Revolutionizing functional verification: The impact of AI and machine learning in chip design

Surendra, Praveen Kumar Manchikoni (2025) Revolutionizing functional verification: The impact of AI and machine learning in chip design. World Journal of Advanced Research and Reviews, 26 (1). pp. 2484-2490. ISSN 2581-9615

This article presents a comprehensive overview of how artificial intelligence and machine learning technologies are revolutionizing functional verification in modern chip design. As semiconductor complexity escalates with advanced process nodes enabling billions of transistors on a single die, traditional verification methods face insurmountable challenges in ensuring design correctness. The verification bottleneck has become the dominant constraint in chip development cycles, consuming the majority of resources and frequently allowing critical bugs to escape to silicon. The integration of AI/ML techniques offers transformative solutions across multiple verification domains, including intelligent test generation, coverage analysis optimization, and bug prediction. These technologies enable more efficient resource allocation, targeted verification of high-risk design areas, and significantly accelerated coverage closure. The article examines implementation strategies for AI-driven verification systems and presents concrete case studies demonstrating measurable improvements in verification efficiency, quality, and time-to-market