Analog Design

Analog circuit design, traditionally a time-consuming and expertise-intensive process, is undergoing a transformation through the application of machine learning. Current research focuses on automating the design process using various techniques, including variational autoencoders, Bayesian optimization, reinforcement learning, and evolutionary algorithms, often enhanced by incorporating domain-specific knowledge through graph neural networks or large language models. These advancements aim to significantly accelerate and improve the efficiency of analog circuit design, impacting both the speed of technological innovation and the cost-effectiveness of integrated circuit development.