DIVE: Dynamic Information-Guided Variable Expansion for Deeper Analog Circuit Optimization

In analog circuit design, transistor sizing remains a critical challenge due to high-dimensional parameter spaces and expensive simulations. While Bayesian optimization shows promise, existing methods struggle with the "curse of dimensionality."Inspired by expert designers' workflow of focusing on key parameters first before gradually optimizing secondary parameters, we introduce DIVE (Dynamic Information-guided Variable Expansion), a framework that reformulates parameter optimization as an information efficiency maximization problem. Unlike approaches that explore all parameters simultaneously, DIVE progressively includes design variables with the highest information content. Our framework introduces three innovations: (1) constraint-aware weighted mutual information analysis that evaluates parameters' contributions to objectives and constraints; (2) an adaptive variable inclusion mechanism that determines when to expand the optimization space; and (3) a mutual information-guided kernel learning strategy for Gaussian process models. Evaluations across multiple analog circuits demonstrate that DIVE achieves a 1.61×-21.11× reduction in required simulations while delivering up to 2.69× spec improvements over the state-of-the-art methods. By progressing from simple to complex parameter spaces based on information theory, DIVE establishes a new paradigm for reaching deeper optima by mimicking experienced designers' design philosophy in circuit optimization.