Abstract

Metasurfaces, which consist of arrays of ultrathin planar nanostructures (also known as “meta-atoms”), offer immense potential for use in high-performance optical devices through the precise manipulation of electromagnetic waves with subwavelength spatial resolution. However, designing meta-atom structures that simultaneously meet multiple functional requirements (e.g., for multiband or multiangle operation) is an arduous task that poses a significant design burden. Therefore, it is essential to establish a robust method for producing intricate meta-atom structures as functional devices. To address this issue, we developed a rapid construction method for a multifunctional and fabrication-friendly meta-atom library using deep neural networks coupled with a meta-atom selector that accounts for realistic fabrication constraints. To validate the proposed method, we successfully applied the approach to experimentally demonstrate a dual-band metasurface collimator based on complex free-form meta-atoms. Our results qualify the proposed method as an efficient and reliable solution for designing complex meta-atom structures in high-performance optical device implementations.

Details

Title
Dual-band optical collimator based on deep-learning designed, fabrication-friendly metasurfaces
Author
Ueno, Akira 1 ; Hung-I, Lin 2 ; Yang, Fan 3 ; An, Sensong 3 ; Martin-Monier, Louis 3 ; Shalaginov, Mikhail Y 2 ; Gu, Tian 4 ; Hu, Juejun 4 

 Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Innovative Technology Laboratories, AGC Inc., Yokohama, Japan 
 Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; 2Pi Inc., Cambridge, MA, USA 
 Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 
 Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; 2Pi Inc., Cambridge, MA, USA; Materials Research Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 
Pages
3491-3499
Publication year
2023
Publication date
2023
Publisher
Walter de Gruyter GmbH
ISSN
21928606
e-ISSN
21928614
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2851555933
Copyright
© 2023. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.