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Efficient Sensitivity Analysis of Dynamic Neuro-space Mapping for Transistor Modeling

  • Lin ZhuEmail author
  • Jian Zhao
  • Wenyuan Liu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 517)

Abstract

In this paper, an enhanced dynamic Neuro-space mapping (Neuro-SM) method is proposed with emphasis on transistor modeling. By modifying the dynamic voltage relationships in an existing nonlinear model, the proposed Neuro-SM produces a new and more accurate model than the nonlinear model as well as the static Neuro-SM. Compared to the existing dynamic Neuro-SM, a new sensitivity analysis technique is derived to speed up the training of the proposed model with dc, small- and large-signal data. The validity and efficiency of the proposed Neuro-SM method are demonstrated by modeling examples of a GaAs high-electron-mobility transistor (HEMT). Suitable value of time delay parameter which is equal to one divided by 3 or 5 times of the largest frequency considered in simulation is suggested and demonstrated by the modeling example.

Keywords

Neural networks Neuro-SM Transistor modeling Optimization Simulation 

Notes

Acknowledgements

This work is supported by Scientific Research Plan Project by Tianjin Education Commission (No. 2016CJ13).

References

  1. 1.
    Zhang, L., Xu, J., Yagoub, M., et al.: Neuro-space mapping technique for nonlinear device modeling and large-signal simulation. IEEE MIT-S Int. Microw. Symp. Philadelphia, PA, Jun. 2003, pp. 173–176Google Scholar
  2. 2.
    Zhu, L., Liu, K., Zhang, Q., et al.: An enhanced analytical neuro-space mapping method for large-signal microwave device modeling. IEEE MIT-S Int. Microw. Symp. Dig. Montreal, QC, Jun. 2012, pp. 1–3Google Scholar
  3. 3.
    Zhu, L., Zhang, Q., Liu, K., et al.: A novel dynamic neuro-space mapping approach for nonlinear microwave device modeling. IEEE Microw. Wirel. Compon. Lett. 26(2), 131–133 (2016)CrossRefGoogle Scholar
  4. 4.
    Long, Y., Guo, Y., Zhong, Z.: A 3-D table-based method for non-quasi-static microwave FET devices modeling. IEEE Trans. Microw. Theory Tech. 60(10), 3088–3095 (2012)CrossRefGoogle Scholar
  5. 5.
    Song, Q., Spall, J., Soh, Y., et al.: Robust neural network tracking controller using simultaneous perturbation stochastic approximation. IEEE Trans. Neural Netw. 19(5), 817–835 (2008)CrossRefGoogle Scholar
  6. 6.
    Zhang, L., Xu, J., Yagoub, M.C., 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)Google Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Medici 2013 I-2013.12-0. Synopsys Inc., Mountain View, CA, 2013Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Tianjin Chengjian UniversityTianjinChina
  2. 2.Shaanxi University of Science and TechnologyXi’anChina

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