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Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes

Song, Jialin and Tokpanov, Yury S. and Chen, Yuxin and Fleischman, Dagny and Fountaine, Kate T. and Atwater, Harry A. and Yue, Yisong (2018) Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes. . (Submitted)

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We apply numerical methods in combination with finite-difference-time-domain (FDTD) simulations to optimize transmission properties of plasmonic mirror color filters using a multi-objective figure of merit over a five-dimensional parameter space by utilizing novel multi-fidelity Gaussian processes approach. We compare these results with conventional derivative-free global search algorithms, such as (single-fidelity) Gaussian Processes optimization scheme, and Particle Swarm Optimization---a commonly used method in nanophotonics community, which is implemented in Lumerical commercial photonics software. We demonstrate the performance of various numerical optimization approaches on several pre-collected real-world datasets and show that by properly trading off expensive information sources with cheap simulations, one can more effectively optimize the transmission properties with a fixed budget.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Fountaine, Kate T.0000-0002-0414-8227
Atwater, Harry A.0000-0001-9435-0201
Yue, Yisong0000-0001-9127-1989
Record Number:CaltechAUTHORS:20190205-101105728
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:92659
Deposited By: Tony Diaz
Deposited On:05 Feb 2019 19:09
Last Modified:09 Mar 2020 13:19

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