Presentation
5 March 2021 Advancing photonics with machine learning
Author Affiliations +
Abstract
Discovering unconventional optical designs via machine-learning promises to advance on-chip circuitry, imaging, sensing, energy, and quantum information technology. In this talk, photonic design approaches and emerging material platforms will be discussed showcasting machine-learning-assisted topology optimization for thermophotovoltaic metasurface designs and machine-learning-enabled quantum optical measurements.
Conference Presentation
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alexandra Boltasseva, Vladimir Shalaev, and Zhaxylyk Kudyshev "Advancing photonics with machine learning", Proc. SPIE 11694, Photonic and Phononic Properties of Engineered Nanostructures XI, 116940L (5 March 2021); https://doi.org/10.1117/12.2589478
Advertisement
Advertisement
KEYWORDS
Machine learning

Information technology

Optical design

Optical testing

Quantum information

RELATED CONTENT

Advancing photonic design with machine learning
Proceedings of SPIE (January 01 1900)
Reflections on the teaching of applied optics
Proceedings of SPIE (March 01 1992)
New type of prismatic telescope
Proceedings of SPIE (September 15 1995)

Back to Top