AI-Assisted Data-Driven RFIC Designs: A Review of Recent Progress, Design Frameworks, and Challenges

Radio-Frequency Integrated Circuits (RFICs) are foundational to modern wireless and wireline communication and sensing applications, including high-speed wireless 5G and 6G networks, satellite links, wireline interconnects for chip-to-chip and processor-to-memory signaling, and various radar and imaging systems. Distinct from analog or digital circuit design, RF circuits require the joint design and optimization of three-dimensional (3D) electromagnetic (EM) structures as passive components (governed by Maxwell’s equations) and nonlinear transistors as active gain components (determined by device physics). With this multiphysics nature, traditional RFIC design flows are human-centric and rely heavily on expert intuition and extensive iterative EM simulations, which often lead to long design cycles and restricted design spaces. Recently, inspired by progress in the photonics, antenna, microwave modeling, and electronic design automation (EDA) communities, artificial intelligence (AI) and machine learning (ML) have gained increasing traction as data-driven tools for augmenting RF circuit design. The goal is to convert EM-and circuit-level data into reusable models, fast evaluators, and inverse-design engines that can accelerate end-to-end RF circuit synthesis from performance specifications (specs) to layouts and/or explore new RF circuits with non-intuitive, non-conventional passive EM components. In this view, data-driven RFIC automation depends not only on model architectures, but also on how EM- and circuit-simulation labels are generated, reused, and verified in downstream design flows. This paper seeks to review this fast-growing technology direction. The paper starts by reviewing AI-assisted surrogate modeling for RFIC EM passives, including surrogates based on tabular, image, and graph representations of on-chip EM passives, together with neural operators that can improve surrogate learning capability. As a unique contribution of this paper, reported data-gene...