Deep-learning-enabled inverse engineering of multi-wavelength invisibility-to-superscattering switching with phase-change materials

Inverse design of nanoparticles for desired scattering spectra and dynamic switching between the two opposite scattering anomalies, i.e. superscattering and invisibility, is important in realizing cloaking, sensing and functional devices. However, traditionally the design process is quite complicated, which involves complex structures with many choices of synthetic constituents and dispersions. Here, we demonstrate that a well-trained deep-learning neural network can handle these issues efficiently, which can not only forwardly predict scattering spectra of multilayer nanoparticles with high precision, but also inversely design the required structural and material parameters efficientlyy. Moreover, we show that the neural network is capable of finding out multi-wavelength invisibility-to-superscattering switching points at the desired wavelengths in multilayer nanoparticles composed of metals and phase-change materials. Our work provides a useful solution of deep learning for inverse design of nanoparticles with dynamic scattering spectra by using phase-change materials. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

The inverse design of nanoparticles is crucial for realizing cloaking, sensing, and functional devices. Traditional design processes are complex, but utilizing a well-trained deep-learning neural network can efficiently handle these issues, predict and inversely design the structure and material parameters of nanoparticles.