A Self-Calibrating Framework for Analog Circuit Sizing Using LLM-Derived Analytical Equations

Abstract:We present a design automation framework for analog circuit sizing that produces calibrated, topology-specific analytical equations from raw circuit netlists. A large language model (LLM) derives a complete Python sizing function in which each device dimension is traceable to a specific design rationale - a form of interpretable output absent from existing optimization-based and LLM-based sizing methods. A deterministic calibration loop extracts process-dependent parameters from a single DC operating point simulation, while a prediction-error feedback mechanism compensates for analytical inaccuracies. We validate the framework on circuits ranging from 6 to 30 transistors - spanning single-stage, current-mirror (simple and cascoded), folded-cascode, gain-boosted folded-cascode, two-stage Miller-compensated, nested-Miller-compensated, and complementary class-AB output topologies - across six process nodes from 32 nm to 180 nm. On matched-specification benchmarks, including the class-AB opamp case, the framework converges within a few simulations. Despite large initial prediction errors, convergence depends on the measurement-feedback architecture, not prediction accuracy. The one-shot calibration automatically captures process-dependent variations, enabling cross-node portability without modification, retraining, or per-process characterization.

14 pages, 4 figures, 7 tables, plus supplementary. V3: Extended to 8 topology families (6-30 transistors) across 6 process nodes with 5-trial statistics; adds cross-model evaluation (5 LLMs), ablation study, matched TuRBO-1 baseline, and structural audit of 2,634 LLM-derived equations

Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI)

arXiv:2604.07387 [cs.AR]

(or arXiv:2604.07387v3 [cs.AR] for this version)