Revolutionizing functional verification: The impact of AI and machine learning in chip design
Year: 2025
Type: article
Abstract: 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
Institution: Central Michigan University
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7084035643
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