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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.18342 |
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Table of Contents:
- 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 $μ\mathrm{m}^2$ field, with ~ 1 $μ\mathrm{m}$ 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.