Bootstrap sampling style ensemble neural network for inverse design of optical nanoantennas

Abstract

The process of machine learning-assisted nanophotonicsinverse design has been plagued by the problem of non-uniqueness for a long time, which is a problem worth studying. In this paper, we present a novel methodology for the design of bowtie optical nanoantennas (BONAs) by employing a Bootstrap Sampling Style Ensemble Neural Network (BSENN) model. Our approach combines a bagging algorithm with a tandem neural network to address the non-uniqueness challenge inherent in the inverse design process of BONAs. By splitting the data, training in batches, and integrating the results, our BSENN model is able to provide reliable predictions and offer a solution to the non-uniqueness problem. The main objective of our work is to explore diverse BONAs design structures that yield identical spectral responses, thereby providing a broader range of alternatives for the design of optical nanoantennas. Through the utilization of the BSENN model, we aim to enhance the design process and offer increased flexibility and versatility in the field of optical nanoantenna design.