Presentation
24 May 2022 Advancing photonic design 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 optical metasurface designs for applications in thermophotovoltaics, reflective optics and more.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alexandra Boltasseva "Advancing photonic design with machine learning", Proc. SPIE PC12130, Metamaterials XIII, PC121300R (24 May 2022); https://doi.org/10.1117/12.2624334
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KEYWORDS
Machine learning

Optical design

Thermography

Information technology

Quantum information

Reflectivity

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