A Context-Aware Feedback Loop for AI-Assisted Verification IP Synthesis: Bridging the Gap from Natural Language to Regression-Ready

The widening gap between System-on-Chip (SoC) design complexity and verification productivity has rendered traditional script-based automation insufficient. While Large Language Models (LLMs) offer promise for code synthesis, they typically fail in hardware verification contexts due to a lack of architectural consistency and an inability to reason about temporal signal semantics. This paper proposes a Context-Aware Verification Loop (CAVL) methodology, an iterative framework that integrates semantic project indexing with simulation-based feedback to achieve verification closure. Unlike static generation, CAVL employs a dynamic refinement cycle where compiler diagnostics, simulation logs, and functional coverage metrics serve as feedback signals to guide the AI agent. We validate this framework on a dual-mode I2C Universal Verification Methodology (UVM) environment as a representative case study. The experimental results indicate, within this single-protocol context, the framework’s capacity to (1) resolve complex signal-level contention issues through logic refactoring, (2) achieve complete functional coverage via directed test synthesis, and (3) maintain cross-file architectural consistency with reduced human intervention. This work presents an initial quantitative baseline for AI-driven Electronic Design Automation (EDA), suggesting that context-aware feedback loops offer a pathway toward restructuring the verification engineer’s role from implementation to architectural intent specification.