SMS-Sizing: An Agentic Search Framework for Feasible Analog Circuit Sizing and Archive Expansion

Analog circuit sizing selects transistor dimensions, bias currents, capacitor values, and other device parameters for a fixed topology so that simulated performance satisfies all specifications simultaneously. Each candidate design requires a circuit simulation to determine whether it meets all specifications. Our goal is to find multiple feasible designs with distinct parameter configurations within a limited simulation budget. Some existing sizing methods simplify the search by restricting parameter ranges or discretizing design variables. These restrictions can exclude feasible designs and limit the diversity of the solutions found. We propose Sensitivity-Manifold Search (\method). Compared with prior work, our study makes three contributions. First, we formulate the task as expanding one verified feasible design into a set of distinct alternatives within the original parameter space, and define metrics for feasible yield and design diversity. Second, we develop a search framework that generates candidates from verified designs, uses surrogate predictions to guide candidate selection and simulation budget allocation, and checks region updates against simulation evidence. Third, we use baseline comparisons and ablation studies to assess performance and examine how different components affect feasible yield and design diversity. All designs added to the archive are verified by circuit simulation. Across six amplifier topologies under eight simultaneous constraints, starting from a single feasible seed and using a budget of 100 simulations, \method reliably expands the design archive. Anchored candidates achieve a $59.4\%$ conversion rate, while uniform random search finds no feasible designs in our experiments. In head-to-head comparisons, \method maintains up to $2.8\times$ greater spatial reach and $2.1\times$ larger worst-case constraint margins, preserving candidate diversity across spatial separation resolutions.