Open AccessInverse design of photonic nanostructures using dimensionality reduction: reducing the computational complexity
Author(s) -
Mohammadreza Zandehshahvar,
Yashar Kiarashi,
Michael Chen,
Reid Barton,
Ali Adibi
Publication year - 2021
Publication title -
optics letters/optics index
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.524
H-Index - 272
eISSN - 1071-2763
pISSN - 0146-9592
DOI - 10.1364/ol.425627
Subject(s) - dimensionality reduction , inverse , computational complexity theory , curse of dimensionality , reduction (mathematics) , photonic crystal , photonics , computer science , nanostructure , artificial neural network , inverse problem , dielectric , materials science , algorithm , optics , artificial intelligence , mathematics , nanotechnology , optoelectronics , physics , mathematical analysis , geometry
In this Letter, we present a deep-learning-based method using neural networks (NNs) for inverse design of photonic nanostructures. We show that by using dimensionality reduction in both the design and the response spaces, the computational complexity of the inverse design algorithm is considerably reduced. As a proof of concept, we apply this method to design multi-layer thin-film structures composed of consecutive layers of two different dielectrics and compare the results using our techniques to those using conventional NNs.