A neural network approach to kinetic Mie polarimetry for particle size diagnostics in nanodusty plasmas

Abstract

The analysis of the size of nanoparticles is an essential task in plasma technology and dusty plasmas. Light scattering techniques, based on Mie theory, can be used as a non-invasive and in-situ diagnostic tool for this purpose. However, the standard back-calculation methods require expertise from the user. To address this, we introduce a neural network that performs the same task. We discuss how we set up and trained the network to analyze the size of plasma-grown amorphous carbon nanoparticles (a:C–H) with a refractive index n in the range of real(n) = and imag(n) = and a radius of up to several hundred nanometers, depending on the used wavelength. The diagnostic approach is kinetic, which means that the particles need to change in size due to growth or etching. An uncertainty analysis as well as a test with experimental data are presented. Our neural network achieves results that agree with those of prior fitting algorithms while offering higher methodical stability. The model also holds a major advantage in terms of computing speed and automation.