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5 March 2021 Machine-learning dimensionality reduction for multi-objective design of photonic devices
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Abstract
Modern design of photonic devices is quickly and steadily departing from classical geometries to focus more and more on non-trivial structures and metamaterials. These devices are governed by a multitude of parameters and the optimal design requires to simultaneously consider different figure of merits. In this invited talk we will present our recent work on the application of machine learning tools to the multi-objective optimization of multi-parameter photonic devices. In particular, we will demonstrate the potentiality of dimensionality reduction for the analysis of the complex design space of subwavelength metamaterials devices.
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© (2021) 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, Siegfried Janz, Ross Cheriton, Jens H. Schmid, Pavel Cheben, and Dan-Xia Xu "Machine-learning dimensionality reduction for multi-objective design of photonic devices", Proc. SPIE 11689, Integrated Optics: Devices, Materials, and Technologies XXV, 116890H (5 March 2021); https://doi.org/10.1117/12.2586717
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