Accelerated Prediction of Terahertz Performance Metrics in GaN IMPATT Sources via Artificial Neural Networks
This work investigates the application of artificial neural network (ANN)-based regression models to predict the static and dynamic characteristics of GaN impact avalanche transit time (IMPATT) sources in the terahertz (THz) frequency regime. A comprehensive dataset, derived from self-consistent qua...
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2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10988773/ |
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| author | Santu Mondal Sneha Ray Aritra Acharyya Rudra Sankar Dhar Arindam Biswas Hiroaki Satoh Gurudas Mandal Vitaliy Maksimenko Victor Krishtop |
| author_facet | Santu Mondal Sneha Ray Aritra Acharyya Rudra Sankar Dhar Arindam Biswas Hiroaki Satoh Gurudas Mandal Vitaliy Maksimenko Victor Krishtop |
| author_sort | Santu Mondal |
| collection | DOAJ |
| description | This work investigates the application of artificial neural network (ANN)-based regression models to predict the static and dynamic characteristics of GaN impact avalanche transit time (IMPATT) sources in the terahertz (THz) frequency regime. A comprehensive dataset, derived from self-consistent quantum drift-diffusion (SCQDD) simulations of GaN IMPATT structures designed for a wide frequency range from the microwave frequency bands, up to 5 THz, is used to train the ANN models. The models effectively capture the impact of variations in structural, doping, and biasing parameters on device performance. The proposed ANN approach significantly reduces computational time for predicting breakdown characteristics, power output, and conversion efficiency properties of IMPATT sources, achieving similar accuracy to traditional SCQDD simulations while requiring only 7.8–20.1% of the computational time. Mean square errors are observed to be on the order of <inline-formula> <tex-math notation="LaTeX">$10^{-4}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$10^{-6}$ </tex-math></inline-formula>, demonstrating the models’ high accuracy. Experimental validation shows strong agreement in terms of breakdown voltage, power output, and efficiency, supporting the potential of machine learning to streamline the design and optimization of high-frequency semiconductor devices. |
| format | Article |
| id | doaj-art-00218b0f556d41c0a156aedc2436e373 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-00218b0f556d41c0a156aedc2436e3732025-08-20T03:06:04ZengIEEEIEEE Access2169-35362025-01-0113842848430210.1109/ACCESS.2025.356741010988773Accelerated Prediction of Terahertz Performance Metrics in GaN IMPATT Sources via Artificial Neural NetworksSantu Mondal0https://orcid.org/0009-0000-6458-5560Sneha Ray1Aritra Acharyya2https://orcid.org/0000-0003-0231-4145Rudra Sankar Dhar3https://orcid.org/0000-0002-6571-3808Arindam Biswas4https://orcid.org/0000-0002-2576-8919Hiroaki Satoh5https://orcid.org/0000-0002-5868-0219Gurudas Mandal6Vitaliy Maksimenko7https://orcid.org/0009-0007-2296-140XVictor Krishtop8https://orcid.org/0000-0001-8871-8751Department of Information Technology, Asansol Engineering College, Asansol, West Bengal, IndiaDepartment of Electronics and Communication Engineering, Kalyani Government Engineering College, Nadia, Kalyani, West Bengal, IndiaDepartment of Electronics and Communication Engineering, Kalyani Government Engineering College, Nadia, Kalyani, West Bengal, IndiaDepartment of Electronics and Communication Engineering, National Institute of Technology at Mizoram, Aizawl, Mizoram, IndiaDepartment of Mining Engineering, School of Mines and Metallurgy, Kazi Nazrul University, Asansol, Burdwan, West Bengal, IndiaResearch Institute of Electronics, Shizuoka University, Hamamatsu, JapanCentre for IoT and AI Integration with Education-Industry-Agriculture, Kazi Nazrul University, Asansol, West Bengal, IndiaGeneral Physics Department, Perm National Research Polytechnic University, Perm, RussiaGeneral Physics Department, Perm National Research Polytechnic University, Perm, RussiaThis work investigates the application of artificial neural network (ANN)-based regression models to predict the static and dynamic characteristics of GaN impact avalanche transit time (IMPATT) sources in the terahertz (THz) frequency regime. A comprehensive dataset, derived from self-consistent quantum drift-diffusion (SCQDD) simulations of GaN IMPATT structures designed for a wide frequency range from the microwave frequency bands, up to 5 THz, is used to train the ANN models. The models effectively capture the impact of variations in structural, doping, and biasing parameters on device performance. The proposed ANN approach significantly reduces computational time for predicting breakdown characteristics, power output, and conversion efficiency properties of IMPATT sources, achieving similar accuracy to traditional SCQDD simulations while requiring only 7.8–20.1% of the computational time. Mean square errors are observed to be on the order of <inline-formula> <tex-math notation="LaTeX">$10^{-4}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$10^{-6}$ </tex-math></inline-formula>, demonstrating the models’ high accuracy. Experimental validation shows strong agreement in terms of breakdown voltage, power output, and efficiency, supporting the potential of machine learning to streamline the design and optimization of high-frequency semiconductor devices.https://ieeexplore.ieee.org/document/10988773/Artificial neural networks (ANN)avalanche transit time (ATT)IMPATT diodeslarge-signal simulationmachine learningself-consistent quantum drift-diffusion (SCQDD) model |
| spellingShingle | Santu Mondal Sneha Ray Aritra Acharyya Rudra Sankar Dhar Arindam Biswas Hiroaki Satoh Gurudas Mandal Vitaliy Maksimenko Victor Krishtop Accelerated Prediction of Terahertz Performance Metrics in GaN IMPATT Sources via Artificial Neural Networks IEEE Access Artificial neural networks (ANN) avalanche transit time (ATT) IMPATT diodes large-signal simulation machine learning self-consistent quantum drift-diffusion (SCQDD) model |
| title | Accelerated Prediction of Terahertz Performance Metrics in GaN IMPATT Sources via Artificial Neural Networks |
| title_full | Accelerated Prediction of Terahertz Performance Metrics in GaN IMPATT Sources via Artificial Neural Networks |
| title_fullStr | Accelerated Prediction of Terahertz Performance Metrics in GaN IMPATT Sources via Artificial Neural Networks |
| title_full_unstemmed | Accelerated Prediction of Terahertz Performance Metrics in GaN IMPATT Sources via Artificial Neural Networks |
| title_short | Accelerated Prediction of Terahertz Performance Metrics in GaN IMPATT Sources via Artificial Neural Networks |
| title_sort | accelerated prediction of terahertz performance metrics in gan impatt sources via artificial neural networks |
| topic | Artificial neural networks (ANN) avalanche transit time (ATT) IMPATT diodes large-signal simulation machine learning self-consistent quantum drift-diffusion (SCQDD) model |
| url | https://ieeexplore.ieee.org/document/10988773/ |
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