Alif As-siddiq
    5TH INTERNATIONAL CONFERENCE ON GREEN DESIGN AND MANUFACTURE 2019 (ICONGDM 2019), 2019, 2129
  • [30] Rockburst prediction method based on K-nearest neighbor pattern recognition
    Su Guoshao
    Lei Wenjie
    Zhang Xiaofei
    Progress in Mining Science and Safety Technology, Pts A and B, 2007, : 840 - 845