This paper analyzes the thermal dependence of high-periphery GaN-on-SiC HEMT performance. The proposed approach is based on artificial neural networks (ANNs) that are used to model the scattering parameters versus temperature and frequency under a high dissipated power condition for a GaN HEMT with a gate width of 1.5 mm. The modeling results agree very well with measurements up to 65 GHz in the whole considered temperature range going from 35°C to 200°C, confirming the high accuracy and the good generalization capability of the proposed ANN approach.
Marinkovic, Z., Crupi, G., Vadala', V., Raffo, A., Caddemi, A., Markovic, V., et al. (2019). Temperature Dependent Small-Signal Neural Modeling of High-Periphery GaN HEMTs. In 2019 14th International Conference on Advanced Technologies, Systems and Services in Telecommunications, TELSIKS 2019 - Proceedings (pp.33-36). Institute of Electrical and Electronics Engineers Inc. [10.1109/TELSIKS46999.2019.9002335].
Temperature Dependent Small-Signal Neural Modeling of High-Periphery GaN HEMTs
Vadala', V.;
2019
Abstract
This paper analyzes the thermal dependence of high-periphery GaN-on-SiC HEMT performance. The proposed approach is based on artificial neural networks (ANNs) that are used to model the scattering parameters versus temperature and frequency under a high dissipated power condition for a GaN HEMT with a gate width of 1.5 mm. The modeling results agree very well with measurements up to 65 GHz in the whole considered temperature range going from 35°C to 200°C, confirming the high accuracy and the good generalization capability of the proposed ANN approach.
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