Nanophotonic structure inverse design for switching application using deep learning.

ABSTRACT: Switching functionality is pivotal in advancing communication systems, serving as a paramount mechanism. Despite numerous innovations in this field, optical switch design, fabrication, and characterization have traditionally followed an iterative approach. Within this paradigm, the designer formulates an informed conjecture regarding the switch's structural configuration and subsequently resolves Maxwell's equations to ascertain its performance. Conversely, the inverse problem, which entails deriving a switch geometry to achieve a targeted electromagnetic response, continues to pose formidable challenges and necessitates substantial time and effort, particularly under the constraints of specific assumptions. In this work, we propose a deep neural network-based method to approximate the spe

SUBMITTER: Adibnia E 

PROVIDER: S-EPMC11387741 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature