Artificial intelligence techniques for SPICE optimization of MOSFET modeling

Author

Suseno, Jatmiko E. ; Riyadi, Munawar A. ; Alias, Nurul Ezaila ; Heong, Yau Wei ; Ismail, Razali

Author_Institution

Electr. Eng. Fac., Univ. Teknology Malaysia (UTM), Malaysia

fYear

2009

fDate

25-26 July 2009

Firstpage

76

Lastpage

80

Abstract

This paper proposes new method for optimize and verified electric characterization graph of MOSFET by using artificial neural network. Optimization using neural network (ONN) will compare current-voltage (I-V) characteristic graph between the TCAD simulation and TSPICE modeling as desire data control a model parameter of BSIM. In this paper, the neural network method is dynamic feedforward neural network. After NN training, the best result is at neural network architecture of 36-30-10-5 with mean squared error (MSE) of 1e-28 at epoch of 5.

Keywords

MOSFET; SPICE; artificial intelligence; feedforward neural nets; graph theory; mean square error methods; technology CAD (electronics); MOSFET modeling; SPICE optimization; TCAD simulation; TSPICE modeling; artificial intelligence techniques; artificial neural network; dynamic feedforward neural network; electric characterization graph; mean squared error; Artificial intelligence; Artificial neural networks; CMOS technology; FETs; MOSFET circuits; Neural networks; Optimization methods; SPICE; Turing machines; Voltage;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovative Technologies in Intelligent Systems and Industrial Applications, 2009. CITISIA 2009

Conference_Location

Monash

Print_ISBN

978-1-4244-2886-1

Electronic_ISBN

978-1-4244-2887-8

Type

conf

DOI

10.1109/CITISIA.2009.5224238

Filename

5224238