🤖 AI Summary
To address the low efficiency and difficulty in root-cause localization during pre-signoff MCMM timing debugging in VLSI design, this paper proposes a multi-LLM collaborative intelligent agent system. Methodologically, it introduces (1) the Timing Debugging Relation Graph (TDRG)—the first domain-specific knowledge graph integrating circuit topology and timing constraints; (2) an Agentic RAG framework unifying graph-based retrieval, executable code reasoning, and hierarchical planning; and (3) an end-to-end pipeline for automated report parsing, root-cause identification, and repair recommendation generation. Evaluated on industrial-scale benchmarks, the system achieves 98% success rate on single-report debugging and 90% on multi-report joint debugging, substantially reducing debug turnaround time. This work represents the first systematic adoption of embodied intelligent agents in VLSI timing verification, establishing a new paradigm for AI-driven signoff automation.
📝 Abstract
Timing analysis is an essential and demanding verification method for Very Large Scale Integrated (VLSI) circuit design and optimization. In addition, it also serves as the cornerstone of the final sign-off, determining whether the chip is ready to be sent to the semiconductor foundry for fabrication. Recently, as the technology advance relentlessly, smaller metal pitches and the increasing number of devices have led to greater challenges and longer turn-around-time for experienced human designers to debug timing issues from the Multi-Corner Multi-Mode (MCMM) timing reports. As a result, an efficient and intelligent methodology is highly necessary and essential for debugging timing issues and reduce the turnaround times. Recently, Large Language Models (LLMs) have shown great promise across various tasks in language understanding and interactive decision-making, incorporating reasoning and actions. In this work, we propose a timing analysis agent, that is empowered by multi-LLMs task solving, and incorporates a novel hierarchical planning and solving flow to automate the analysis of timing reports from commercial tool. In addition, we build a Timing Debug Relation Graph (TDRG) that connects the reports with the relationships of debug traces from experienced timing engineers. The timing analysis agent employs the novel Agentic Retrieval Augmented Generation (RAG) approach, that includes agent and coding to retrieve data accurately, on the developed TDRG. In our studies, the proposed timing analysis agent achieves an average 98% pass-rate on a single-report benchmark and a 90% pass-rate for multi-report benchmark from industrial designs, demonstrating its effectiveness and adaptability.