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20 August 2020 A deep convolutional mixture density network for the inverse design of layered photonic structures
Rohit Unni, Kan Yao, Yuebing Zheng
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Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 1146913 (2020) https://doi.org/10.1117/12.2568021
Event: SPIE Nanoscience + Engineering, 2020, Online Only
Abstract
Machine learning (ML) has emerged in recent years as a data-driven approach for photonic inverse design. Despite their impressive performance in finding abstract mappings between the design parameters and optical properties, ML algorithms suffer from a high likelihood of slow converging when there exist multiple designs giving similar optical responses. Here we adopt a deep convolutional mixture density neural network, which models the design as a mixture of Gaussian distributions rather than discrete values, to address the non-uniqueness issue. An example of layered structures consisting of alternating oxides under arbitrary incidence conditions is present to showcase the proof of concept.
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Rohit Unni, Kan Yao, and Yuebing Zheng "A deep convolutional mixture density network for the inverse design of layered photonic structures", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 1146913 (20 August 2020); https://doi.org/10.1117/12.2568021
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KEYWORDS
Data modeling
Machine learning
Nanophotonics
Neural networks
Optical design
Optical properties
Oxides
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