Enhancing Magneto‐Dielectric Material Characterization by Integrating SRR Sensor, De‐Embedding Procedure, and Artificial Neural Network Modeling

This work presents an improved methodology for characterizing the effective permittivity ( ε ) and permeability ( μ ) of magnetodielectric (MD) composites, using Fe 3 O 4 nanoparticles dispersed in a PDMS polymer matrix. The proposed approach integrates a planar split‐ring resonator sensor design and an artificial neural network model for parameter extraction, complemented by a line‐line deembedding methodology to eliminate parasitic effects from measurements. The experimental results are compared with predictions derived from the Maxwell‐Garnett and Polder‐Van Santen effective medium models, demonstrating a close agreement between theory and measurements. This study highlights the importance of accurate experimental procedures and advanced modeling techniques in understanding the electromagnetic behavior of MD composites and offers insights into their potential for developing next‐generation microwave components.