Neural-Based Models of Semiconductor Devices for SPICE Simulator

Abstract

The paper addresses a simple and fast new approach to implement Artificial Neural Networks (ANN) models for the MOS transistor into SPICE. The proposed approach involves two steps, the modeling phase of the device by NN providing its input/output patterns, and the SPICE implementation process of the resulting model. Using the Taylor series expansion, a neural based small-signal model is derived. The reliability of our approach is validated through simulations of some circuits in DC and small-signal analyses

Double Gate MOSFET Modeling Based on Adaptive Neuro-Fuzzy Inference System for Nanoscale Circuit Simulation

“…Therefore, in HSPICE implementation, the MOSFET may be considered as a voltage-controlled current source [22]. Therefore, in HSPICE implementation, the MOSFET may be considered as a voltage-controlled current source [22].…”

“…The main element contributing to the nonlinear behavior of the DG MOSFET device is the drain current I d that is the function of the L, V ds , V gs , t ox , and t si parameters. Therefore, in HSPICE implementation, the MOSFET may be considered as a voltage-controlled current source [22]. Its general syntax is This statement creates a current source according to the expression given between quotes [23].…”

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

“…Recently, artificial neural networks (ANNs) were introduced as a powerful tool for optimization and modeling electronic devices and circuits [4,5]. ANNs can be developed even if the equivalent circuit and equation of the device are unavailable [6]. In [6], for instance, training was done using only V ds and V gs for the modeling of a MOSFET DC current using a well-known multilayer perceptron (MLP) network with 15 hidden neurons and the mean squared error (MSE) of 2.3e-4 was achieved.…”

“…ANNs can be developed even if the equivalent circuit and equation of the device are unavailable [6]. In [6], for instance, training was done using only V ds and V gs for the modeling of a MOSFET DC current using a well-known multilayer perceptron (MLP) network with 15 hidden neurons and the mean squared error (MSE) of 2.3e-4 was achieved. However, other effective parameters of the MOSFET model were neglected and those parameters dramatically decreased the network accuracy in submicron simulations.…”

Modeling semiconductor devices by using Neuro Space Mapping

“…[14], NN model of a MOS transistor is implemented into SPICE circuit simulator. In their model, drain and gate voltages are input parameters and the drain current is the NN output.…”