# DRC-Coder: Automated DRC Checker Code Generation Using LLM Autonomous Agent

arXiv: https://arxiv.org/abs/2412.05311 · HTML: https://mlsanity.com/paper/2412.05311/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 28 Nov 2024.

## Builds on (most cited first, 9 of 9)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://mlsanity.com/paper/2207.01206/) (2022)
- [CodeT: Code Generation with Generated Tests](https://mlsanity.com/paper/2207.10397/) (2022)
- [VerilogCoder: Autonomous Verilog Coding Agents with Graph-based Planning and Abstract Syntax Tree (AST)-based Waveform Tracing Tool](https://mlsanity.com/paper/2408.08927/) (2024)
- [NVCell: Standard Cell Layout in Advanced Technology Nodes with Reinforcement Learning](https://mlsanity.com/paper/2107.07044/) (2021)

## Built on by (most cited first, 15 of 15)

- [A Survey of Research in Large Language Models for Electronic Design Automation](https://mlsanity.com/paper/2501.09655/) (2025)
- [KVCOMM: Online Cross-context KV-cache Communication for Efficient LLM-based Multi-agent Systems](https://mlsanity.com/paper/2510.12872/) (2025)
- [AMSnet 2.0: A Large AMS Database with AI Segmentation for Net Detection](https://mlsanity.com/paper/2505.09155/) (2025)
- [Revolution or Hype? Seeking the Limits of Large Models in Hardware Design](https://mlsanity.com/paper/2509.04905/) (2025)
- [Automated QoR improvement in OpenROAD with coding agents](https://mlsanity.com/paper/2601.06268/) (2026)
- [LLM-Based Multi-Agent Systems for Code Generation: A Multi-Vocal Literature Review](https://mlsanity.com/paper/2604.16321/) (2026)
- [Rule2DRC: Benchmarking LLM Agents for DRC Script Synthesis with Execution-Guided Test Generation](https://mlsanity.com/paper/2605.15669/) (2026)
- [Polymath: A Self-Optimizing Agent with Dynamic Hierarchical Workflow](https://mlsanity.com/paper/2508.02959/) (2025)
- [Report for NSF Workshop on AI for Electronic Design Automation](https://mlsanity.com/paper/2601.14541/) (2026)
- [Foundation-Model-Based Agents in Industrial Automation: Purposes, Capabilities, and Open Challenges](https://mlsanity.com/paper/2605.02592/) (2026)
- [EvoDRC: A Self-Evolving Agentic Framework for Automated DRC Violation Repair](https://mlsanity.com/paper/2607.20019/) (2026)
- [Agentic Electronic Design Automation: A Handoff Perspective](https://mlsanity.com/paper/2606.19795/) (2026)
- [PICopilot: An LLM-based Agentic Framework for Assisting Photonic Integrated Circuit Design via Script Generation](https://mlsanity.com/paper/2608.01791/) (2026)
- [Inspector: Conversational and Lightweight Analyzer of Analog Circuit Layouts Using LLM and CNNs](https://mlsanity.com/paper/2609.34976/) (2026)
- [THEIA: A Multimodal Dataset and Benchmark for Vision-Language Analysis of Layout](https://mlsanity.com/paper/2609.35035/) (2026)

## Abstract (arXiv)

In the advanced technology nodes, the integrated design rule checker (DRC) is often utilized in place and route tools for fast optimization loops for power-performance-area. Implementing integrated DRC checkers to meet the standard of commercial DRC tools demands extensive human expertise to interpret foundry specifications, analyze layouts, and debug code iteratively. However, this labor-intensive process, requiring to be repeated by every update of technology nodes, prolongs the turnaround time of designing circuits. In this paper, we present DRC-Coder, a multi-agent framework with vision capabilities for automated DRC code generation. By incorporating vision language models and large language models (LLM), DRC-Coder can effectively process textual, visual, and layout information to perform rule interpretation and coding by two specialized LLMs. We also design an auto-evaluation function for LLMs to enable DRC code debugging. Experimental results show that targeting on a sub-3nm technology node for a state-of-the-art standard cell layout tool, DRC-Coder achieves perfect F1 score 1.000 in generating DRC codes for meeting the standard of a commercial DRC tool, highly outperforming standard prompting techniques (F1=0.631). DRC-Coder can generate code for each design rule within four minutes on average, which significantly accelerates technology advancement and reduces engineering costs.