RGB-ACD: RGB-Inspired Topology Generation for LLM-Guided Analog Circuit Design

Automating analog circuit design requires searching discrete topologies and continuous device parameters under coupled performance constraints. Translating natural-language requirements into circuit structures that satisfy these constraints remains challenging. We introduce RGB-inspired Analog Circuit Design (RGB-ACD), a large language model (LLM)-guided agent for topology generation and device sizing. RGB-ACD represents circuit topologies as three-channel RGB images and uses this encoding to learn a mapping from text to circuit connectivity. We construct a dataset of 35,200 text-topology pairs and train a generator that predicts electrical connections while preserving fixed device structure. To meet performance targets, the agent couples an outer topology-refinement loop with an inner parameter-optimization loop. The outer LLM uses simulation residuals to revise topology-generation instructions while retaining the original targets. For each candidate topology, the inner loop uses finite-difference gradient estimates to update sensitive parameter groups under process constraints. RGB-ACD outperforms all evaluated baselines in success rate across all three difficulty levels on OCB:Ckt-Bench-101 and AMSBench-Design. On hard tasks, success rates reach 66% and 51%, exceeding the strongest baselines by 42 and 30 percentage points, respectively.