Balancing Objective Optimization and Constraint Satisfaction for Robust Analog Circuit Optimization

Automated design of analog integrated circuits (ICs) involves balancing multiple objectives under process, voltage, and temperature (PVT) variations. An excess of constraints can ensnare algorithms in local optima, while the variations elevate the costs of simulation. To address this challenge, we propose a two-search mode multi-task evolutionary framework to balance objective optimization and constraint satisfaction under variations. Specifically, considering the inherent relationships between objective optimizations and constraint violations, our method adaptively switches between unconstrained surrogate-assisted and constrained simulation-driven search modes. Furthermore, our framework treats PVT variations as a multi-task challenge, facilitating inter-corner knowledge transfer via multi-task evolution, substantially lowering simulation costs. Our framework has been evaluated using two different sensing elements and an amplifier within a 22 nm process. Based on Monte-Carlo simulations, compared to multi-task reinforcement learning, this method attains a 60% to 80% reduction in the relative inaccuracy of sensing elements and accomplishes a 60% decrease in total runtime.