In-Situ Inverse Design of a Plasma Metamaterial Beam Steering Device
POSTER
Abstract
Inverse design is a methodology for creating devices that manipulate electromagnetic (EM) waves by algorithmically modifying device parameters to achieve a desired functionality. Utilizing plasma as the 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.
We use black-box optimization algorithms 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 measures 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 in-silico inverse design are demonstrated, with the in-situ algorithm resulting in ~100x better isolation between ports. This work presents guidelines for choosing optimizers for noisy, high-dimensional physical systems.
We use black-box optimization algorithms 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 measures 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 in-silico inverse design are demonstrated, with the in-situ algorithm resulting in ~100x better isolation between ports. This work presents guidelines for choosing optimizers for noisy, high-dimensional physical systems.
Presenters
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Katherine Bronstein
Oregon State University
Authors
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Katherine Bronstein
Oregon State University
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Noah A Harris
Oregon State University
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Aleczander J Harder
Oregon State University
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Jennay L Edmondson
Oregon State University
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Jesse A Rodriguez
Oregon State University