A Generative AI and RAG-Driven Framework for Zero-Touch Gate level simulations and Timing Debug
Abstract
Abstract—Gate-Level Simulation (GLS) remains a critical gatekeeper in the semiconductor industry for catching elusive clock-domain crossing (CDC) issues, power-up initialization anomalies, and post-layout timing violations. However, it is universally acknowledged as a major bottleneck in the ASIC tape-out cycle. Malformed netlist collaterals, divergent netlist-to-RTL hierarchies, brittle testbench migration loops, and prolonged simulation runtimes routinely derail schedules, stalling verification teams for weeks. This paper introduces generative AI and RAG driven framework, a production-proven framework that fully automates and left-shifts the end-to-end GLS lifecycle. The framework eliminates manual bottlenecks through three core innovations:
1.An intelligent Physical Design (PD) Gatekeeper & Testbench Linter that executes multi-core parallel netlist audits under two hours and dynamically patches simulation environments on the fly.
2.A Constraint-Driven Test Selection Engine that intersects static Synopsys Design Constraints (SDC) attributes with dynamic RTL functional coverage to isolate high-ROI, bug-hunting test cases using a novel mathematical toggle-density formulation.
3.A Retrieval-Augmented Generation (RAG) Debug Engine that utilizes Large Language Models (LLMs) to compress multi-gigabyte waveform databases into localized state-delta tables, accelerating root-cause analysis for timing and X-propagation defects.
Deployed on a massive, industrial-scale design containing millions of gates, this generative AI and retrieval-augmented framework dramatically accelerated the initial environment setup timeline, cut down the time required for netlist audits, and significantly shortened troubleshooting cycles. By substantially boosting engineering productivity while simultaneously lowering computing expenses, the framework effectively transforms gate-level simulation from a high-risk project bottleneck into an automated, rapid, and predictable milestone.
1.An intelligent Physical Design (PD) Gatekeeper & Testbench Linter that executes multi-core parallel netlist audits under two hours and dynamically patches simulation environments on the fly.
2.A Constraint-Driven Test Selection Engine that intersects static Synopsys Design Constraints (SDC) attributes with dynamic RTL functional coverage to isolate high-ROI, bug-hunting test cases using a novel mathematical toggle-density formulation.
3.A Retrieval-Augmented Generation (RAG) Debug Engine that utilizes Large Language Models (LLMs) to compress multi-gigabyte waveform databases into localized state-delta tables, accelerating root-cause analysis for timing and X-propagation defects.
Deployed on a massive, industrial-scale design containing millions of gates, this generative AI and retrieval-augmented framework dramatically accelerated the initial environment setup timeline, cut down the time required for netlist audits, and significantly shortened troubleshooting cycles. By substantially boosting engineering productivity while simultaneously lowering computing expenses, the framework effectively transforms gate-level simulation from a high-risk project bottleneck into an automated, rapid, and predictable milestone.
