Experiment and TCAD Hybrid-data-Driven Modeling of Advanced Node GAAFETs by Transfer Learning

Gate-All-Around transistors for advanced technology nodes present significant simulation challenges due to their intricate 3-dimensional structures and diverse process and device configurations. The scarcity of experimental data, constrained by high costs and time, further complicates the accurate calibration of robust TCAD models. This study introduces a novel neural network technique that synergizes experimental and TCAD hybrid data with transfer learning. By training a base model on a comprehensive GAAFET simulation dataset and fine-tuning it with minimal experimental data, the method reduces dependency on experimental data by over 80%. Results demonstrate a 98% improvement in predictive accuracy and a substantial reduction in calibration time compared to conventional methods. This approach highlights the potential to significantly enhance the efficiency and accuracy of advanced node device design and development.