volume | PIER Journals

Abstract

An ANN-based small-signal equivalent circuit model for 130 nm MOSFET device is proposed in this paper. The proposed model combines the conventional small-signal equivalent circuit model and artificial neural networks (ANNs) to achieve higher accuracy. Good agreement is obtained between proposed model and measured results confirming the validity and effectiveness of proposed model.

1. Morifuji, E., H. S. Momose, T. Ohguro, T. Yoshitomi, H. Kimijima, F. Matsuoka, M. Kinugawa, Y. Katsumata, and H. Iwai, "Future perspective and scaling down roadmap for RF CMOS," Symposium on VLSL Technology Digest of Technical Papers, 165-166, 1999. Google Scholar

2. Cheng, Y. H., M. J. Deen, and C. H. Chen, "MOSFET modeling for RF IC design," IEEE Trans. Electron Devices, Vol. 52, 1286-1303, 2005.
doi:10.1109/TED.2005.850656 Google Scholar

3. Chan, Y.-J., C.-H. Huang, C.-C. Weng, and B.-K. Liew, "Characteristics of deep-submicrometer MOSFET and its empirical nonlinear RF model," IEEE Trans. Microwave Theory Tech., Vol. 46, 611-615, May 1998.
doi:10.1109/22.668671 Google Scholar

4. Yoon, J., H. Seo, I. Choi, and B. Kim, "Wideband LNA using a negative GM cell for improvement of linearity and noise figure," Journal of Electromagnetic Waves and Applications, Vol. 24, No. 5-6, 619-630, 2010.
doi:10.1163/156939310791036412 Google Scholar

5. Lovelace, D., J. Costa, and N. Camilleri, "Extracting small-signal model parameters of silicon MOSFET transistors," IEEE MTT-S Dig., 865-868, San Diego, CA, 1994. Google Scholar

6. Biber, C. E., M. L. Schmatz, and T. Morf, "Improvements on a MOSFET model for nonlinear RF simulation," IEEE MTTS Dig., Vol. 865, No. 868, Denver, CO, 1997. Google Scholar

7. Lee, S., Y. H. Kyu, C. S. Kim, J. G. Koo, and K. S. Nam, "A novel approach to extracting small-signal model parameters of silicon MOSFET's," IEEE Microw. Guid. Wave Lett., Vol. 7, 75-77, 1997.
doi:10.1109/75.556037 Google Scholar

8. Zhang, Q. J., K. C. Gupta, and V. K. Devabhaktuni, "Artificial neural networks for RF and microwave design: From theory to practice," IEEE Trans. Microwave Theory Tech., Vol. 51, 1339-1350, 2003.
doi:10.1109/TMTT.2003.809179 Google Scholar

9. Li, X., J. Gao, and G. Boeck, "Printed dipole antenna design by artificial neural network modeling for RFID application," International Journal of RF and Microwave Computer-aided Engineering, Vol. 16, No. 6, 607-611, 2006.
doi:10.1002/mmce.20183 Google Scholar

10. Li, X., J. Gao, J.-G. Yook, and X. Chen, "Bandpass filter design by artificial neural network modeling," Asia-Pacific Microwave Conference, Vol. 2, 713-716, 2005. Google Scholar

11. Li, X. and J. Gao, "Pad modeling by using artificial neural network," Progress In Electromagnetics Research, Vol. 74, 167-180, 2007.
doi:10.2528/PIER07041201 Google Scholar

12. Li, X., Y. Li, and J. Zhao, "Ann-based pad modeling technique for MOS-FET devices," Progress In Electromagnetics Research, Vol. 118, 303-319, 2011.
doi:10.2528/PIER11042702 Google Scholar

13. Li, X., J. Gao, and G. Boeck, "Microwave nonlinear device modeling using artificial neural network," Semicond. Sci. Technol., Vol. 21, 833-840, 2006.
doi:10.1088/0268-1242/21/7/001 Google Scholar

14. Mohamed, M. D. A., E. A. Soliman, and M. A. ElGamal, "Optimization and characterization of electromagnetically coupled patch antennas using RBF neural networks," Journal of Electromagnetic Waves and Applications, Vol. 20, No. 8, 1101-1114, 2006.
doi:10.1163/156939306776930240 Google Scholar

15. Jin, L., C. L. Ruan, and L. Y. Chun, "Design E-plane bandpass filter based on EM-ANN model," Journal of Electromagnetic Waves and Applications, Vol. 20, No. 8, 1061-1069, 2006.
doi:10.1163/156939306776930259 Google Scholar

16. Acikgoz, H., Y. L. Bihan, O. Meyer, and L. Pichon, "Microwave characterization of dielectric materials using bayesian neural networks," Progress In Electromagnetics Research C, Vol. 3, 169-182, 2008.
doi:10.2528/PIERC08030603 Google Scholar

17. Vakula, D. and N. V. S. N. Sarma, "Fault diagnosis of planar antenna arrays using neural networks," Progress In Electromagnetics Research M, Vol. 6, 35-46, 2009.
doi:10.2528/PIERM09011204 Google Scholar

18. Michalski, J. J., "Artificial neural networks approach in microwave filter tuning," Progress In Electromagnetics Research M, Vol. 13, 173-188, 2010.
doi:10.2528/PIERM10053105 Google Scholar

19. Zhang, L., J. Xu, M. C. E. Yagoub, R. T. Ding, and Q. J. Zhang, "Efficient analytical formulation and sensitivity analysis of neurospace mapping for nonlinear microwave device modeling," IEEE Trans. Microwave Theory Tech., Vol. 53, 2752-2767, 2005.
doi:10.1109/TMTT.2005.854190 Google Scholar

20. Kim, C.-H., C. S. Kim, H. K. Yu, and K. S. Nam, "Unique extraction of substrate parameters of common-source MOSFET's," IEEE Microwave Guided Wave Lett., Vol. 9, 108-110, Mar. 1999. Google Scholar

21. Chang, K. M. and H. P. Wang, "A new small-signal MOSFET model and parameter extraction method for RF IC's application," Microelectron J., Vol. 35, 749-759, 2004.
doi:10.1016/j.mejo.2004.06.001 Google Scholar

22. Gao, J. and A. Werthof, "Direct parameter extraction method for deep submicrometer metal oxide semiconductor field effect transistor small signal equivalent circuit," IET Microwaves Antennas Propag., Vol. 3, 564-571, 2009.
doi:10.1049/iet-map.2008.0162 Google Scholar

23. Angelov, I., H. Zirath, and N. Rorsman, "A new empirical nonlinear model for HEMT and MESFET devices," IEEE Trans. Microwave Theory Tech., Vol. 40, 2258-2266, 1992.
doi:10.1109/22.179888 Google Scholar

24. Sirakawa, K., M. Shimiz, N. Okubo, and Y. Daido, "A large signal characterization of an HEMT using a multilayered neural network," IEEE Trans. Miccrowave Theory Tech., Vol. 45, 1630-1633, 1997.
doi:10.1109/22.622932 Google Scholar

25. Sirakawa, K., M. Shimiz, N. Okubo, and Y. Daido, "Structural determination of multilayered large signal neural-network HEMT model," IEEE Trans. Microwave Theory Tech., Vol. 46, 1367-1375, 1998.
doi:10.1109/22.721137 Google Scholar

26. Yoon, J., H. Seo, I. Choi, and B. Kim, "Wideband LNA using a negative GM cell for improvement of linearity and noise figure," Journal of Electromagnetic Waves and Applications, Vol. 24, No. 5-6, 619-630, 2010.
doi:10.1163/156939310791036412 Google Scholar

27. Lee, M.-W. and S.-H. Kam, "A highly efficient three-stage doherty power amplifier with flat gain for WCDMA applications," Journal of Electromagnetic Waves and Applications, Vol. 24, No. 17-18, 2537-2545, 2010.
doi:10.1163/156939310793675619 Google Scholar

28. Shi, X., K. S. Yeo, W. M. Lim, M. A. Do, and C. C. Boon, "A spice compatible model of on-wafer coupled interconnects for CMOS RFICS," Progress In Electromagnetics Research, Vol. 102, 287-299, 2010.
doi:10.2528/PIER10010608 Google Scholar

29. Sacha, G. M., F. B. Rodriguez, E. Serrano, and P. Varona, "Generalized image charge method to calculate electrostatic magnitudes at the nanoscale powered by artificial neural networks," Journal of Electromagnetic Waves and Applications, Vol. 24, No. 8-9, 1145-1155, 2010.
doi:10.1163/156939310791586160 Google Scholar

Dr. Nan Li

Professor Xiuping Li

Dr. Shanguo Quan