Machine Learning in Interpolation and Extrapolation for Nanophotonic Inverse Design.

ABSTRACT: The algorithmic design of nanophotonic structures promises to significantly improve the efficiency of nanophotonic components due to the strong dependence of electromagnetic function on geometry and the unintuitive connection between structure and response. Such approaches, however, can be highly computationally intensive and do not ensure a globally optimal solution. Recent theoretical results suggest that machine learning techniques could address these issues as they are capable of modeling the response of nanophotonic structures at orders of magnitude lower time per result. In this work, we explore the utilization of artificial neural network (ANN) techniques to improve the algorithmic design of simple absorbing nanophotonic structures. We show that different approaches show various apt

SUBMITTER: Acharige D 

PROVIDER: S-EPMC9494689 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature