Title :
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
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;
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
DOI :
10.1109/CITISIA.2009.5224238
