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 <tex-math notation="LaTeX">I </tex-math> – <tex-math notation="LaTeX">V </tex-math> (current–voltage) and <tex-math notation="LaTeX">C </tex-math> – <tex-math notation="LaTeX">V </tex-math> (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)].