Document Type
Article
Publication Date
4-21-2023
Abstract
Machine learning provides a promising platform for both forward modeling and the inverse design of photonic structures. Relying on a data-driven approach, machine learning is especially appealing for situations when it is not feasible to derive an analytical solution for a complex problem. There has been a great amount of recent interest in constructing machine learning models suitable for different electromagnetic problems. In this work, we adapt a region-specified design approach for the inverse design of multilayered nanoparticles. Given the high computational cost of dataset generation for electromagnetic problems, we specifically investigate the case of a small training dataset, enhanced via random region specification in an inverse convolutional neural network. The trained model is used to design nanoparticles with high absorption levels and different ratios of absorption over scattering. The central design wavelength is shifted across 350–700 nm without re-training. We discuss the implications of wavelength, particle size, and the training dataset size on the performance of the model. Our approach may find interesting applications in the design of multilayer nanoparticles for biological, chemical, and optical applications as well as the design of low-scattering absorbers and antennas.
Recommended Citation
Alex Vallone et al 2023 J. Phys. Photonics 5 24002
https://doi.org/10.1088/2515-7647/acc7e5
Copyright
The authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Included in
Nanoscience and Nanotechnology Commons, Other Computer Engineering Commons, Other Electrical and Computer Engineering Commons
Comments
This article was originally published in Journal of Physics: Photonics, volume 5, in 2023. https://doi.org/10.1088/2515-7647/acc7e5