Exploiting machine learning to design nanophotonic scatterers

Carsten Rockstuhl; Yannick Augenstein; Sergei Gladyshev; Theodosios D. Karamanos; Lina Kuhn; Dominik Beutel; Thomas Weiss; Andrey Bogdanov

doi:10.1117/12.3017367

18 June 2024 Exploiting machine learning to design nanophotonic scatterers

Carsten Rockstuhl, Yannick Augenstein, Sergei Gladyshev, Theodosios D. Karamanos, Lina Kuhn, Dominik Beutel, Thomas Weiss, Andrey Bogdanov

Author Affiliations +

Proceedings Volume PC13017, Machine Learning in Photonics; PC1301705 (2024) https://doi.org/10.1117/12.3017367
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

Machine learning techniques using artificial neural networks (ANN) have proven to be extremely ef-fective in designing nanophotonic systems. This presentation focuses on two applications where ANNs are utilized for designing nanophotonic scatterers. In the first scenario, ANNs act as surrogate solvers for Maxwell's equations, allowing the design of scatterers tailored to specific fabrication technologies like laser nanoprinting. Designing low-index material scatterers is complex, so solving the inverse problem multiple times from different starting points is crucial. A Fourier neural operator ANN serves as a surrogate Maxwell solver, simplifying this process. The second scenario integrates ANNs into a holistic metasurface design framework. Individual meta-atoms are efficiently described by their scattering responses, typically expressed as polarizability or T-matrix that provide metasurfaces with functionality on demand. Then, suitably trained ANNs are used to identify feasible physical objects that offer the desired T-matrices.

Conference Presentation

Citation Download Citation

Carsten Rockstuhl, Yannick Augenstein, Sergei Gladyshev, Theodosios D. Karamanos, Lina Kuhn, Dominik Beutel, Thomas Weiss, and Andrey Bogdanov "Exploiting machine learning to design nanophotonic scatterers", Proc. SPIE PC13017, Machine Learning in Photonics, PC1301705 (18 June 2024); https://doi.org/10.1117/12.3017367

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
PRESENTATION

WATCH
PRESENTATION SAVE TO MY LIBRARY

GET CITATION

KEYWORDS

Artificial neural networks

Design and modelling

Machine learning

Nanophotonics

Education and training

Inverse problems

Scattering

Show All Keywords

Keywords/Phrases

Search In:

Publication Years