• Dr. Sunil Sharma
• Prof. Pradeep Chhawchharia
• Hirayana Soni
• Ravi Teli
• Dr, Arti Sharma

Modern semiconductor production requires advanced techniques because the manufacturing process needs enhanced speed control while reducing manufacturing defects. The research analyzes how Digital Twin (DT) technology serves as a groundbreaking management strategy to enhance semiconductor production optimization. A digital twin functions as a virtual representation of production processes to perform real-time evaluation which supports better efficiency and decision-making during operations. The designed framework uses advanced data analytics alongside machine learning and IoT technologies for predictive maintenance combined with quality assurance functionalities. The research proves that digital twin deployment leads to higher manufacturing output levels and decreased operational costs together with an environment that promotes steady manufacturing advancements. Manufacturers can use these discoveries to adopt digital twins because they help increase efficiency while ensuring competitive market position in today's evolving industrial environment. According to recent industry findings, companies implementing DT solutions report up to 30% reduction in downtime, 20% increase in productivity, and 25% reduction in operational costs.

Semiconductor, Digital Twin, Machine Learning, IoT, Data Analytics.

"Digital Twin Integration for Smarter Semiconductor Manufacturing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 6, page no.481-488, June-2025, Available :http://www.jetir.org/papers/JETIRGW06079.pdf

"Digital Twin Integration for Smarter Semiconductor Manufacturing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 6, page no. pp481-488, June-2025, Available at : http://www.jetir.org/papers/JETIRGW06079.pdf