集成电路设计是一项极为复杂且需要大量人力交互的工作，随着集成度不断提升，设计正确性、性能、功耗和面积目标的保证变得愈发困难，需要花费大量的设计时间和精力。然而，幸运的是，随着人工智能的发展，特别是大模型的应用，我们正处于一个设计革命的时代！AI在EDA领域的应用已经展现出巨大的潜力，涵盖了代码生成、仿真验证、逻辑综合、可测试性设计、布局布线以及流程控制等多个关键方面。我们对过去三年关键会议期刊的论文及引用延伸的论文进行了调研、总结与分类归纳，并整理了我们已发现的开源代码。由于笔者水平有限，难免在分类和描述上有疏漏和不足之处，恳请读者批评指正。

引用：

@article{CASTEST2024AIEDA,

  title = {Summary of Artificial Intelligence and Electronic Design Automation},

  author = {Jing Ye, Jianan Mu, Zhiteng Chao, Jiaping Tang, Mingjun Wang, Bin Sun, Rengang Zhang, Xinyu Zhang, Ge Yu, Tenghui Hua, Zexi Zhao, Leihan Zhang, Jutao Xiao},

  year = {2024},

  url = {http://www.castest.com.cn/AIEDA}

}

作者：

叶靖，穆嘉楠，晁志腾，汤家平，赵艺璇，王铭珺，孙彬，张仁刚，张鑫宇，余戈，华腾辉，赵泽熙，张镭瀚，肖举涛，李佳乐，戴沁銮

联系我们：

info@castest.com.cn

期刊会议列表：

Auto flow control and parameter optimization

Auto generation of specification, code, or benchmark

Clock tree optimization

Design for test

FPGA synthesis, placement, and routing

Hardware security

High level synthesis

Lithography

Physical feature analysis and prediction

Placement

and routing

Power delivery network prediction

PPA prediction toward synthesis, placement, and routing

Reliability

Routability prediction

Standard cell library design optimization

Sub-resolution assist feature generation

Survey and others

Synthesis, gate sizing, technology mapping

Timing analysis and prediction

Verification, simulation, and debug

Yield learning

AI+EDA

开源代码

3D IC

Architecture or microarchitecture design optimization