Machine learning for efficient inverse design of nanophotonics structures

Mohammadreza Zandehshahvar; Mohammad Hadighe Javani; Michael Chen; Tyler Brown; Yashar Kiarashi; Ali Adibi

doi:10.1117/12.2617485

5 March 2022 Machine learning for efficient inverse design of nanophotonics structures

Mohammadreza Zandehshahvar, Mohammad Hadighe Javani, Michael Chen, Tyler Brown, Yashar Kiarashi, Ali Adibi

Proceedings Volume PC12010, Photonic and Phononic Properties of Engineered Nanostructures XII; PC120100W (2022) https://doi.org/10.1117/12.2617485
Event: SPIE OPTO, 2022, San Francisco, California, United States

Abstract

We present a new machine learning (ML)-based approach for efficient inverse design of nanophotonic structures. Generating training data for a ML method is the most computationally expensive step in the ML-based inverse design and knowledge discovery, and it becomes cumbersome when the number of design parameters and the complexity of the structure increase. Here we show how to optimize the training process and considerably reduce the computation requirements without increasing error in order to efficiently model the input-output relationship in a nanophotonic structure and solve the inverse design problem.

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Mohammadreza Zandehshahvar, Mohammad Hadighe Javani, Michael Chen, Tyler Brown, Yashar Kiarashi, and Ali Adibi "Machine learning for efficient inverse design of nanophotonics structures", Proc. SPIE PC12010, Photonic and Phononic Properties of Engineered Nanostructures XII, PC120100W (5 March 2022); https://doi.org/10.1117/12.2617485

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KEYWORDS

Nanophotonics

Machine learning

Structural design

Data modeling

Inverse problems

Knowledge discovery

Nanostructures

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