Efficient parameters selection for artificial intelligence models of nanoscale MOSFETs

Author

Nohoji, Amir Hossein Abdollahi ; Farokhi, Farhad ; Shokouhifar, Mohammad ; Zamani, Mahdi

Author_Institution

Sci. Assoc. of Electr. & Electron. Eng., Islamic Azad Univ., Tehran, Iran

fYear

2011

fDate

8-11 May 2011

Abstract

In this paper, the effect of network type in modeling I-V characteristic of MOS transistors was studied. Neural networks training data are generated in Hspice environment for MOSFET BSIM3 with TSMC-0.18 technology. Training was performed in MATLAB environment while testing was done in Hspice as well. Also in this work, feature selection using UTA method is utilized for determining consistency of BSIM3 parameters in MOSFET drain current estimation.

Keywords

MOSFET; SPICE; neural chips; Hspice environment; Hspice testing; I-V characteristic; MATLAB environment; MOS transistors; MOSFET BSIM3 parameters; MOSFET drain current estimation; UTA method; artificial intelligence model; feature selection; nanoscale MOSFET; neural networks training data; parameter selection; Artificial neural networks; Integrated circuit modeling; MOSFETs; Mathematical model; Microwave theory and techniques; Neurons; Training; Hspice; MLP; MOSFET modeling; Neuro_fuzzy; feature selection;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Computer Engineering (CCECE), 2011 24th Canadian Conference on

Conference_Location

Niagara Falls, ON

ISSN

0840-7789

Print_ISBN

978-1-4244-9788-1

Electronic_ISBN

0840-7789

Type

conf

DOI

10.1109/CCECE.2011.6030574

Filename

6030574