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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 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 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 to 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. 

Silicon-based spin qubits represent a promising technology for scalable quantum computing. However, the complex nature of this field, which requires a deep understanding of quantum mechanics, materials science, and nanoelectronics, poses a significant challenge in making it accessible to future engineers and scientists. Spin Quantum Gate Lab, a spin qubit simulation tool, is proposed in this paper to address this obstacle. This tool is designed to introduce key concepts of spin qubit to undergraduate students, enabling the simulation of single-qubit rotational gates and two-qubit controlled-phase gates. By providing hands-on experience with quantum gate operations, it effectively links theoretical quantum concepts to practical experience, fostering a deeper understanding of silicon-based quantum computing.