Modelling of Terahertz IMPATT Diodes Based on GaN Using Artificial Neural Networks
This paper explores the application of artificial neural networks (ANNs) to predict static and dynamic performance metrics of GaN impact avalanche transit time (IMPATT) diodes operating in the terahertz (THz) frequency range. Using datasets derived from self-consistent quantum drift-diffusion (SCQDD...
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Published in | Devices for Integrated Circuit pp. 1 - 4 |
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Main Authors | , , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
05.04.2025
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Subjects | |
Online Access | Get full text |
ISSN | 2996-3044 |
DOI | 10.1109/DevIC63749.2025.11012390 |
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Summary: | This paper explores the application of artificial neural networks (ANNs) to predict static and dynamic performance metrics of GaN impact avalanche transit time (IMPATT) diodes operating in the terahertz (THz) frequency range. Using datasets derived from self-consistent quantum drift-diffusion (SCQDD) simulations, the ANN models capture key parameters like breakdown voltage (V B ), RF power output (PRF), and DC-to-RF conversion efficiency (η L ), with significantly reduced computational time. The results show a reduction of computational time by 80%, while achieving accuracy within 10 -4 to 10 -6 in prediction errors. |
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ISSN: | 2996-3044 |
DOI: | 10.1109/DevIC63749.2025.11012390 |