In this paper, we present an overview of our innovative computational methodology based on statistical learning optimization to optimize highly efficient and robust metasurface designs. We have optimized highly efficient single- and multi-functional devices. In addition, we have extended our multi-objective optimization to account for manufacturing imperfections.
The numerical modeling of light interaction with nanostructured materials is at the heart of many computational photonics studies. A typical example of interest to the present work is the simulation of light trapping in complex photovoltaic devices. This can be a challenging task when the underlying material layers are textured in a very general way. Very often, such studies rely on the Finite Difference Time-Domain (FDTD) method. The FDTD method is a widely used approach for solving the system of time-domain Maxwell equations in the presence of heterogenous media and complex three-dimensional structures. In the classical formulation of the this method, the whole computational domain is discretized using a uniform structured (Cartesian) grid. In this work, we consider an alternative approach by adapting and exploiting a particular finite element method, which is able to deal with topography conforming geometrical models based on non-uniform discretization meshes. The underlying modeling method is known as the Discontinuous Galerkin Time-Domain (DGTD) method. It is a discontinuous finite element type that relies on a high order interpolation of the electromagnetic field components within each cell of an unstructured tetrahedral mesh.
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