Presentation
5 March 2022 Dimensionality reduction and optimization for the inverse design of photonic integrated devices
Author Affiliations +
Abstract
The widespread use of metamaterials and non-trivial geometries has radically changed the way photonic integrated devices are developed, opening new design possibility and allowing for unprecedented performance. Yet, these devices are often described by a large number of interrelated parameters which cannot be handled manually, requiring innovative design approaches for their effective optimization. In this invited talk, we will discuss the potentiality offered by the combination of machine learning dimensionality reduction and multi-objective optimization for the design of high performance photonic integrated devices.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Daniele Melati, Mohsen Kamandar Dezfouli, Yuri Grinberg, Muhammad Al-Digeil, Dan-Xia Xu, Jens Schmid, Pavel Cheben, Abi Waqas, Paolo Manfredi, Shahrzad Khajavi, Winnie N. Ye, Paula Nuño-Ruano, Jianhao Zhang, Eric Cassan, Delphine Marris-Morini, Laurent Vivien, and Carlos Alonso-Ramos "Dimensionality reduction and optimization for the inverse design of photonic integrated devices", Proc. SPIE PC12005, Smart Photonic and Optoelectronic Integrated Circuits 2022, PC120050D (5 March 2022); https://doi.org/10.1117/12.2617543
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KEYWORDS
Photonic devices

Machine learning

Metamaterials

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