Analog Circuit

Analog circuit design, traditionally a labor-intensive process requiring significant human expertise, is undergoing a transformation driven by machine learning. Current research focuses on automating various aspects of the design process, from generating circuit topologies and sizing components to predicting circuit performance and extracting design constraints, employing models such as variational autoencoders, language models, graph neural networks, and Bayesian optimization techniques. These advancements promise to significantly accelerate and improve the efficiency of analog circuit design, impacting fields ranging from integrated circuit fabrication to neuromorphic computing.