AI-Enhanced Mixed-Signal Simulation and EDA for Integrated Circuit Design Using CMOS Technologies
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".
Deadline for manuscript submissions: 15 March 2026 | Viewed by 91
Special Issue Editors
Interests: fusion circuit design; circuit performance optimization; medical AI applications
Interests: physical design; GPU-accelerated EDA; machine learning for EDA
Special Issue Information
Dear Colleagues,
The relentless scaling of CMOS technologies and increasing demand for high-performance integrated circuits present formidable challenges in design complexity, verification time, and yield optimization. This Special Issue explores groundbreaking advances in artificial intelligence-driven electronic design automation (EDA) specifically tailored for RF and analog CMOS circuit design, with emphasis on mixed-signal simulation innovations.
We seek contributions addressing the convergence of AI/ML techniques with traditional RF/analog CMOS design methodologies across the following three domains: (1) intelligent mixed-signal simulation frameworks that handle the unique challenges of RF/analog circuits in advanced CMOS nodes; (2) machine learning applications for automated synthesis, optimization, and verification of RF/analog building blocks, such as LNAs, VCOs, PLLs, and data converters; and (3) AI-native EDA solutions that transform conventional IC design flows through predictive modeling and intelligent automation.
Key topics include physics-aware neural networks for IC behavioral modeling, AI-accelerated electromagnetic simulation, machine learning for process variation analysis in CMOS technologies, automated layout generation for analog/RF circuits, intelligent parasitic-aware optimization, and deep learning approaches for signal integrity analysis in mixed-signal systems.
Dr. Zhaori Bi
Dr. Zhiang Wang
Dr. Keren Zhu
Guest Editors
Manuscript Submission Information
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Keywords
- AI-native EDA
- IC design automation
- mixed-signal circuit design
- machine learning for analog/RF design
- intelligent circuit simulation
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