Role of AI in VLSI Device Modelling

Abstract

The rapid advancement of Artificial Intelligence (AI) has significantly impacted various scientific and engineering disciplines, including Very Large-Scale Integration (VLSI) device modeling. Traditional modeling approaches in VLSI rely on physics-based equations and empirical data, often requiring extensive computational resources and time. AI-driven methodologies, particularly machine learning and deep learning techniques, offer a more efficient and accurate alternative for device modeling and performance prediction. This thesis explores the role of AI in VLSI device modeling, emphasizing the use of neural networks, regression models, and reinforcement learning to predict critical device parameters such as current-voltage characteristics, leakage currents, and threshold voltages. By leveraging AI-based models, the research demonstrates improved accuracy and reduced computational complexity compared to conventional techniques. The study also evaluates various AI algorithms, including artificial neural networks (ANNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), in predicting semiconductor device behavior. Furthermore, this work examines the integration of AI with physics-based modeling to enhance interpretability and reliability. Comparative analysis with industry-standard simulation tools highlights the effectiveness of AI-driven approaches in optimizing VLSI device design. The research findings indicate that AI can significantly improve predictive accuracy, accelerate the design cycle, and enable real-time optimization of VLSI circuits. The study concludes that AI will play a crucial role in the future of semiconductor modeling, paving the way for more efficient, intelligent, and scalable VLSI design methodologies. This work serves as a foundation for further research into AI-driven automation in semiconductor technology.