WaveFormer: A Behavior-Aware Transformer for Analog Performance Prediction Via Circuit Response Pretraining

Analog-circuit response curves describe how observed voltages and currents vary with stimulus and physical coordinates, providing supervision beyond individual scalar specifications. We introduce WAVEFORMER, a framework that learns circuit representations from DC, complex AC, and transient responses and transfers its shared circuit–testbench backbone to scalar performance prediction. Shared target-aware circuit and function-aware testbench encoders condition three analysis-specific multiscale response heads, jointly trained with masked value and shape losses. The WAVEFORMER BENCHMARK pairs 33,695 parameter configurations across 30 topologies with response curves and supports evaluation of response fidelity, representation transfer, and model-guided sizing. On held-out configurations of known topologies, WAVEFORMER reduces response normalized root mean squared error (NRMSE) by 25.9–46.5% relative to Deep-ONet. With the same performance-predictor architecture and fine-tuning budget,response pretraining reduces Primary NRMSE by 4.7%, with improvements in DC and AC metrics but a slight degradation in transient metrics. The resulting performance predictor also guides SPICE-verified sizing, connecting response-supervised learning to circuit design.