volume | PIER Journals

Abstract

An analytical modeling method for heterojunction bipolar transistor (HBT) is proposed in this paper. The new neuro-space mapping (Neuro-SM) model applied to DC, small signals and large signals simultaneously consists of two mapping networks, which provide the additional degrees of freedom.Sensitivity analysis expressions are derived to accelerate the training process. When the non-linearity of device is high, or the response of the model is complex, the weights in the proposed model are automatically adjusted to address the accuracy limitations. The proposed modeling method is verified by measured HBT examples in DC, smallsignals and largesignals Harmonic Balance (HB) simulation. The modeling experiments of the measured HBT demonstrate that the errors of the proposed Neuro-SM model are less than 2% by matching combined DC, small-signal S-parameters and large-signal HB data, which are less than the errors of the traditional Neuro-SM model and the coarse model. The proposed analytic Neuro-SM model fits the response of the fine model well.

1. Squartecchia, M., T. K. Johansen, J. Y. Dupuy, et al. "E-band indium phosphide double heterojunction bipolar transistor monolithic microwave-integrated circuit power amplifier based on stacked transistors," Microwave and Optical Technology Letters, Vol. 61, No. 2, 550-555, 2019.
doi:10.1002/mop.31558

2. Boulgheb, A., M. Lakhdara, and S. Latreche, "Improvement of the self-heating performance of an advanced SiGe HBT transistor through the Peltier effect," IEEE Transactions on Electron Devices, Vol. 68, No. 2, 479-484, 2021.
doi:10.1109/TED.2020.3044869

3. Tanaka, S., "A study on AM-AM/PM characteristics of a single-stage HBT power amplifier," IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Vol. E104-A, No. 2, 484-491, 2021.
doi:10.1587/transfun.2020GCP0010

4. Mohammadi, F. and A. Sadrossadat, "Modeling and simulation techniques for microwave components," Microwave Systems and Applications, 2017.

5. Zhang, H., G. Niu, M. B. Willemsen, and A. J. Scholten, "Improved compact modeling of SiGe HBT linearity with MEXTRAM," IEEE Transactions on Electron Devices, Vol. 68, No. 6, 2597-2603, 2021.
doi:10.1109/TED.2021.3070530

6. Karimi, G., R. Banitalebi, and S. B. Sedaghat, "Simulation of SiGe:C HBTs using neural network and adaptive neuro-fuzzy inference system for RF applications," International Journal of Electronics, Vol. 100, No. 7, 959-975, 2013.
doi:10.1080/00207217.2012.727353

7. Rudolph, M., "Compact HBT modeling: status and challenges," IEEE MTT-S International Microwave Symposium, 1206-1209, Anaheim, CA, USA, 2010.

8. Johansen, T. K., M. Rudolph, T. Jensen, et al. "Small- and large-signal modeling of InP HBTs in transferred-substrate technology," International Journal of Microwave and Wireless Technologies, Vol. 6, No. 3–4, 243-251, 2014.
doi:10.1017/S1759078714000051

9. Zhang, A. and J. Gao, "An improved small signal model of InP HBT for millimeter-wave applications," Microwave and Optical Technology Letters, Vol. 63, No. 8, 2160-2164, 2021.
doi:10.1002/mop.32876

10. Zhang, J., M. Liu, J. Wang, and K. Xu, "An analytic method for parameter extraction of InP HBTs small-signal model," Circuit World, Vol. 48, No. 4, 393-400, 2021.
doi:10.1108/CW-06-2020-0099

11. Cheng, L., H. Lu, M. Xia, et al. "An augmented small-signal model of InP HBT with its analytical based parameter extraction technique," Microelectronics Journal, Vol. 121, 105366, 2022.
doi:10.1016/j.mejo.2022.105366

12. Zhang, Q. J. and K. C. Gupta, Neural Networks for RF and Microwave Design, Artech House, Norwood, Mass, 2000.

13. Feng, F., W. Na, J. Jin, et al. "Artificial neural networks for microwave computer-aided design: The state of the art," IEEE Transactions on Microwave Theory and Techniques, Vol. 11, No. 70, 4597-4619, 2022.
doi:10.1109/TMTT.2022.3197751

14. Zlatica, D. M., G. Crupi, A. Caddemi, et al. "A review on the artificial neural network applications for small-signal modeling of microwave FETs," International Journal of Numerical Modelling Electronic Networks Devices and Fields, Vol. 33, No. 3, e2668, 2020.
doi:10.1002/jnm.2668

15. Feng, F., W. Na, J. Jin, et al. "ANNs for fast parameterized EM modeling: the state of the art in machine learning for design automation of passive microwave structures," IEEE Microwave Magazine, Vol. 22, No. 10, 37-50, 2021.
doi:10.1109/MMM.2021.3095990

16. Zhang, A. and J. Gao, "InP HBT small signal modeling based on artificial neural network for millimeter-wave application," 2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, 1-3, Hangzhou, China, 2020.

17. Zhu, L., J. Zhao, Z. Li, et al. "A general neuro-space mapping technique for microwave device modeling," EURASIP Journal on Wireless Communications and Networking, Vol. 2018, No. 1, 37, 2018.
doi:10.1186/s13638-018-1034-4

18. Zhang, W., F. Feng, V.-M.-R. Gongal-Reddy, et al. "Space Mapping approach to electromagnetic centric multiphysics parametric modeling of microwave components," IEEE Transactions on Microwave Theory and Techniques, Vol. 66, No. 7, 3169-3185, 2018.
doi:10.1109/TMTT.2018.2832120

19. Yan, S., Y. Zhang, W. Liu, et al. "A novel electromagnetic centric multiphysics parametric modeling approach using neuro-space mapping for microwave passive components," Photonics, Vol. 9, No. 12, 960, 2022.
doi:10.3390/photonics9120960

20. Zhao, Z., L. Zhang, F. Feng, et al. "Space mapping technique using decomposed mappings for GaN HEMT modeling," IEEE Transactions on Microwave Theory and Techniques, Vol. 68, No. 8, 3318-3341, 2020.
doi:10.1109/TMTT.2020.3004622

21. Zhang, L., J. J. Xu, M. C. E. Yagoub, et al. "Efficient analytical formulation and sensitivity analysis of neuro-space mapping for nonlinear microwave device modeling," IEEE Transactions on Microwave Theory and Techniques, Vol. 53, No. 9, 2752-2767, 2005.
doi:10.1109/TMTT.2005.854190

22. Yan, S., S. Zhang, Y. Zhang, et al. "An accurate neuro-space mapping method for heterojunction bipolar transistor modeling," 2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, 1-4, Hangzhou, China, 2020.

23. Yan, S., Q. Cheng, H. Wu, and Q. J. Zhang, "Neuro-space mapping for modeling heterojunction bipolar transistor," Transactions of Tianjin University, Vol. 21, No. 1, 90-94, 2015.
doi:10.1007/s12209-015-2493-x

24. Wu, H. F., Q. F. Cheng, S. X. Yan, et al. "Transistor model building for a microwave power heterojunction bipolar transistor," IEEE Microwave Magazine, Vol. 16, No. 2, 85-92, 2015.
doi:10.1109/MMM.2014.2377588

25. Zhang, Q. J., "Neuro modeler plus,", Dept. Electron., Carleton Univ., Ottawa, ON., Canada, 2008.

Dr. Shuxia Yan

Dr. Yuxing Li

Dr. Chenglin Li

Dr. Fengqi Qian

Dr. Xu Wang

Dr. Wenyuan Liu