Computation cost reduction in 3D shape optimization of nanophotonic components

Abstract

Inverse design methodologies effectively optimize many design parameters of a photonic device with respect to a primary objective, uncovering locally optimal designs in a typically non-convex parameter space. Often, a variety of secondary objectives (performance metrics) also need to be considered before fabrication takes place. Hence, a large collection of optimized designs is useful, as their performance on secondary objectives often varies. For certain classes of components such as shape-optimized devices, the most efficient optimization approach is to begin with 2D optimization from random parameter initialization and then follow up with 3D re-optimization. Nevertheless, the latter stage is substantially time- and resource-intensive. Thus, obtaining a desired collection of optimized designs through repeated 3D optimizations is a computational challenge. To address this issue, a machine learning-based regression model is proposed to reduce the computation cost involved in the 3D optimization stage. The regression model correlates the 2D and 3D optimized structural parameters based on a small dataset. Using the predicted design parameters from this model as the initial condition for 3D optimization, the same optima are reached faster. The effectiveness of this approach is demonstrated in the shape optimization-based inverse design of TE₀-TE₁ mode converters, an important component in mode-division multiplexing applications. The final optimized designs are identical in both approaches, but leveraging a machine learning-based regression model offers a 35% reduction in computation load for the 3D optimization step. The approach provides a more effective means for sampling larger numbers of 3D optimized designs.