Optimized Machine Learning-Augmented Hybrid Empirical Models for AlGaN/GaN HEMTs: A Comprehensive Analysis
Precise modeling of gallium nitride (GaN) high-electron mobility transistors (HEMTs) is vital for ensuring reliable and scalable RF circuit design, and efficient characterization of the device behavior. This article presents robust hybrid equivalent circuit (EC)–machine learning (ML) fram...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11105079/ |
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