SANE: A Neural Surrogate for Accelerated Multi-Topology Analog Circuit Simulation

Analog circuit simulation with numerical ODE solvers is reliable, but repeated transient analysis can be too slow for parameter sweeps, tolerance studies, and optimisation. This paper presents \textbf{SANE} (\textbf{S}pectral-\textbf{A}ware \textbf{N}eural \textbf{E}mulation), a single causal Temporal Convolutional Network (TCN) trained to emulate transient waveforms for \textbf{21 analog circuit topologies}. The loss combines time-domain $\ell_1$ error with multi-resolution STFT terms on spectral magnitude and phase. This helps the shared model capture both waveform shape and frequency content across different circuits. The dataset contains \num{176100} simulated waveform pairs spanning passive filters, diode and transistor circuits, operational amplifier stages, and switching converters. On this set, SANE reaches a mean NRMSE of 8.39\% across all topologies. After training, inference takes \SI{0.127}{\milli\second} per sample on an RTX~5090. The reference solver takes from milliseconds to tens of seconds on a single CPU core, corresponding to a mean speed-up of about ${\sim}38{,}000\times$ across the library. Because the model is differentiable, it can also be used for gradient-based parameter optimisation.