In-Situ Inverse Design of a Plasma Metamaterial Beam Steering Device

Abstract

Inverse design is a commonly used methodology for creating devices that manipulate electromagnetic (EM) waves by algorithmically modifying device parameters to achieve a desired functionality. Utilizing plasma, a dynamically tunable medium, allows the optimization of the design process to be conducted directly on the experimental hardware (in-situ). A key advantage of this method is the creation of devices that are inherently switchable and dynamically reconfigurable. Bayesian optimization is used to tune the plasma density of 91 independent discharges that make up a plasma metamaterial (PMM) device to steer incoming EM waves to desired exit waveguides. Measurements were conducted in an automated loop where a vector network analyzer records the PMM transmission characteristics for each device setting. By relying only on measured scattering parameters, this gradient-free approach is robust to experimental drift and noise and does not require complex full-wave models. Significant performance improvements over traditional simulation-based (in-silico) inverse design are demonstrated, with in-situ Bayesian optimization achieving up to 10,000x higher isolation between ports than the best in-silico design at the same target frequency. This work also presents guidelines for applying Bayesian optimization to noisy, high-dimensional physical systems.