Youngmin Oh

ATLAS is a Bayesian optimization framework that, at each iteration, builds a Mapper graph over the surrogate-predicted feasible region to estimate the number of connected regions and uses a topological sensitivity score to label candidates as bridge, frontier, or interior points, injecting a targeted exploration bonus into the acquisition function; on four analog circuit benchmarks in the GF180 and SKY130 processes it attains the highest or tied-highest success rate on all four circuits, reaching 100% on the hardest circuit CMA where most baselines score 0%.

ATLAS is a Bayesian optimization framework that, at each iteration, builds a Mapper graph over the surrogate-predicted feasible region to estimate the number of connected regions and uses a topological sensitivity score to label candidates as bridge, frontier, or interior points, injecting a targeted exploration bonus into the acquisition function; on four analog circuit benchmarks in the GF180 and SKY130 processes it attains the highest or tied-highest success rate on all four circuits, reaching 100% on the hardest circuit CMA where most baselines score 0%.