Abstract
Accurate modeling of self-heating effect of high-power transistor is critical for reliable design of microwave circuit and system. In this paper, a novel neuro-space mapping (Neuro-SM) method incorporating self-heating effect is presented. By modifying the voltage and temperature relationships in the existing electro-thermal nonlinear model, the proposed Neuro-SM produces a new model exceeding the accuracy limit of the model. To accurately describe the self-heating effect, separate mappings for temperature and voltage at gate and drain are used as the mapping structure in the proposed method. The mappings combined with thermal sub-circuit including thermal resistance-capacitance parallel with thermal current are used to describe the self-heating effect. The validity and efficiency of the proposed Neuro-SM method incorporating self-heating effect are demonstrated through a modeling example of a high-power transistor used in cellular infrastructure market.
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References
Mishra, U., Shen, L., Kazior, T., et al.: GaN-based RF power devices and amplifiers. Proc. IEEE 96(2), 287–305 (2008)
Shiojima, K., Makimura, T., Kosugi, T., et al.: High-power AlGaN/GaN dual-gate high electron mobility transistor mixers on SiC substrates. Electron. Lett. 40(12), 775–776 (2004)
Wang, C., Xu, Y., Yu, X., et al.: An electrothermal model for empirical large-signal modeling of AlGaN/GaN HEMTs including self-heating and ambient temperature effects. IEEE Trans. Microw. Theory Tech. 62(12), 2878–2887 (2014)
Liu, L., Ma, J., Ng, G.: Electrothermal large-signal model of III-V FETs including frequency dispersion and charge conservation. IEEE Trans. Microw. Theory Tech. 57(12), 3106–3117 (2009)
Jarndal, A., Ghannouchi, F.: Improved modeling of GaN HEMTs for predicting thermal and trapping-induced-kink effects. Solid-State Electron. 123, 19–25 (2016)
Zhang, L., Xu, J., Yagoub, M., et al.: Neuro-space mapping technique for nonlinear device modeling and large-signal simulation. In: IEEE MIT-S International Microwave Symposium, Philadelphia, PA, pp. 173–176, June 2003
Zhang, Q., Gupta, K., Devabhaktuni, V.: Artificial neural networks for RF and microwave design: from theory to practice. IEEE Trans. Microw. Theory Tech. 51(4), 1339–1350 (2003)
Bandler, J., Cheng, Q., Dakroury, S., et al.: Space mapping: the state of the art. IEEE Trans. Microw. Theory Tech. 52(1), 337–361 (2004)
Zhang, L., Xu, J., Yagoub, M., et al.: Efficient analytical formulation and sensitivity analysis of neuro-space mapping for nonlinear microwave device modeling. IEEE Trans. Microw. Theory Tech. 53(9), 2752–2767 (2005)
Zhu, L., Liu, K., Zhang, Q., et al.: An enhanced analytical neuro-space mapping method for large-signal microwave device modeling. In: IEEE MIT-S International Microwave Symposium Digest, Montreal, QC, pp. 1–3, June 2012
Zhu, L., Zhang, Q., Liu, K., et al.: A novel dynamic neuro-space mapping approach for nonlinear microwave device modeling. IEEE Microw. Wireless Compon. Lett. 26(2), 131–133 (2016)
Xu, J., Yagoub, M., Ding, R., et al.: Exact adjoint sensitivity for n based microwave modeling and design. IEEE Trans. Microw. Theory Tech. 51(1), 226–237 (2003)
Angelov, I., Zirath, H., Rorsman, N.: A new empirical nonlinear model for HEMT and MESFET devices. IEEE Trans. Microw. Theory Tech. 40(12), 2258–2266 (1992)
Acknowledgments
This work is supported by the Key project of Tianjin Natural Science Foundation (No. 16JCZDJC38600).
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Zhu, L., Zhao, J., Liu, W., Pan, L., Liu, D. (2019). A Novel Neuro-Space Mapping Technique Incorporating Self-heating Effect for High-Power Transistor Modeling. In: Liang, Q., Mu, J., Jia, M., Wang, W., Feng, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2017. Lecture Notes in Electrical Engineering, vol 463. Springer, Singapore. https://doi.org/10.1007/978-981-10-6571-2_212
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DOI: https://doi.org/10.1007/978-981-10-6571-2_212
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