Human Skin Permittivity Characterization for Mobile Handset Evaluation at Sub-THz

TL;DR

This paper introduces a novel method for measuring the complex permittivity of human finger skin at sub-terahertz frequencies using an open-ended waveguide and neural network-based inverse modeling, with consistent results across individuals.

Contribution

It presents a new measurement approach combining simulations, simplified modeling, and machine learning to characterize skin permittivity at sub-THz frequencies.

Findings

Permittivity variations are within ±1.5 across individuals and skin regions.

The measurement method shows high repeatability with less than ±1.5% uncertainty.

The approach enables accurate skin permittivity characterization for mobile handset evaluation.

Abstract

This manuscript proposes a method for characterizing the complex permittivity of the human finger skin based on an open-ended waveguide covered with a thin dielectric sheet at sub-terahertz frequencies. The measurement system is initially analyzed through full-wave simulations with a detailed finger model. Next, the model is simplified by replacing the finger with an infinite sheet of human skin to calculate the forward electromagnetic problem related to the permittivity characterization. Following this, a radial basis network is employed to train the inverse problem solver. Finally, the complex permittivities of finger skins are characterized for 10 volunteers. The variations in complex relative permittivity across different individuals and skin regions are analyzed, revealing a deviation of $<\pm 1.5$ for both the dielectric constants and loss factors across 140 to 220 GHz. Repeated measurements at the same location on the finger demonstrate good repeatability with a relative estimation uncertainty $<\pm 1.5\%$.