Experimental characterization and numerical modeling of total ionizing dose effects on field oxide MOS dosimeters. (May 2021)

• Record Type: Journal Article
• Authors: Cassani, M.V.; Sambuco Salomone, L.; Carbonetto, S.; Faigón, A.; Redin, E.; Garcia-Inza, M.
• Is Part Of: Radiation physics and chemistry. Volume 182(2021)
• Publication Date: 2021-05
• Subjects: Radiation chemistry -- Periodicals; Radiometry -- Periodicals; Radiation -- Periodicals; Chimie sous rayonnement -- Périodiques; 539.2
• DOI: 10.1016/j.radphyschem.2020.109338
• Languages: English
• ISSNs: 0969-806X

Abstract

Abstract: The response of MOS dosimeters fabricated using field oxide as gate insulator was characterized measuring the threshold voltage shift with absorbed dose. Sensitivity for different applied bias and threshold voltage evolution with dose for constant bias were experimentally obtained. A physics-based numerical model was developed to reproduce these measurements. The model includes the main physical processes leading to hole trapping and neutralization with the capture rate per free hole as the only fitting parameter. The model predicted further experiments of the zero bias sensitivity for an extended dose range. For low threshold voltage values, charge neutralization was observed. Simulations were insensitive to the value of the neutralization-related physical parameter, even during threshold voltage recovery. We showed that this result is related with the presence of a potential well for electrons within the oxide which drives the neutralization process. This dosimeter was compared to other thick oxide ones and different dosimetry considerations were discussed. Highlights: The response of a field oxide MOS dosimeter exposed to 6 MV photons was characterized. Measured sensitivity is comparable to commercial MOS dosimeters with similar oxide thickness. A physics-based numerical model was employed to reproduce experimental results and to study the microscopic process. Once low dose measurements were fitted, the model is able to predict high dose switched bias experiments.