Automatic Inverse Design of High-Performance Beam-Steering Metasurfaces via Genetic-type Tree Optimization

Chia Hsiang Lin, Yu Sheng Chen, Jhao Ting Lin, Hao Chung Wu, Hsuan Ting Kuo, Chen Fu Lin, Peter Chen, Pin Chieh Wu

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


We introduce a genetic-type tree search (GTTS) algorithm combined with unsupervised clustering for the automatic inverse design of high-performance metasurfaces. With the proposed method, we realize highly directive beam-steering metasurfaces via the cooptimization of the amplitude and phase. In comparison with previous topology optimization approaches, the developed GTTS algorithm optimizes the organization of subwavelength nanoantennas and, thus, is applicable to the design of both passive and active metasurfaces. The optimized beam-steering metasurface specifically exhibits a nearly constant directivity when the steering angle varies from 5° to 30°. Furthermore, the optimized nonintuitive reflectance and phase profiles assist in achieving highly directive beam steering when the phase modulation range is <180°, which was previously challenging. Our approach can diminish the requirements of scattering light properties with substantially enhanced angular resolution of beam-steering metasurfaces, which enables the realization of high-performance metasurfaces that will be promising for a wide range of advanced nanophotonic applications.

Original languageEnglish
Pages (from-to)4981-4989
Number of pages9
JournalNano letters
Issue number12
Publication statusAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Chemistry(all)
  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanical Engineering


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