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Título
A Deep Learning-Monte Carlo Combined Prediction of Side-Effect Impact Ionization in Highly Doped GaN Diodes
Autor(es)
Materia
Gunn diodes
doped GaN
THz generation
Monte Carlo simulations
electronic transport
artificial intelligence (AI)
deep learning
Clasificación UNESCO
1203.04 Inteligencia Artificial
2203.06 Transporte de Electrones
Fecha de publicación
2023
Citación
S. García-Sánchez, R. Rengel, S. Pérez, T. González and J. Mateos, "A Deep Learning-Monte Carlo Combined Prediction of Side-Effect Impact Ionization in Highly Doped GaN Diodes," in IEEE Transactions on Electron Devices, vol. 70, no. 6, pp. 2981-2987, June 2023, doi: 10.1109/TED.2023.3265625.
Resumen
The existence of leakage current pathways leading to the appearance of impact ionization and the potential device breakdown in planar Gunn GaN diodes is analyzed by means of a combined Monte Carlo-deep learning approach. Front-view (lateral) Monte Carlo simulations of the devices show the appearance of a high-field hotspot at the anode corner of the etched region, just at the boundaries between the dielectric, the GaN-doped layer, and the buffer. Thus, if the isolation created by the etched trenches is not complete, a relevant hot carrier population within the buffer is observed at sufficiently high applied voltages, provoking the appearance of a very significant number of impact ionizations and the consequent avalanche process before the onset of Gunn oscillations. A neural network trained from Monte Carlo simulations allows predicting with extremely good precision the breakdown voltage of the diodes depending on the doping of the GaN active layer, the permittivity of the isolating dielectric, and the lattice temperature. Low doping, high temperature and high permittivity provide larger operational voltages, which implies a tradeoff with the conditions required to achieve THz Gunn oscillations at low voltages.
URI
ISSN
0018-9383
DOI
10.1109/TED.2023.3265625
Versión del editor
Colecciones
- GINEAF. Artículos [85]