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

TL;DR

This paper demonstrates an in-situ inverse design method for a plasma metamaterial beam steering device using Bayesian optimization, achieving significantly improved port isolation compared to traditional simulation-based methods.

Contribution

It introduces a robust in-situ Bayesian optimization approach for designing reconfigurable plasma metamaterials without relying on complex models.

Findings

Achieved up to 10,000x higher port isolation than in-silico designs.

Demonstrated robustness of the method against experimental noise and drift.

Provided guidelines for applying Bayesian optimization to high-dimensional physical systems.

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.