Artificial Neural Network Based CNTFETs Modeling

Ji Zhang; Sheng Chang; Hao Wang; Jin He; Qi Jun Huang

doi:10.4028/www.scientific.net/AMM.667.390

Paper Titles

Study on Monitoring Bacillus Subtilis Fermentation Process by Mid-Infrared Spectroscopy
p.368

Found Robust Calibration Model in Fermentation Process by Combining Different Sample Sets
p.372

A Low Power Reference Buffer Used in High-Speed High-Precision Pipelined ADC
p.379

A Novel Three-Phase Current Source Active Power Filter
p.383

Artificial Neural Network Based CNTFETs Modeling
p.390

Research on Efficiency of Solar Cell Integrated with Energy Management Circuitry on Chip
p.396

A CMOS Bandgap Reference with Temperature Compensation
p.401

Ultraviolet LED Light Source System for Lithography
p.405

PMSM Sensorless Field-Oriented Control System Based on Sliding-Mode Observer
p.411

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 667Artificial Neural Network Based CNTFETs Modeling

Artificial Neural Network Based CNTFETs Modeling

Abstract:

Based on artificial neural network (ANN), a new method of modeling carbon nanotube field effect transistors (CNTFETs) is developed. This paper presents two ANN CNTFET models, including P-type CNTFET (PCNTFET) and N-type CNTFET (NCNTFET). In order to describe the devices more accurately, a segmentation voltage of the voltage between gate and source is defined for each type of CNTFET to segment the workspace of CNTFET. With the smooth connection by a quasi-Fermi function for, the two segmented networks of CNTFET are integrated into a whole device model and implemented by Verilog-A. To validate the ANN CNTFET models, quantitative test with different device intrinsic parameters are done. Furthermore, a complementary CNTFET inverter is designed using these NCNTFET and PCNTFET ANN models. The performances of the inverter show that our models are both efficient and accurate for simulation of nanometer scale circuits.

You might also be interested in these eBooks

Advances in Computers, Electronics and Mechatronics

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 667)

Pages:

390-395

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.667.390

Citation:

Cite this paper

Online since:

October 2014

Authors:

Ji Zhang*, Sheng Chang, Hao Wang, Jin He, Qi Jun Huang

Keywords:

ANN, Back-Propagation (BP) Algorithm, CNTFET, Verilog-A

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] F. D'Agostino and D. Quercia: Introduction to VLSI design (EECS 467) (2000).

Google Scholar

[2] J. Guo, S. O. Koswatta, N. Neophytou and M. Lundstrom: International journal of high speed electronics and systems Vol. 16(2006), p.897.

Google Scholar

[3] T. Dürkop, S. A. Getty, E. Cobas and M. S. Fuhrer: Nano letters, Vol. 4(2004), p.35.

Google Scholar

[4] Q. J. Zhang, K. C. Gupta: Neural networks for RF and microwave design (Book+ Neuromodeler Disk) (Artech House, Inc., USA 2000).

Google Scholar

[5] Zhang Q J, K. C. Gupta and V. K. Devabhaktuni: Microwave Theory and Techniques, IEEE Transactions on Vol. 51(2003), p.1339.

Google Scholar

[6] J. J. Gao, L. Zhang, J. Xu, and Q. J. Zhang: Microwave Symposium Digest, 2005 IEEE MTT-S International, (2005), p.469.

Google Scholar

[7] H. B. Hammouda, M. Mhiri, Z. Gafsi, and K. Besbes: American Journal of Applied Sciences Vol. 5 (2008), p.385.

Google Scholar

[8] M. Hayati, A. Rezaei, M. Seifi: Solid-State Electronics, Vol. 54(2010), p.52.

Google Scholar

[9] D. Jie and H. S. P. Wong: Electron Devices, IEEE Transactions on Vol. 54(2007), p.3186.

Google Scholar

[10] D. Jie and H. S. P. Wong: Electron Devices, IEEE Transactions on Vol. 54(2007), p.3195.

Google Scholar

[11] Information on http: /www. mathworks. cn/cn/help/nnet/ref/initnw. html.

Google Scholar

[12] Information on http: /www. mathworks. cn/cn/help/nnet/ref/trainbr. html.

Google Scholar