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Driven by Moore's Law, the complexity and scale of modern chip design areincreasing rapidly. Electronic Design Automation (EDA) has been widely appliedto address the challenges encountered in the full chip design process. However,the evolution of very large-scale integrated circuits has made chip designtime-consuming and resource-intensive, requiring substantial prior expertknowledge. Additionally, intermediate human control activities are crucial forseeking optimal solutions. In system design stage, circuits are usuallyrepresented with Hardware Description Language (HDL) as a textual format.Recently, Large Language Models (LLMs) have demonstrated their capability incontext understanding, logic reasoning and answer generation. Since circuit canbe represented with HDL in a textual format, it is reasonable to questionwhether LLMs can be leveraged in the EDA field to achieve fully automated chipdesign and generate circuits with improved power, performance, and area (PPA).In this paper, we present a systematic study on the application of LLMs in theEDA field, categorizing it into the following cases: 1) assistant chatbot, 2)HDL and script generation, and 3) HDL verification and analysis. Additionally,we highlight the future research direction, focusing on applying LLMs in logicsynthesis, physical design, multi-modal feature extraction and alignment ofcircuits. We collect relevant papers up-to-date in this field via the followinglink: https://github.com/Thinklab-SJTU/Awesome-LLM4EDA.

LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automation