Abstract

Ultra Large Scale Integration (ULSI) circuits have revolutionized modern electronics by integrating millions to billions of transistors on a single chip. Ensuring the reliability of these complex systems is a critical challenge due to various aging, environmental, and operational factors. This paper presents an AI-driven approach to reliability modeling for ULSI systems. By leveraging advanced machine learning algorithms, the proposed framework predicts potential failure points and enhances system robustness. Experimental results demonstrate that AI models outperform traditional statistical methods in accuracy and computational efficiency. The study highlights the benefits of integrating AI into reliability engineering for next-generation integrated circuits. Keywords; ULSI, Reliability Modeling, Artificial Intelligence, Machine Learning, Predictive Maintenance, Integrated Circuits