A Hybrid Machine Learning and Physics-Based Model for Quasi-Ballistic Nanotransistors

We introduce a hybrid model that synergistically combines machine learning (ML) with semiconductor device physics to simulate nanoscale transistors. This approach integrates a physics-based (PB) ballistic transistor model with an ML model that predicts ballisticity, enabling flexibility to interface the model with device data. The inclusion of device physics not only enhances the interpretability of the ML model but also streamlines its training process, reducing the necessity for extensive training data. The model’s effectiveness is validated on both silicon nanotransistors and carbon nanotube (CNT) field-effect transistors (FETs), demonstrating high model accuracy with a simplified ML component. We assess the impacts of various ML models—multilayer perceptron (MLP), recurrent neural network (RNN), and RandomForestRegressor (RFR)—on predictive accuracy and training data requirements. Notably, hybrid models incorporating these components can maintain high accuracy with a small training dataset, with the RNN-based model exhibiting better accuracy compared with the MLP and RFR models. The trained hybrid model provides significant speedup compared to device simulations and can be applied to predict circuit characteristics based on the modeled nanotransistors.