Robust and Scalable Flat‐Optics on Flexible Substrates via Evolutionary Neural Networks

Abstract

In the past 20 years, flat‐optics has emerged as a promising light manipulation technology, surpassing bulk optics in performance, versatility, and miniaturization capabilities. As of today, however, this technology is yet to find widespread commercial applications. One of the challenges is obtaining scalable and highly efficient designs that can withstand the fabrication errors associated with nanoscale manufacturing techniques. This problem becomes more severe in flexible structures, in which deformations appear naturally when flat‐optics structures are conformally applied to, for example, biocompatible substrates. Herein, an inverse design platform that enables the fast design of flexible flat‐optics that maintain high performance under deformations of their original geometry is presented. The platform leverages on suitably designed evolutionary large‐scale optimizers, equipped with fast‐trained neural network predictors based on encoder decoder architectures. This approach supports the implementation of flexible flat‐optics robust to both fabrication errors or user‐defined perturbation stress. This method is validated by a series of experiments in which broadband flexible light polarizers, which maintain an average polarization efficiency of 80% over 200 nm bandwidths when measured under large mechanical deformations, are realized. These results could be helpful for the realization of a robust class of flexible flat‐optics for biosensing, imaging, and biomedical devices.

Keywords

• flexible flat optics
• inverse design
• neural networks
• robust design