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13 March 2024 Exploring the nexus of nanophotonics and machine learning: unraveling the influence of design parameters on device response
Mohammad H. Javani, Mohammadreza Zandehshahvar, Tyler Brown, Mahmoodreza Marzban, Ali Adibi
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Proceedings Volume PC12896, Photonic and Phononic Properties of Engineered Nanostructures XIV; PC128960I (2024) https://doi.org/10.1117/12.3012747
Event: SPIE OPTO, 2024, San Francisco, California, United States
Abstract
In the realm of nanophotonics, establishing the intricate relationship between design parameters and the ultimate response of a given nanophotonic devices stands as a formidable challenge. The prevalent utilization of numerical solutions to Maxwell's equations, whether through in-house codes or commercial software, often conceals the underlying physics. In this talk, we present machine-learning (ML) algorithms for elucidating the connection between design parameters and device response. We discuss two distinct ML methods to discern the roles and significance of individual design parameters, namely SHAP (SHapley Additive exPlanations) values and Pruning. By scrutinizing two diverse nano-devices using these complementary techniques, this talk sheds light on the compelling insights derived from this innovative approach.
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Mohammad H. Javani, Mohammadreza Zandehshahvar, Tyler Brown, Mahmoodreza Marzban, and Ali Adibi "Exploring the nexus of nanophotonics and machine learning: unraveling the influence of design parameters on device response", Proc. SPIE PC12896, Photonic and Phononic Properties of Engineered Nanostructures XIV, PC128960I (13 March 2024); https://doi.org/10.1117/12.3012747
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KEYWORDS
Design and modelling
Nanophotonics
Machine learning
Maxwell equations
Numerical analysis
Physics
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