Measurement of Material Volume Fractions in a Microwave Resonant Cavity Sensor Using Convolutional Neural Network

TL;DR

This paper introduces a CNN-based method for real-time, non-destructive estimation of dielectric mixture volume fractions inside a microwave resonant cavity, achieving high accuracy with simulated and experimental data.

Contribution

It presents a novel CNN approach that accurately estimates material fractions from S-parameters without de-embedding, demonstrating robustness and high predictive accuracy.

Findings

Simulation $R^2$=0.99, MAE below 6%

Experimental $R^2$=0.94, MAE below 6%

Data augmentation improves $R^2$ to above 0.998

Abstract

A non-destructive, real-time method for estimating the volume fraction of a dielectric mixture inside a resonant cavity is presented. A convolutional neural network (CNN)-based approach is used to estimate the fractional composition of two-phase dielectric mixtures inside a resonant cavity using scattering parameter (S-parameter) measurements. A rectangular cavity sensor with a strip feed structure is characterized using a vector network analyzer (VNA) from 0.01--20~GHz. The CNN is trained using both simulated and experimentally measured S-parameters and achieves high predictive accuracy even without de-embedding or filtering, demonstrating robustness to measurement imperfections. The simulation results achieve a coefficient of determination ($R^2$)=0.99 using $k$-fold cross-validation, while the experimental model using raw data achieves an $R^2=0.94$ with a mean absolute error (MAE) below 6\%. Data augmentation further improves the accuracy of the experimental prediction to above $R^2=0.998$ (MAE$<$0.72\%). The proposed method enables rapid, non-destructive, accurate, low-cost, and real-time estimation of material fractions, illustrating strong potential for sensing applications in microwave material characterization.