A Wideband GaN HEMT Modelling with Comprehensive Hybrid Parameter Extraction for 5G Power Amplifiers

• Published in: 2023 IEEE International Symposium on Circuits and Systems (ISCAS) pp. 1 - 5
• Main Authors: Lu, Zhongzhiguang, Xie, Hanlin, Piao, Jiaming, Zhengzhe, Wei, Ing, Ng Geok, Zheng, Yuanjin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 21.05.2023
• Subjects: artificial neural network (ANN); GaN HEMT; parameter extraction; polynomial fitting; power amplifier
• ISSN: 2158-1525
• DOI: 10.1109/ISCAS46773.2023.10182085

Due to better efficiency, gain and thermal performance compared to other semiconductor technologies, GaN power amplifiers are very attractive in the present 5G era. Meanwhile, accurate GaN HEMT device modelling is one of the critical steps to design PAs successfully. Therefore, research on high-frequency GaN HEMT device modelling method is of vital importance. This paper first presents a wideband GaN HEMTs model for 5G power amplifiers. A loadpull system available for 10-67 GHz measurement is set up to obtain wideband S parameter and RF performance results. The whole GaN device modelling could be divided into two parts: the small signal modelling and the large signal modelling. Direct optimization method with polynomial fitting is employed to obtain equivalent small signal circuit parameters, which improve the accuracy and efficiency of parameter extraction. Also, artificial neural network (ANN) technique is utilized to build charge and nonlinear current models, which takes the self-heating and trapping effects into consideration in the large signal modelling. The ANN technique could substitute the complex empirical equations as other papers has reported, and thus makes the extracted parameters less and the extraction process more accurate and efficient. At last, the proposed model is implemented and verified in ADS, the error between the measurement and simulation results is less than 5%.