LLM Agents as Catalysts for Resilient DFT: An Orchestration-Based Framework Beyond Brittle Scripts

As the complexity of Very-Large-Scale Integration (VLSI) circuits escalates, Design-for-Test (DFT) faces significant challenges. Traditional script-based automation flows are increasingly complex and present a high technical barrier for non-specialists. In order to overcome the above issue, this paper introduces DFTAgent, a novel framework that leverages Large Language Models to intelligently orchestrate a DFT toolchain. DFTAgent is evaluated on the ISCAS’85, ISCAS’89, and ITC’99 benchmarks. The results demonstrate that DFTAgent successfully completes the complete ATPG task cycle, achieving fault coverage comparable to a manually scripted baseline while exhibiting significant advantages in flexibility and error handling. By abstracting complex DFT tools behind a natural language interface and a visual workflow, this approach promises to democratize access to advanced VLSI testing methodologies and accelerate design cycles.