Machine learning classification for field distributions of photonic modes

Abstract

Machine learning techniques can reveal hidden structure in large data amounts and can potentially extent or even replace analytical scientific methods. In nanophotonics, modes can increase the light yield from emitters located inside the nanostructure or near the surface. Optimizing such systems enforces to systematically analyze large amounts of three-dimensional field distribution data. We present a method based on finite element simulations and machine learning for the identification of modes with large field energies and specific spatial properties. By clustering we reduce the field distribution data to a minimal subset of prototypes. The predictive power of the approach is demonstrated using an analysis of experimentally measured fluorescence enhancement of quantum dots on a photonic crystal surface. The clustering method can be used for any optimization task that depends on three-dimensional field data, and is therefore relevant for biosensing, quantum dot solar cells or photon upconversion.

arXiv comment31 pages, 5 figures

References in corpus (7)

• Scikit-learn: Machine Learning in Python
• Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer
• Nanophotonic Computational Design
• Application of machine learning for hematological diagnosis
• Adaptive Finite Element Method for Simulation of Optical Nano Structures
• Inverse Design of Near Unity Efficiency Perfectly Vertical Grating Couplers
• Increased Fluorescence of PbS Quantum Dots in Photonic Crystals by Excitation Enhancement

Cited by in corpus (3)

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• Deterministic and probabilistic deep learning models for inverse design of broadband acoustic cloak
• Nanophotonic enhanced two-photon excited photoluminescence of perovskite quantum dots