Deep learning-driven forward and inverse design of nanophotonic nanohole arrays: streamlining design for tailored optical functionalities and enhancing accessibility-Reference-Cited by-同舟云学术

Deep learning-driven forward and inverse design of nanophotonic nanohole arrays: streamlining design for tailored optical functionalities and enhancing accessibility

Published:2024 Issue:35 Volume:16 Page:16641-16651
ISSN:2040-3364
Container-title:Nanoscale
language:en
Short-container-title:Nanoscale

Author:

Jahan Tasnia¹,Dash Tomoshree¹,Arman Shifat E.²,Inum Reefat³,Islam Sharnali¹^ORCID,Jamal Lafifa²,Yanik Ahmet Ali³,Habib Ahsan¹⁴^ORCID

Affiliation:

1. Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka-1000, Bangladesh

2. Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka-1000, Bangladesh

3. Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA-95064, USA

4. Dhaka University Nanotechnology Center, University of Dhaka, Dhaka-1000, Bangladesh

Abstract

This study develops and optimizes deep learning models for the forward and inverse design of nanophotonic nanohole arrays, demonstrating their effectiveness through both FDTD simulations and experimental validation.

Funder

University of Dhaka

National Science Foundation

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/NR/D4NR03081H

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