AI based design of 2D material integrated optical polarizers

TL;DR

This paper introduces a machine learning approach using neural networks to efficiently design 2D material-based optical polarizers, significantly reducing computational time while maintaining high accuracy, validated by experimental results.

Contribution

The study develops a neural network model that predicts polarizer performance across a large parameter space, enabling rapid and accurate design of 2D material integrated optical polarizers.

Findings

Reduced computational time by about 4 orders of magnitude.

Achieved average FOM prediction deviation of less than 0.04.

Fabricated devices' FOMs closely matched predictions with less than 0.2 discrepancy.

Abstract

On-chip integration of highly anisotropic two-dimensional (2D) materials offers new opportunities for realizing high performance polarization selective devices. Obtaining optimized designs for such devices requires extensively sweeping large parameter spaces, which in conventional approaches relies on massive mode simulations that demand considerable computational resources. Here, we address this limitation by developing a machine learning (ML) model based on fully connected neural networks (FCNNs). Trained by using mode simulation results for low resolution structural parameters, the FCNN model can accurately predict polarizer figures of merits (FOMs) for high resolution parameters and rapidly map the global variation trend across the entire parameter space. We test the performance of the FCNN model using two types of polarizers with 2D graphene oxide (GO) and molybdenum disulfide (MoS2). Results show that, compared to conventional mode simulation approach, our approach can not only reduce the overall computing time by about 4 orders of magnitude, but also achieve highly accurate FOM predictions with an average deviation of less than 0.04. In addition, the measured FOM values for the fabricated devices show good agreement with the predicted ones, with discrepancies remaining below 0.2. These results validate artificial intelligence (AI) as an effective approach for designing and optimizing 2D-material based optical polarizers with high efficiency.