International Journal on Science and Technology

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Leveraging AI for Optimal Design Margins in Modern Semiconductor Design

Author(s) Puneet Gupta
Country United States
Abstract This article explores the transformative role of artificial intelligence in addressing the challenges of on-chipvariations and design optimization in advanced semiconductor nodes. As the semiconductor industrypushes toward smaller process nodes, traditional methods of managing variations through conservativedesign margins have become increasingly unsustainable. The article examines how AI-driven approachesare revolutionizing design methodology through enhanced model generation, intelligent marginoptimization, and advanced analytics for design implementation. The article investigates the application ofmachine learning in physical design optimization, process variation modeling, and cross-technologyscaling. Results demonstrate that AI-based solutions can significantly improve power-performance-areatrade-offs while reducing design closure cycles and enhancing manufacturing yield. The article alsodiscusses the integration of AI capabilities with existing EDA tools and presents prospects for AIapplications in semiconductor design.
Keywords Artificial Intelligence, Semiconductor Design, On-Chip Variations, Machine Learning, Design Optimization, Process Variation, Electronic Design Automation, Advanced Technology Nodes
Field Computer
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-04-03
Cite This Leveraging AI for Optimal Design Margins in Modern Semiconductor Design - Puneet Gupta - IJSAT Volume 16, Issue 2, April-June 2025.
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