Deep Learning Driven Modeling of Advanced Node FinFET

This study presents a novel application of deep learning algorithms to enhance the modeling of 14 nm FinFETs, specifically addressing the critical aspect of material discovery. While Density Functional Theory (DFT) is essential for material characterization, its computational intensity and time-consuming nature pose significant limitations. Similarly, shallow machine learning (ML) methods, despite their utility, often struggle with extensive data preprocessing, overfitting, and inherent biases. Our approach overcomes these challenges by integrating advanced data processing with deep learning for material discovery, specifically tailored for advanced node FinFET<br/>modeling.We meticulously prepared material descriptors and demonstrated the superior performance of deep learning in this context. With minimal fine-tuning, our deep learning model achieved a Mean Absolute Error (MAE) of approximately 0.14 eV. This performance significantly surpasses that of traditional shallow learning methods, including Support Vector Regression, Random Forest, and Extreme Gradient Boosting (XGBoost), as evidenced by a higher R² score. These results underscore the exceptional proficiency of deep learning in accelerating material discovery and, consequently, improving the accuracy of advanced node FinFET modeling. This research highlights the profound efficiency of deep learning in pushing the boundaries of semiconductor device simulation.