ChatLS: Multimodal Retrieval-Augmented Generation and Chain-of-Thought for Logic Synthesis Script Customization

Large Language Models (LLMs) have demonstrated significant potential in automating the Electronic Design Automation (EDA) process through effective integration with EDA tools. This paper targets the customization of logic synthesis scripts, which is crucial for accommodating the unique characteristics of each design in the EDA workflow. The proposed framework, called ChatLS, integrates multimodal retrieval-augmented generation (RAG) and chain-of-thought (CoT) reasoning, enabling LLMs to collaboratively analyze design features and precisely customize synthesis scripts. Experimental results demonstrate that ChatLS has achieved superior performance in customizing synthesis scripts with a commercial logic synthesis tool.