The performance prediction model of NMOSFET based on BP neural network

Abstract

A neural network prediction model for NMOFET device is proposed in this paper by using BP algorithm in machine learning technology and Silvaco TCAD simulation tool, so as to improve the efficiency of actual simulation work and provide a new method for the performance research of NMOSFET. In the process of modeling, the substrate bias, the impurity concentration of substrate, the thickness of oxide and the threshold voltage adjustment implant doping concentration are regarded as independent variable, and substituted into the simulation software for simulation. The performance parameters of NMOSFET are obtained as the dependent variable, such as the threshold voltage, the maximum transconductance, the subthreshold slope and the Ion/Ioff ratio. All the simulation results are used to assisted the training and prediction of BP neural network. The results of experiment show that the BP neural network prediction model with Levenberg-Marquardt algorithm can well predict the performance of NMOSFET devices. The determination coefficient of all models is above 0.99, and the mean absolute percentage error of all models is below 1.5%.

Deep Neural Networks-Based Direct-Current Operation Prediction and Circuit Migration Design

“…Fu et al [19] presented a model based on the backpropagation neural network for the prediction of NMOS performance parameters. The modeling process involved employing substrate bias, substrate impurity concentration, oxide thickness, and adjusted implant doping concentration for threshold voltage as independent variables while considering threshold voltage and others as dependent variables.…”