TY - JOUR
T1 - Subthreshold and reverse bias model of graphene/p-type silicon Schottky diodes
AU - Beltrán, Katty
AU - Paredes, Jhon
AU - Torres, F. Javier
AU - Sánchez, Alfredo
AU - Zambrano, César
AU - Casalino, Maurizio
AU - Prócel, Paul
AU - Isabella, Olindo
AU - Prócel, Luis Miguel
N1 - Publisher Copyright:
© 2025 Vietnam National University, Hanoi
PY - 2025/9
Y1 - 2025/9
N2 - This work presents a novel approach to studying, simulating, and modeling the graphene–silicon interface in Schottky diodes by integrating quantum-mechanical and device-level analyses. Such devices hold great performance potential in photodetecting, energy-harvesting, and sensing applications. Quantum-mechanical calculations determine key structural and electronic properties, such as the work function and effective mass, which are critical for understanding the interface's behavior. These parameters are then incorporated into finite-element simulations, solving the Poisson and Continuity equations to develop a subthreshold and reverse bias model for the graphene/p-type silicon Schottky device. The model characterizes J–V curves, identifying dominant electron transport mechanisms like thermionic emission and diffusion at varying recombination velocities. It also sheds light on the image-force lowering effect, which significantly impacts current density, especially under reverse bias conditions, by modulating the Schottky barrier height. The model is validated by comparing the model with experimental data from graphene–silicon photodetectors, demonstrating its accuracy in predicting device performance. This approach offers valuable insights into optimizing any kind of Schottky diodes. By effectively bridging quantum-mechanical theory with practical device performance, the model proves to be a powerful tool for designing advanced semiconductor devices with enhanced efficiency and functionality, ensuring consistency from the atomistic to the device level.
AB - This work presents a novel approach to studying, simulating, and modeling the graphene–silicon interface in Schottky diodes by integrating quantum-mechanical and device-level analyses. Such devices hold great performance potential in photodetecting, energy-harvesting, and sensing applications. Quantum-mechanical calculations determine key structural and electronic properties, such as the work function and effective mass, which are critical for understanding the interface's behavior. These parameters are then incorporated into finite-element simulations, solving the Poisson and Continuity equations to develop a subthreshold and reverse bias model for the graphene/p-type silicon Schottky device. The model characterizes J–V curves, identifying dominant electron transport mechanisms like thermionic emission and diffusion at varying recombination velocities. It also sheds light on the image-force lowering effect, which significantly impacts current density, especially under reverse bias conditions, by modulating the Schottky barrier height. The model is validated by comparing the model with experimental data from graphene–silicon photodetectors, demonstrating its accuracy in predicting device performance. This approach offers valuable insights into optimizing any kind of Schottky diodes. By effectively bridging quantum-mechanical theory with practical device performance, the model proves to be a powerful tool for designing advanced semiconductor devices with enhanced efficiency and functionality, ensuring consistency from the atomistic to the device level.
KW - Diffusion
KW - Effective mass
KW - Finite-element simulation
KW - Graphene–silicon interface
KW - Image-force lowering
KW - Quantum mechanical analysis
KW - Recombination velocity
KW - Schottky diode
KW - Subthreshold swing
KW - Thermionic emission
KW - Work function
KW - p-type doping
UR - https://www.scopus.com/pages/publications/105008910605
U2 - 10.1016/j.jsamd.2025.100925
DO - 10.1016/j.jsamd.2025.100925
M3 - Artículo
AN - SCOPUS:105008910605
SN - 2468-2284
VL - 10
JO - Journal of Science: Advanced Materials and Devices
JF - Journal of Science: Advanced Materials and Devices
IS - 3
M1 - 100925
ER -