Title :
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
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;
Conference_Titel :
Electrical and Computer Engineering (CCECE), 2011 24th Canadian Conference on
Conference_Location :
Niagara Falls, ON
Print_ISBN :
978-1-4244-9788-1
Electronic_ISBN :
0840-7789
DOI :
10.1109/CCECE.2011.6030574
