journal article·2025-09Open access

Subthreshold and reverse bias model of graphene/p-type silicon Schottky diodes

Katty Beltrán, Jhon Paredes, F. Javier Torres, Alfredo Sánchez, César Zambrano, Maurizio Casalino, Paul Prócel, Olindo Isabella, Luis Miguel Prócel

Abstract

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.
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Research context

OpenAlex topics: Graphene research and applications · Semiconductor materials and interfaces · Nanowire Synthesis and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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