Machine Learning Techniques for Analog and RF Circuit Design: A Review of Modeling, Optimization, and Design Automation

The complexity of analog and radio frequency (RF) circuit design is well known. The methods that have traditionally been used to design these circuits become increasingly inefficient as the complexity of the circuits increases. Many researchers have attempted to employ machine learning methods to improve the efficiency of the analog and RF circuit design process. A literature review of these different methodologies was performed in order to provide an overview of the current state-of-the-art in the field of machine learning for analog and RF circuit design. Main scientific databases were searched between 2015 and 2025 for relevant publications on the use of machine learning for analog and RF circuit design. The findings in each of these publications were categorized according to their use of machine learning for tasks such as circuit modeling, yield prediction, parameter optimization, RF analysis, and automation of design workflows. The survey of existing literature reveals that machine learning methods, including neural networks, Gaussian processes, support vector machines, Bayesian optimization, and reinforcement learning methods have been shown to significantly improve the efficiency of analog and RF circuit design processes. However, challenges in the availability of data, interpretability of the learned models, and the generalizability of those models to different circuit design problems remain. Overall, the integration of machine learning methods into analog and RF circuit design processes appears to be most effective when the machine learning methods are integrated with traditional analog and RF circuit design methods. The findings of this literature review can be utilized by future researchers and engineers in the field in order to develop next-generation analog and RF circuit design methodologies based upon machine learning.