Wide-field Hyperspectral Optical Microscopy for Rapid Characterization of Two-Dimensional Semiconductors and Heterostructures

TL;DR

This paper introduces a rapid, wide-field hyperspectral optical microscopy technique for characterizing 2D semiconductors, enabling fast, non-invasive assessment of material quality and heterostructures with high spatial and spectral resolution.

Contribution

The authors develop a broadband, wide-field hyperspectral microscope that captures detailed spatial-spectral data of 2D materials within seconds, improving speed and reducing perturbation compared to traditional methods.

Findings

Detects spectral fingerprints for material identification and doping.

Identifies grain boundaries and alloy compositions.

Enables advanced analysis like machine learning-based spatial segmentation.

Abstract

Electronic and optoelectronic applications of two-dimensional (2D) semiconductors demand precise control over material quality, including thickness, composition, doping, and defect density. Conventional benchmarking methods (e.g., charge transport, confocal mapping, electron or scanning probe microscopy) are slow, perturb sample quality, or involve trade-offs between speed, resolution, and scan area. To accelerate assessment of 2D semiconductors, we demonstrate a broadband, wide-field hyperspectral optical microscope for 2D materials (2D-HOM) that rapidly captures a spatial-spectral data cube within seconds. The data cube includes x-y spatial coordinate (a 300 * 300 $\mu\mathrm{m}^2$ field, with ~ 1 $\mu\mathrm{m}$ resolution) and a selectable wavelength range between 1100 to 200 nm at each pixel. Using synthesized films and heterostructures of transition metal dichalcogenides ($\mathrm{MoS}_{2}$, $\mathrm{WS}_{2}$, $\mathrm{V}_{x}\mathrm{W}_{1-x}\mathrm{S}_{2}$, and $\mathrm{WSe}_{2}$), we show that this cost-effective technique detects spectral fingerprints of material identity, doping, grain boundaries, and alloy composition, and enables advanced analysis, including unsupervised machine learning for spatial segmentation.