Determining optical constants of 2D materials with neural networks from multi-angle reflectometry data

Ergun Simsek

doi:10.1088/2632-2153/ab6d5f

Abstract

Synthetically generated multi-angle reflectometry data is used to train a neural network based learning system to estimate the refractive index of atomically thin layered materials in the visible part of the electromagnetic spectrum. Unlike previously developed regression based optical characterization methods, the prediction is achieved via classification by using the probabilities of each input element belonging to a label as weighting coefficients in a simple analytical formula. Various types of activation functions and gradient descent optimizers are tested to determine the optimum combination yielding the best performance. For the verification of the proposed method’s accuracy, four different materials are studied. In all cases, the maximum error is calculated to be less than 0.3%. Considering the highly dispersive nature of the studied materials, this result is a substantial improvement in terms of accuracy and efficiency compared to traditional approaches.

Highlights

Synthetically generated multi-angle reflectometry data is used to train a neural network based learning system to estimate the refractive index of atomically thin layered materials in the visible part of the electromagnetic spectrum
Unlike previously developed regression based optical characterization methods, the prediction is achieved via classification by using the probabilities of each input element belonging to a label as weighting coefficients in a simple analytical formula
For example in [15], we develop a numerical method to extract the refractive index of monolayer MoS2 that are synthesized on quartz and silicon substrates with chemical vapor deposition (CVD)

Summary

25 February 2020

Keywords: classification, functional prediction, material characterization Supplementary material for this article is available online Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

Introduction: deep learning and optical material characterization

Numerical determination of optical constants of 2D materials

Numerical results: number of epochs and improving accuracy

Findings

Conclusions