ANN-Driven Modeling of Gate-All-Around Transistors Incorporating Complete Current Profiles

Abstract

In this article, we present an Artificial Neural Network (ANN)-based compact model that accurately captures the complete current characteristics of gate-all-around transistors, including drain, gate, and substrate currents. Unlike previous models, our approach simplifies the modeling of substrate current by defining a simple conversion function and by utilizing simpler loss functions that account for physical effects such as impact ionization. This accurate representation of substrate current is critical for addressing hot-carrier-induced reliability concerns. The proposed model is extensively validated with calibrated Technology Computer-Aided Design (TCAD) simulations as well as with experimental data from multiple technologies. Additionally, it demonstrates smooth higher-order derivatives in symmetry tests, ensuring its suitability for RF applications.