Enhancing Reflectarray Robustness Through Adjoint Optimization Enabled Recovery

Antenna array failures have been investigated since the early 1990s, often making use of array factor (AF) theory to accelerate the analysis. However, AF modeling is not always appropriate for reflectarray antennas (RAs) due to their operation in a scattering mode rather than being a driven antenna. Therefore, full-wave analysis is required to model RAs with failures. Unfortunately, this places a tremendous computational burden on any attempt at reoptimizing the RA to heal its performance. To overcome this issue, we introduce here an efficient method that exploits adjoint optimization (AO) to make RA healing tractable. To demonstrate the approach, a
$25\times25$
element RA is analyzed under multiple failure scenarios to determine the expected gain recovery after optimization. The proposed optimization framework achieves a
$550\times $
speed improvement over conventional approaches, thus making optimization-based RA healing tractable.