Neural Network-Based and Modeling With High Accuracy and Potential Model Speed

In this brief, we demonstrate a neural network (NN) -based device modeling framework. This NN model is built to model advanced field-effect transistors (FETs). Specific transfer functions and loss functions are chosen to achieve high accuracy and smoothness in the output of this NN model. Both I – V (current–voltage) and C – V (capacitance–voltage) characteristics are studied in this work. Speed comparison between the NN-based model and Berkeley short-channel IGFET model (BSIM) has been done to show that NN has a great potential to accelerate circuit simulation speed. We also present that this NN modeling framework is not only useful for more Moore technologies e. g. , gate-all-around FET (GAAFET) but also beyond Moore transistors e. g. , negative capacitance FET (NCFET).