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Large-Signal Behavior Modeling of GaN P-HEMT Based on GA-ELM Neural Network

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Abstract

The Genetic Algorithm-Extreme Learning Machine (GA-ELM) neural network algorithm is proposed to model the relevant characteristics of GaN pseudomorphic high electron mobility transistor (P-HEMT) large signal. This algorithm solves the over-fitting problem of the Back Propagation (BP) neural network algorithm in the prediction data. It has the characteristics of fast calculation speed, so it can greatly save calculation processing time. It can also randomly generate the connection weights of the input layer, the hidden layer and the threshold of the hidden layer neurons, avoiding errors in parameter selection. In order to verify the superiority of the algorithm, the modeling effects of the BP neural network algorithm model, the Genetic Algorithm-Back Propagation (GA-BP) neural network algorithm model and the GA-ELM neural network algorithm model are compared in this paper. The results show that the proposed GA-ELM neural network algorithm model has the highest accuracy.

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Data Availability Statement

Data used to support the findings of this study are available from the corresponding author upon request.

References

  1. J. Cai, J.B. King, J. Su, C. Yu, S. Chen, L. Sun, H. Wang, J. Liu, Bayesian inference-based behavioral modeling technique for GaN HEMTs. IEEE Trans. Microw. Theory Tech. 67, 6 (2019)

    Google Scholar 

  2. K.J. Chen, O. Hberlen, A. Lidow, C.L. Tsai, T. Ueda, Y. Uemoto, Y. Wu, GaN-on-Si power technology: devices and applications. IEEE Trans. Electron Devices 64, 3 (2017)

    Article  Google Scholar 

  3. S. Colangeli, A. Bentini, W. Ciccognani, E. Limiti, A. Nanni, GaN-Based robust low-noise amplifiers. IEEE Trans. Electron Devices 60, 10 (2013)

    Article  Google Scholar 

  4. G. Crupi, A. Raffo, V. Vadala et al., High-periphery GaN HEMT modeling up to 65 GHz and 200 °C. Solid-State Electron. 152, 11–16 (2018)

    Article  Google Scholar 

  5. Q. Dai, N. Liu, Alleviating the problem of local minima in backpropagation through competitive learning. Neurocomputing 94, 152–158 (2012)

    Article  Google Scholar 

  6. X. Du, M. Helaoui, A. Jarndal, T. Liu, F.M. Ghannouchi, ANN-Based large-signal model of AlGaN/GaN HEMTs with accurate buffer-related trapping effects characterization. IEEE Trans. Microw. Theory Tech. 68, 7 (2020)

    Article  Google Scholar 

  7. B. Feng, R. Liu, Efficient-memory and low-latency BP decoding algorithm for polar codes. IEEE Commun. Lett. 24, 6 (2020)

    Google Scholar 

  8. S. Ghosh, S. A. Ahsan, A. Dasgupta, S. Khandelwal, Y. S. Chauhan, GaN HEMT modeling for power and RF applications using ASM-HEMT. In: 2016 3rd International Conference on Emerging Electronics (ICEE), Mumbai, (2016)

  9. R. Grienggrai, S. Ramalingam, Robust passivity and stability analysis of uncertain complex-valued impulsive neural networks with time-varying delays. Neural Process. Lett. 53(1), 581–606 (2021)

    Article  Google Scholar 

  10. R. Grienggrai, S. Ramalingam, S. Rajendran, Dissipativity analysis of delayed stochastic generalized neural networks with Markovian jump parameters. Int. J. Nonlinear Sci. Numer. Simul. (2021). https://doi.org/10.1515/ijnsns-2019-0244

    Article  Google Scholar 

  11. U. Humphries, G. Rajchakit, R. Sriraman, P. Kaewmesri, P. Chanthorn, C.P. Lim, R. Samidurai, An extended analysis on robust dissipativity of uncertain stochastic generalized neural networks with Markovian jumping parameters. Symmetry 12(6), 1–21 (2020)

    Article  Google Scholar 

  12. A. Jarndal, On neural networks based electrothermal modeling of GaN devices. IEEE Access 7, 94205–94214 (2019)

    Article  Google Scholar 

  13. A. Jarndal, S. Husain, M. Hashmi, F.M. Ghannouchi, Large-signal modeling of GaN HEMTs using hybrid GA-ANN, PSO-SVR, and GPR-based approaches. IEEE J. Electron Devices Soc. 9, 195–208 (2021)

    Article  Google Scholar 

  14. A. Jarndal, G. Kompa, Large-signal model for AlGaN/GaN HEMTs accurately predicts trapping- and self-heating-induced dispersion and intermodulation distortion. IEEE Trans. Electron Devices 54, 11 (2007)

    Article  Google Scholar 

  15. J. King, C. Wilson, Charge conservative FET modelling using ANNs. In: 2017 12th European Microwave Integrated Circuits Conference (EuMIC), Nuremberg, 2017, pp. 208–211

  16. S.Y. Lee, B.A. Cetiner, H. Torpi, S.J. Cai, J. Li, K. Alt, Y.L. Chen, C.P. Wen, K.L. Wang, T. Itoh, An X-band GaN HEMT power amplifier design using an artificial neural network modeling technique. IEEE Trans. Electron Devices 48, 3 (2001)

    Article  Google Scholar 

  17. X.B. Liu, F. Zhao, X.F. Ai, Q.H. Wu, Pulse radar randomly interrupted transmitting and receiving optimization based on genetic algorithm in radio frequency simulation. Eurasip J. Adv. Signal Process. 9, 1 (2021)

    Article  Google Scholar 

  18. Y. Li, Y. Wang, Y. Li, R. Zhou, Z. Lin, An artificial neural network assisted optimization system for analog design space exploration. IEEE Trans. Comput.-Aided Des. Integr. Sys. 39(10), 2640–2653 (2020). https://doi.org/10.1109/TCAD.2019.2961322

    Article  Google Scholar 

  19. M.B. Mompeán, J.M. Martínez-Villena, A.R. Muñoz, J.V. Francés-Víllora, J.F. Guerrero-Martínez, M. Wegrzyn, M. Adamski, Support tool for the combined software/hardware design of on-chip ELM training for SLFF neural networks. IEEE Trans. Industr. Inf. 12, 3 (2016)

    Article  Google Scholar 

  20. M.B. Mompean, J.M. Martinez-Villena, A. Rosado-Munoz, J.V. Frances-Villora, J.F. Guerrero-Martinez, M. Wegrzyn, M. Adamski, Support tool for the combined software/hardware design of on-chip ELM training for SLFF neural networks. IEEE Trans. Ind. Inf. 12, 3 (2016)

    Article  Google Scholar 

  21. K. Ning, M. Liu, M. Dong, C. Wu, Z. Wu, Two efficient twin ELM methods with prediction interval. IEEE Trans. Neural Netw. Learn. Syst. 26, 9 (2015)

    Article  MathSciNet  Google Scholar 

  22. G. Rajchakit, P. Chanthorn, M. Niezabitowski, R. Raja, D. Baleanu, A. Pratapg, Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks. Neurocomputing 417, 290–301 (2020)

    Article  Google Scholar 

  23. H. Ren, B. Hou, G. Zhou, L. Shen, C. Wei, Q. Li, Variable pitch active disturbance rejection control of wind turbines based on BP neural network PID. IEEE Access 8, 71782–71797 (2020)

    Article  Google Scholar 

  24. S. Saravanan, M.S. Ali, G. Rajchakit, B. Hammachukiattikul, B. Priya, G.K. Thakur, Finite-time stability analysis of switched genetic regulatory networks with time-varying delays via Wirtinger’s integral inequality. Complexity 2021, 9540548 (2021)

    Article  Google Scholar 

  25. J. Shawash, D.R. Selviah, Real-time nonlinear parameter estimation using the Levenburg-Marquardt algorithm on field programmable gate arrays. IEEE Trans. Ind. Electron. 60, 1 (2013)

    Article  Google Scholar 

  26. R. Sriraman, G. Rajchakit, C.P. Lim, P. Chanthorn, R. Samidurai, Discrete-time stochastic quaternion-valued neural networks with time delays: an asymptotic stability analysis. Symmetry 12(6), 936 (2020)

    Article  Google Scholar 

  27. I. Tariq, M.A. Sindhu, R.A. Abbasi, A.S. Khattak, O. Maqbool, G.F. Siddiqui, Resolving cross-site scripting attacks through genetic algorithm and reinforcement learning. Exp. Syst. Appl. 168, 4 (2021)

    Article  Google Scholar 

  28. W.X. Wang, B.X. Ma, X.Z. Luo, X.X. Li, S.Y. Lei, Y.J. Li, J.T. Sun, Study on the moisture content of dried Hami big jujubes by near-infrared spectroscopy combined with variable preferred and GA-ELM model. Spectrosc Spectr Anal 40, 543–549 (2020)

    Google Scholar 

  29. S. Wang, H. Zhu, M. Wu, W. Zhang, Active disturbance rejection decoupling control for three-degree-of- freedom six-pole active magnetic bearing based on BP neural network. IEEE Trans. Appl. Supercond. 30, 4 (2020)

    Article  Google Scholar 

  30. Y.R. Wu, M. Singh, J. Singh, Device scaling physics and channel velocities in AIGaN/GaN HFETs: velocities and effective gate length. IEEE Trans. Electron Devices 53, 4 (2006)

    Article  Google Scholar 

  31. Z. Xiang, S. Feng, Y. Zhang et al., Effect of two-dimensional electron gas on horizontal heat transfer in AlGaN/AlN/GaN heterojunction transistors [J]. Solid-State Electron. 147, 35–38 (2018)

    Article  Google Scholar 

  32. K. Yan, Z. Ji, H. Lu, J. Huang, W. Shen, Y. Xue, Fast and accurate classification of time series data using extended ELM: application in fault diagnosis of air handling units. IEEE Trans. Syst., Man, Cybern.: Syst. 49, 7 (2019)

    Google Scholar 

  33. J. Yang, Y. Jia, N. Ye et al., A novel empirical I-V model for GaN HEMTs [J]. Solid-State Electron. 146, 1–8 (2018)

    Article  Google Scholar 

  34. Y. Zhang, S. Feng, H. Zhu, C. Guo, B. Deng, G. Zhang, Effect of self-heating on the drain current transient response in AlGaN/GaN HEMTs. IEEE Electron Device Lett. 35, 3 (2014)

    Article  Google Scholar 

  35. Z. Zhao, L. Zhang, F. Feng, W. Zhang, Q.J. Zhang, Space mapping technique using decomposed mappings for GaN HEMT modeling. IEEE Trans. Microw. Theory Tech. 68, 8 (2020)

    Google Scholar 

Download references

Acknowledgements

This project is supported by the National Natural Science Foundation of China (Grant No: 61804046, 61704049), the Foundation of Department of Science and Technology of Henan Province (Grant No. 202102210322, 212102210286), and the Foundation of He’nan Educational Committee (Grant No. 21A510002).

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Authors and Affiliations

  1. Electrical Engineering College, Henan University of Science and Technology, Luoyang, 471023, People’s Republic of China

    Shaowei Wang, Jincan Zhang, Min Liu, Bo Liu & Jinchan Wang

  2. Novaco Microelectronics Technologies Ltd, Nantong, 226000, People’s Republic of China

    Shi Yang

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  1. Shaowei Wang
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  2. Jincan Zhang
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  3. Min Liu
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Correspondence to Jincan Zhang.

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Wang, S., Zhang, J., Liu, M. et al. Large-Signal Behavior Modeling of GaN P-HEMT Based on GA-ELM Neural Network. Circuits Syst Signal Process 41, 1834–1847 (2022). https://doi.org/10.1007/s00034-021-01891-7

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