Deep-Learning Architecture-Based Approach for 2-D-Simulation of Microwave Plasma Interaction

This article presents a convolutional neural network (CNN)-based deep-learning (DL) model, inspired from UNet with a series of encoder and decoder units with skip connections, for the simulation of microwave–plasma interaction. The microwave propagation characteristics in complex plasma medium pertaining to transmission, absorption, and reflection primarily depend on the ratio of electromagnetic (EM) wave frequency and electron plasma frequency, and the plasma density profile. The scattering of a plane EM wave with fixed frequency (1 GHz) and amplitude incident on a plasma medium with different Gaussian density profiles (in the range of
$1 \times10^{17}$

$- 1\times 10^{22} {\text {m}^{-3}}$
) have been considered. The training data associated with microwave–plasma interaction has been generated using 2-D finite-difference time-domain (FDTD)-based simulations. The trained DL model is then used to reproduce the scattered electric field values for the 1-GHz incident microwave on different plasma profiles with an error margin of less than 2%. We propose a complete DL-based pipeline to train, validate, and evaluate the model. We compare the results of the network, using various metrics like structural similarity index metric (SSIM) index, average percent error, and mean square error, with the physical data obtained from well-established FDTD-based EM solvers. To the best of our knowledge, this is the first effort toward exploring a DL-based approach for the simulation of complex microwave–plasma interaction. The DL technique proposed in this work is significantly fast when compared to the existing computational techniques and can be used as a new, prospective, and alternative computational approach for investigating microwave–plasma interaction in a real-time scenario.