Microwave Dielectric Property Retrieval From Open-Ended Coaxial Probe Response With Deep Learning

This work presents a technique for dielectric property retrieval through Debye parameter reconstruction from open-ended coaxial probe (OECP) response. Debye parameters were obtained with the application of a deep learning (DL) model to the reflection coefficient response of the OECP when terminated with a material under test. The OECP was modelled with the well-known admittance technique from 0.5 to 6 GHz with 20 MHz resolution. A dataset was generated using the admittance technique and obtained data was utilized to design the DL model. As part of the standard procedure, the dataset was separated to train, validate, and test parts by allocating the 80%, 10%, and 10% of the dataset to each section, respectively. Obtained percent relative error for Debye parameters were 1.86 +/- 3.01%, 3.33 +/- 9.52%, and 2.07 +/- 7.42% for epsilon(s), epsilon(infinity) and tau, respectively. To further test the constructed DL model, OECP responses were measured at the same frequency band when it was terminated with five different standard liquids, four mixtures, and a gel-like material. Reconstructed Debye parameters from the DL model were used to retrieve the complex dielectric properties and obtained results were compared with the literature data. Obtained mean percent relative error was ranging from 1.21 +/- 0.06 to 10.89 +/- 0.08 within the frequency band of interest.

Automated summary

This work presents a technique for retrieving dielectric properties using reconstructed Debye parameters from the response of an open-ended coaxial probe. The technique involves applying a deep learning model to calculate the Debye parameters based on the reflection coefficient response of the probe. The results show that the technique achieves high accuracy in dielectric property retrieval, as validated by comparison with literature data.