Enhancing Chip Fabrication Reliability Through AI-Powered Predictive Maintenance and Anomaly Detection: A Study

Megha Potdar, Dr. Andhe Dharani

Abstract: The semiconductor industry is pivotal in advancing technology, with computer chip fabrication being a core component. Maintaining high efficiency and reliability in these fabrication processes is critical, and the integration of artificial intelligence (AI) has shown significant promise in achieving these goals. This literature survey explores the application of AI techniques in predictive maintenance and anomaly detection within computer chip fabrication processes. Predictive maintenance utilizes AI algorithms to foresee potential equipment failures, thereby minimizing downtime and optimizing operational efficiency. Anomaly detection leverages AI to identify deviations from normal operational patterns, enabling early detection of defects and process irregularities. This survey reviews various AI methodologies, including machine learning models, neural networks, and data analytics, that have been implemented or proposed in recent studies. Furthermore, it examines the challenges, benefits, and future directions of integrating AI in semiconductor manufacturing. The findings underscore the transformative potential of AI in enhancing the productivity, quality, and sustainability of computer chip fabrication processes.

Keywords: Predictive Maintenance, Chip Fabrication, Semiconductors, AI Analysis, Immediate Insights

How to Cite?: Megha Potdar, Dr. Andhe Dharani, "Enhancing Chip Fabrication Reliability Through AI-Powered Predictive Maintenance and Anomaly Detection: A Study", Volume 15 Issue 1, January 2026, International Journal of Science and Research (IJSR), Pages: 1482-1484, https://www.ijsr.net/getabstract.php?paperid=SR26119143424, DOI: https://dx.doi.org/10.21275/SR26119143424