Accurate and Efficient Modeling of SOI MOSFET With Technology Independent Neural Networks

Neuro-fuzzy network approach for modeling submicron MOSFETs: application to MOSFET subcircuit simulation

“…The ANN structure consists of neurons, which contain an input, weight coefficient, and a nonlinear activation function. The network is organized as input, output, and hidden layers [11].…”

Advanced Statistical Methodologies for Tolerance Analysis in Analog Circuit Design

“…Given the high computational costs of the Spice models, their approximation through cheaper functions is the first step in many numerical procedures on microelectronic circuits. Within the vast set of methods proposed by researchers on the matter (Ampazis & Perantonis, 2002a;Daems et al, 2003;Friedman, 1991;Hatami et al, 2004;Hershenson et al, 2001;McConaghy et al, 2009;Taher et al, 2005;Vancorenland et al, 2001) in Table 3 we report a numerical comparison between two well reputed fitting methods and our proposed Reverse Spice based algorithm (for short RS). The methods are Multivariate Adaptive Regression Splines (MARS) (Friedman, 1991), i.e.…”

Advances in Analog Circuits

“…Given the high computational costs of the Spice models, their approximation through cheaper functions is the first step in many numerical procedures on microelectronic circuits. Within the vast set of methods proposed by researchers on the matter (Ampazis & Perantonis, 2002a;Daems et al, 2003;Friedman, 1991;Hatami et al, 2004;Hershenson et al, 2001;McConaghy et al, 2009;Taher et al, 2005;Vancorenland et al, 2001) in Table 3 we report a numerical comparison between two well reputed fitting methods and our proposed Reverse Spice based algorithm (for short RS). The methods are Multivariate Adaptive Regression Splines (MARS) (Friedman, 1991) (PNN) (Elder IV & Brown, 2000).…”