Machine-learning driven design of metasurfaces: learn the physics and not the objective function

Ivan Sekulic; Philipp-Immanuel Schneider; Martin Hammerschmidt; Sven Burger

doi:10.1117/12.3022119

18 June 2024 Machine-learning driven design of metasurfaces: learn the physics and not the objective function

Ivan Sekulic, Philipp-Immanuel Schneider, Martin Hammerschmidt, Sven Burger

Proceedings Volume PC13017, Machine Learning in Photonics; PC130170X (2024) https://doi.org/10.1117/12.3022119
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Conference Poster

Abstract

The efficient design of metasurfaces presents a challenging optimization problem due to the relatively large number of meta-atoms and their mutual coupling. In this work, we present two novel multi-output surrogate models used in the context of the Bayesian optimization of a beam splitter. We show how learning the vectorial quantities forming the final objective can lead to more accurate results and significant speed-ups when compared to classical optimization of scalar objectives. Furthermore, we discuss how to incorporate gradient information with respect to design parameters to further accelerate the optimization.

Citation Download Citation

Ivan Sekulic, Philipp-Immanuel Schneider, Martin Hammerschmidt, and Sven Burger "Machine-learning driven design of metasurfaces: learn the physics and not the objective function", Proc. SPIE PC13017, Machine Learning in Photonics, PC130170X (18 June 2024); https://doi.org/10.1117/12.3022119

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