Across RF Cycles: Poisson-Closed Neural Operators for Capacitively Coupled Plasmas

Neural operators (NOs) offer efficient surrogates for time-dependent physical systems, but their use in specialized plasma applications remains limited. We develop a tailored NO for parameterized one-dimensional capacitively coupled plasmas (CCPs), which underpin plasma-assisted processes (e.g., semiconductor etching). CCP dynamics are driven by radio-frequency (RF) forcing, producing strongly oscillatory trajectories that relax over many cycles toward a quasi-periodic state. These multiscale, long-horizon dynamics are difficult for conventional autoregressive NOs because prediction errors accumulate across repeated RF cycles. We address these challenges through a Poisson-closure rollout, Floquet–Poincare-inspired regularization, and random subsequence training for scalable long-trajectory learning. We introduce a parameterized 1D CCP problem as a benchmark for developing and evaluating NOs.