Journal of Computational Electronics

, Volume 16, Issue 3, pp 825–832 | Cite as

Artificial neural network design for compact modeling of generic transistors

Article
  • 201 Downloads

Abstract

A methodology to develop artificial neural network (ANN) models to quickly incorporate the characteristics of emerging devices for circuit simulation is described in this work. To improve the model accuracy, a current and voltage data preprocessing scheme is proposed to derive a minimum dataset to train the ANN model with sufficient accuracy. To select a proper network size, four guidelines are developed from the principles of two-layer network. With that, a reference ANN size is proposed as a generic three-terminal transistor model. The ANN model formulated using the proposed approach has been verified by physical device data. Both the device and circuit-level tests show that the ANN model can reproduce and predict various device and circuits with high accuracy.

Keywords

Compact model Emerging device Device modeling Artificial neural network (ANN) 

References

  1. 1.
    Tsividis, Y.: Operation and Modeling of the MOS Transistor, 2nd edn. McGraw-Hill, New York (1999)Google Scholar
  2. 2.
    Khakifirooz, A., Nayfeh, O.M., Antoniadis, D.A.: A simple semiempirical short-channel MOSFET current–voltage model continuous across all regions of operation and employing only physical parameters. IEEE Trans. Electron Devices 56(8), 1674–1680 (2009)CrossRefGoogle Scholar
  3. 3.
    Root, D.E., Xu, J., Horn, J., Iwamoto, M.: The large-signal model: theoretical foundations, practical considerations, and recent trends. In: Nonlinear Transistor Model Parameter Extraction Technique, ch. 5. pp. 123–170. Cambridge University Press, Cambridge (2011)Google Scholar
  4. 4.
    Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)MATHGoogle Scholar
  5. 5.
    Root, D.E.: Future device modeling trends. IEEE Microw. Mag. 13, 45–59 (2012)CrossRefGoogle Scholar
  6. 6.
    Zhang, Q.J., Gupta, K.C.: Neural Networks for RF and Microwave Design. Artech House, Norwood (2000)Google Scholar
  7. 7.
    Xu, J., Yagoub, M.C.E., Ding, R., Zhang, Q.J.: Exact adjoint sensitivity analysis for neural based microwave modeling and design. IEEE Trans. Microw. Theory Tech. 51(1), 226–237 (2003)CrossRefGoogle Scholar
  8. 8.
    Hagan, M.T., Demuth, H.B., Beale, M.H., Jesus, O.D.: Neural Network Design, 2nd edn (2014)Google Scholar
  9. 9.
    Zhang, L., Chan, M.: SPICE modeling of double-gate tunnel-FETs including channel transports. IEEE Trans. Electron Devices 61(2), 300–307 (2014)CrossRefGoogle Scholar
  10. 10.
    Cheng, Y., Jeng, M.-C., Liu, Z., Huang, J., Chan, M., Chen, K., Ko, P.K., Hu, C.: A physical and scalable \(I\)-\(V\) model in BSIM3v3 for analog/ digital circuit simulation. IEEE Trans. Electron Devices 44(2), 277–287 (1997)CrossRefGoogle Scholar
  11. 11.
    Barron, A.: Neural networks: a review from statistical perspective. Statist. Sci. 9(1), 33–35 (1994)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Cybenko, G.: Approximation by superpositions of a sigmoidal function. Math. Control Signals Syst. 2, 303–314 (1989)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    McAndrew, C.C.: Practical modeling for circuit simulation. IEEE J. Solid State Circuits 33(3), 439–448 (1998)CrossRefGoogle Scholar
  14. 14.
    Khandelwal, S., Duarte, J.P., Venugopalan, S., Paydavosi, N., Lu, D.D., Lin, C.-H., Dunga, M., Yao, S., Morshed, T., Niknejad, A., Hu, C.: BSIM-CMG108.0.0 Technical Manual. (Online). http://wwwdevice.eecs.berkeley.edu/bsim/?page=BSIMCMG_LR (2015). Accessed 01 Sept

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of ECEHong Kong University of Science and TechnologyKowloonHong Kong

Personalised recommendations