TY - GEN
T1 - Decision Tree Based Small-Signal Modelling of GaN HEMT and CAD Implementation
AU - Husain, Saddam
AU - Begaliyeva, Khamida
AU - Aitbayev, Alisher
AU - Chaudhary, Muhammad Akmal
AU - Hashmi, Mohammad
N1 - Funding Information:
The work was supported by the Collaborative Research Grant (CRP) # 021220CRP0222 at Nazarbayev University.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This paper explores the use of Decision Tree algorithm in the development of small signal model of GaN HEMT. In this stage, each measured s-parameters are modelled separately exploiting the bias, frequency and geometry dependence of the device as input predictors. This necessitates the tuning of parameters using Bayesian optimization and Random search algorithms. The outcome in terms of MSE and MAE demonstrates that the Random search algorithm gives a superior agreement with the measured values for the entire frequency range. Subsequently, the developed model is incorporated in the commercial CAD environment and a class-F power amplifier is designed to highlight the seamless integration ability and effectiveness of the developed model.
AB - This paper explores the use of Decision Tree algorithm in the development of small signal model of GaN HEMT. In this stage, each measured s-parameters are modelled separately exploiting the bias, frequency and geometry dependence of the device as input predictors. This necessitates the tuning of parameters using Bayesian optimization and Random search algorithms. The outcome in terms of MSE and MAE demonstrates that the Random search algorithm gives a superior agreement with the measured values for the entire frequency range. Subsequently, the developed model is incorporated in the commercial CAD environment and a class-F power amplifier is designed to highlight the seamless integration ability and effectiveness of the developed model.
KW - ADS Implementation
KW - Bayesian optimization
KW - Decision Tree
KW - GaN HEMT
KW - Random search algorithm
UR - http://www.scopus.com/inward/record.url?scp=85127085192&partnerID=8YFLogxK
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U2 - 10.1109/ICCE53296.2022.9730309
DO - 10.1109/ICCE53296.2022.9730309
M3 - Conference contribution
AN - SCOPUS:85127085192
T3 - Digest of Technical Papers - IEEE International Conference on Consumer Electronics
BT - 2022 IEEE International Conference on Consumer Electronics, ICCE 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Consumer Electronics, ICCE 2022
Y2 - 7 January 2022 through 9 January 2022
ER -