Adjoint-Based Gradient Evaluation for Metasurface Inverse Design via Affine Geometric Transformations
This work addresses the need for advanced inverse design techniques in nanofabrication to enable large-scale and complex metasurface functionalities, though it appears incremental by building on existing adjoint variable methods.
The authors tackled the problem of inverse design for large metasurfaces by developing a systematic adjoint-based gradient evaluation method, achieving efficient optimization with minimal computational cost as demonstrated through numerical validation.
The sharp increasing in fabrication capabilities of nanomaterials, and complex structures such as meta-surfaces and metalens, has opened to the possibility of employing them for accurately control the electromagnetic field, beyond the possibility ensured by traditional devices. The demand for large scale structures and more complex functionalities from meta-surfaces lead to the research for advanced techniques of inverse design, able to conjugate the ability to produce effective designs and limited computational cost. Among the various approaches for inverse design of large meta-surfaces, the ones based on the adjoint variable method are appealing since able to ensure a minimal computational cost for the gradient computation of the cost function. In this work, a systematic methodology for the application of the adjoint variable method for large meta-surface design is presented. The method is based on: (i) a parametrization of the relevant geometric parameters of the meta-atoms, (ii) the fast computation of the gradient with respect such parameters, allowing for the implementation of general affine transformations during the optimization process. The main findings are first theoretically justified and a numerical validation is provided to show the effectiveness of the proposed approach.