Delta-Seeker: Closed-Loop Generative Search for Novel Circuit Design Discovery

Abstract

Generative models are increasingly used for circuit synthesis and optimization by proposing topologies, sizing devices, and exploring large design spaces. We investigate whether they can instead support design discovery, where generated structures must depart from known designs while producing reproducible electrical behavior. We propose Delta-Seeker, a closed-loop circuit search framework that combines discrete diffusion for topology generation and mutation with structural checking, SPICE-in-the-loop sizing, Pareto selection, and feedback from prior search outcomes. The search controller uses progress and recurring structural failures to allocate evaluation effort and steer subsequent topology generation, while independent re-sizing and operating-corner analysis are performed only after search. In generator-only experiments, cross-generation feedback increases structurally valid children from 143 to 945 and reduces the faulty-candidate fraction from 0.925 to 0.761. In op-amp topology discovery at matched sizing cost, Delta-Seeker yields 54 designs that remain feasible in at least three of four independent re-sizings and 18 that additionally pass device-level checks and supply-temperature corner analysis, compared with 38 and 10 under fixed control. Removing cross-generation memory reduces independently robust discoveries from 54 to 43 and seven-corner robust discoveries from 40 to 27. In DC-DC converter search, nearest-parent analysis identifies a corpus-absent topology whose generated switching current path changes its nearest training-corpus parent from infeasible at all four operating corners to feasible at all four, while increasing median efficiency from 31.2% to 98.96%. These results show that closed-loop generative search can support circuit discovery beyond structural novelty by identifying new topologies with independently verified changes in circuit operation.