The p-GaN gate high electron mobility transistor (HEMT) with a 2.0 × 10^-5 cm² sensitive area as a UV photodetector (PD) has been designed and fabricated in this study. AlGaN/gallium nitride (GaN) heterostructure was adopted to get a 2-D electron gas (2DEG) as a conductive channel, resulting in a high photoresponsivity of 8.07 × 10⁴ A/W, a sharp cutoff wavelength at 360 nm, high UV-to-visible rejection ratio of 1.80 × 10⁶, rise and decay time of 0.12 and 1.0 ms, respectively. The dark current of 5.44 × 10^-7 A, the photocurrent of 4.42 × 10^-3 A at 5 V, the external quantum efficiency (EQE) of 2.77% × 10⁵%, and the detectivity of 8.31 × 10¹⁴ Jones for the UV PD were determined under a low UV light intensity of 5 mW/cm² at 360-nm UV illumination. The obtained results show that the performance of as-fabricated UV PD based on AlGaN/GaN HEMT is significantly improved compared to the literature. This device, which has lower noise equivalent power (NEP) and enhanced detectivity features compared to existing ones, is a promising candidate for military and space applications.

We report on a procedure for extracting the SPICE model parameters of a RADFET sensor with a dielectric HfO₂/SiO₂ double-layer. RADFETs, traditionally fabricated as PMOS transistors with SiO₂, are enhanced by incorporating high-k dielectric materials such as HfO₂ to reduce oxide thickness in modern radiation sensors. The fabrication steps of the sensor are outlined, and model parameters, including the threshold voltage and transconductance, are extracted based on experimental data. Experimental setups for measuring electrical characteristics and irradiation are described, and a method for determining model parameters dependent on the accumulated dose is provided. A SPICE model card is proposed, including parameters for two dielectric thicknesses: (30/10) nm and (40/5) nm. The sensitivities of the sensors are 1.685 mV/Gy and 0.78 mV/Gy, respectively. The model is calibrated for doses up to 20 Gy, and good agreement between experimental and simulation results validates the proposed model.