Speaker
Description
In radiation protection, accurately assessing individual exposure relies on operational quantities such as $H_p(10)$ and $H_p(3)$, which serve as measurable surrogates for the effective dose. However, designing compact electronic dosimeters that remain accurate across a wide range of photon energies and incidence angles is a significant challenge, especially when prioritizing low-cost and accessible technology.
This study explores the numerical design of a MOSFET-based personal dosimeter through Monte Carlo simulations using Geant4. Rather than treating the device as a simplified model, we developed a physically consistent representation, incorporating the silicon sensitive region, oxide layers, and surrounding housing, to pinpoint how specific materials and geometries influence energy deposition.
To account for the variability of parameters inherent to the device's physical construction, we systematically evaluated a variety of configurations by adjusting filter compositions, thicknesses, and detector layouts. Each setup was tested against simulated photon beams spanning diagnostic X-rays to standard gamma sources, following standardized irradiation geometries inspired by ICRU recommendations. These simulations allowed us to map the device’s response functions as a function of both photon energy and angle of incidence.
The detector's architecture was iteratively optimized to align its energy response with the target $H_p(10)$ and $H_p(3)$ quantities. By minimizing the discrepancy between the energy deposited in the sensor and the dose delivered to the body, we aimed to refine a design that is suitable for the development of affordable MOSFET-based dosimeters for routine personal monitoring.
Since Geant4 tracks energy deposition rather than the electrical behavior of the transistor, we also examine the coupling of Monte Carlo results with TCAD-based semiconductor simulations. This bridge allows us to relate physical energy deposition to a measurable threshold voltage shift through a simplified device model.