Abstract
Modeling electronic devices has become a numerous work needed to keep up the development of the applications of the communication systems. Since the physical modeling of electronic devices is complex and time consuming. Recent research is using machine learning in modeling electronic devices, especially Artificial Neural Network (ANN) to reduce complexity and time consumption. In this paper, an enhanced model using ANN is proposed to model the scattering (S-) parameters in frequency range from 0.5 to 18 GHZ of GaAs Pseudomorphic High-Electron-Mobility-Transistor (pHEMTs). The proposed model uses four parameters which are the drain-source voltage, the drain–source current, the channel width, and the operating frequency as input parameters for the proposed ANN model to produce s-parameters for the pHEMT. Excellent agreements between the manufacturer’s datasheet (ATF-34143) and model-calculated data are achieved. The ANN outputs are represented in the form of amplitude-phase and achieved higher performance than existing models. In modeling S11 the best training performance reached is 5.642 *10–16 at epoch 392, in modeling at S21 the best performance reached is 2.869*10–21 at epoch 60, in modeling S12 the best performance reached is 4.3601*10–20 at epoch 20 and in modeling S22 the best performance reached is 5.7795*10–23 at epoch 144.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Camarchia, V., Cappelluti, F., Ghione, G., Limiti, E., Moran, D.A.J., Pirola, M.: An overview on recent developments in RF and microwave power H-terminated diamond MESFET technology. In: 2014 International Workshop on Integrated Nonlinear Microwave and Millimetre-wave Circuits (INMMiC), pp. 1–6. IEEE (2014)
Wang, S., Zhang, J., Liu, M., Liu, B., Wang, J., Yang, S.: Large-signal behavior modeling of GaN P-HEMT based on GA-ELM neural network. Circ. Syst. Signal Process. 41(4), 1834–1847 (2021). https://doi.org/10.1007/s00034-021-01891-7
Hari, N., Ahsan, M., Sridhar, R., Padmanaban, S., Albarbar, A., Blaabjerg, F.: Gallium nitride power electronic devices modelling using machine learning. IEEE Access 8, 119654–119667 (2020)
Perkins, N., Abrokwah, J., Rohdin, H.G., Marsh, P., Stanback, J.: Avago technologies general IP Singapore Pte Ltd. Pseudomorphic high electron mobility transistor (pHEMT) comprising low temperature buffer layer. U.S. Patent 8,901,606 (2014)
Zhang, A., Gao, J.: InP HBT small signal modeling based on artificial neural network for millimeter-wave application. In: Proceedings of the 2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO), Hangzhou, China, 7–9 December 2020, pp. 1–3 (2020)
Deng, W., Zhang, W., Peng, Y., Wu, W., Huang, J., Luo, Z.: Compact modeling of metal-oxide TFTs based on artificial neural network and improved particle swarm optimization. J. Comput. Electron. 20(2), 1043–1049 (2021). https://doi.org/10.1007/s10825-020-01641-z
Marinkovic, Z., Crupi, G., Caddemi, A., Markovic, V., Schreurs, D.M.M.-P.: A review on the artificial neural network applications for small-signal modeling of microwave FETs. Int. J. Numer. Model. 33(3), e2668 (2020)
Qi, J., et al.: Small-signal modeling of microwave transistors using radial basis function artificial neural network-comparison of different methods for spread constant determined. Wiley (2022)
Khusro, A., Husain, S., Hashmi, M.S., Auyuneur, M., Ansari, A.Q.: A reliable and fast ANN based behavioral modeling approach for GaN HEMT. In: 2019 16th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD), pp. 277–280. IEEE (2019)
Zhu, Y.Y., Ma, J.G., Fu, H.P., Zhang, Q.J., Cheng, Q.F., Lin, Q.: Accurate modeling of pHEMT output current derivatives over a wide temperature range. Int. J. Numer. Model Electron. Netw. Dev. Field. 30(3–4), e2185 (2017)
Husain, S., Begaliyeva, K., Aitbayev, A., Chaudhary, M.A., Hashmi, M.: Decision tree based small-signal modelling of GaN HEMT and CAD ımplementation. In: 2022 IEEE International Conference on Consumer Electronics (ICCE), pp. 1–6. IEEE (2022)
https://www.alldatasheet.com/view.jsp?Searchword=Atf-34143%20datasheet&gclid=EAIaIQobChMI9PLYps-0-gIVx-Z3Ch1aNQndEAAYASAAEgJEYvD_BwE. Accessed Nov 2022
Hari, N., Chatterjee, S., Iyer, A.: Gallium nitride power device modeling using deep feed forward neural networks. In: 2018 1st Workshop on Wide Bandgap Power Devices and Applications in Asia (WiPDA Asia), pp. 164–168. IEEE (2018)
Wei, J.H., Mao, W., Fang, H., Zhang, Z., Zhang, J.X., Lan, B.J., Wan, J.: Advanced MOSFET model based on artificial neural network. In: 2020 China Semiconductor Technology International Conference (CSTIC), pp. 1–3. IEEE (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mohamed, R., Magdy, A., Nafea, S.F. (2023). Modeling of Psuedomorphic High Electron Mobility Transistor Using Artificial Neural Network. In: Hassanien, A.E., et al. The 3rd International Conference on Artificial Intelligence and Computer Vision (AICV2023), March 5–7, 2023. AICV 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 164. Springer, Cham. https://doi.org/10.1007/978-3-031-27762-7_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-27762-7_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-27761-0
Online ISBN: 978-3-031-27762-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)