Application and Development of EDA Technology in Chip Design: The Evolutionary Path from Traditional Design to Intelligent Optimization

DOI: https://doi.org/10.62517/jike.202604124

Author(s)

Yili Cheng

Affiliation(s)

Integrated Circuit Design and Integrated System, Wuhan University of Technology, China

Abstract

Electronic Design Automation (EDA) serves as a vital tool in the integrated circuit design industry. This paper summarizes the current state of EDA tools and focuses on exploring the convergence between EDA tools and artificial intelligence (AI) technology. It outlines the current development landscape and the challenges encountered. Solutions to overcome these challenges are investigated, and the future direction of EDA-AI tools is predicted.

Keywords

Electronic Design Automation; Artificial Intelligence; Integrated Circuits

References

[1] Liu Shuning, Guan Duanduan, Li Yuliang, et al. Current Status and Standardization of EDA in the Integrated Circuit Field [J]. China Standardization, 2024, (20): 49-53. [2] SEMI-ESDA, "Electronic Design Market Data 2023 Annual Summary Report," May 2024 [3] Shen Jinlu, Feng Guonan, Lü Peijue. EDA Industry Advancement: AI Reshaping the Design Ecosystem [J]. China Industry and Information Technology, 2025, (06): 8-14. [4] SIEMENS, A New Era of EDA Powered by EDA, August 2024 [5] Synopsys, Synopsys Posts Financial Results for Fourth Quarter and Fiscal Year 2024, December 2024 [6] Xiong, X. M., Zhan, S. Y., Rao, B., et al. Key Technology Challenges and Collaborative Innovation Pathways for China's Integrated Circuits [J]. Journal of Guangdong University of Technology, 2025, 42 (06): 1-11. [7] Synopsys, Synopsys Posts Financial Results for Second Quarter Fiscal Year 2025, May 2025 [8] Xu, Q., Stok, L., Drechsler, R., Wang, X., Zhang, G. L., & Markov, I. L. (2025). Revolution or hype? Seeking the limits of large models in hardware design [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2509.04905 [9] Fang, W. J., Wang, J., Lu, Y., Liu, S., Wu, Y. C., Ma, Y. Z., & Xie, Z. Y. (2025). A survey of circuit foundation models: foundation AI models for VLSI circuit design and EDA [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2504.03711 [10] Pysmennyi, I., Kyslyi, R., & Kleshch, K. (2025). AI-driven tools in modern software quality assurance: an assessment of benefits, challenges, and future directions. *Technology Audit and Production Reserves*, 3(2(83)), 44-54. https://doi.org/10.15587/2706-5448.2025.330595 [11] Li, Xiao, Chen, Lin, Chen, Shixi, Jiang, Fan, Li, Chenggeng, Zhang, Wei, & Xu, Jiang. (2024). Deep reinforcement learning-based power management for multi-core systems based on chipsets. *IEEE Transactions on Very Large Scale Integration (VLSI) Systems*, 32(9), 1726-1739. https://doi.org/10.1109/TVLSI.2024.3415487 [12] Liu, M. J., Pinckney, N., Khailany, B., & Ren, H. X. (2023). VerilogEval: evaluating large language models for Verilog code generation [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2309.07544