The Nano Paradox: AI and Machine Learning in Predicting Semiconductor Degradation

Semiconductors such as MOSFETs and advanced transistor devices form the foundation of modern electronics, enabling applications in artificial intelligence, communication systems, and power electronics. However, as device dimensions scale down to the nano-level, their behavior becomes increasingly complex, giving rise to what is known as the “Nano Paradox,” where improved performance is often accompanied by reduced reliability. At this scale, degradation mechanisms such as thermal stress, voltage fluctuations, electromigration, and radiation effects significantly impact device lifespan. Traditional reliability prediction methods are largely reactive and struggle to accurately model such complex physical behaviors. These methods typically detect failures after they occur, making them inefficient for modern high-performance and mission-critical systems. To address this limitation, this research explores the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques for predictive analysis of semiconductor degradation. In this study, various ML models including regression techniques, neural networks, and deep learning approaches are utilized to analyze sensor data such as temperature, voltage, and current. These models are capable of identifying hidden patterns and degradation trends that are not easily detectable using conventional methods. The primary objective of this research is to develop a predictive framework that can determine not only when a semiconductor device is likely to fail, but also why the failure occurs by analyzing contributing factors. This enables early warning systems that support proactive reliability management and informed decision-making. The results demonstrate that AI-driven models can significantly improve prediction accuracy and reduce unexpected failures. Due to limited laboratory access, the study primarily relies on simulated degradation behavior and publicly available datasets. By transitioning from reactive to predictive maintenance, this approach enhances device reliability, extends operational life, and supports the development of robust next-generation electronic systems.