  The electron density is a key parameter to characterize any plasma. Most of
the plasma applications and research in the area of low-temperature plasmas
(LTPs) are based on the accurate estimations of plasma density and plasma
temperature. The conventional methods for electron density measurements offer
axial and radial profiles for any given linear LTP device. These methods have
major disadvantages of operational range (not very wide), cumbersome
instrumentation, and complicated data analysis procedures. The article proposes
a Deep Learning (DL) assisted microwave-plasma interaction-based non-invasive
strategy, which can be used as a new alternative approach to address some of
the challenges associated with existing plasma density measurement techniques.
The electric field pattern due to microwave scattering from plasma is utilized
to estimate the density profile. The proof of concept is tested for a simulated
training data set comprising a low-temperature, unmagnetized, collisional
plasma. Different types of symmetric (Gaussian-shaped) and asymmetrical density
profiles, in the range $10^{16}-10^{19}$ m$^{-3}$, addressing a range of
experimental configurations have been considered in our study. Real-life
experimental issues such as the presence of noise and the amount of measured
data (dense vs sparse) have been taken into consideration while preparing the
synthetic training data-sets. The DL-based technique has the capability to
determine the electron density profile within the plasma. The performance of
the proposed deep learning-based approach has been evaluated using three
metrics- SSIM, RMSLE, and MAPE. The obtained results show promising performance
in estimating the 2D radial profile of the density for the given linear plasma
device and affirms the potential of the proposed ML-based approach in plasma
diagnostics.
