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Characterizing transition-metal dichalcogenide thin-films using hyperspectral imaging and machine learning.

ABSTRACT: Atomically thin polycrystalline transition-metal dichalcogenides (TMDs) are relevant to both fundamental science investigation and applications. TMD thin-films present uniquely difficult challenges to effective nanoscale crystalline characterization. Here we present a method to quickly characterize the nanocrystalline grain structure and texture of monolayer WS₂ films using scanning nanobeam electron diffraction coupled with multivariate statistical analysis of the resulting data. Our analysis pipeline is highly generalizable and is a useful alternative to the time consuming, complex, and system-dependent methodology traditionally used to analyze spatially resolved electron diffraction measurements.

SUBMITTER: Shevitski B

PROVIDER: S-EPMC7360754 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Characterizing transition-metal dichalcogenide thin-films using hyperspectral imaging and machine learning.

Shevitski Brian B Chen Christopher T CT Kastl Christoph C Kuykendall Tevye T Schwartzberg Adam A Aloni Shaul S Zettl Alex A

Scientific reports 20200714 1

Atomically thin polycrystalline transition-metal dichalcogenides (TMDs) are relevant to both fundamental science investigation and applications. TMD thin-films present uniquely difficult challenges to effective nanoscale crystalline characterization. Here we present a method to quickly characterize the nanocrystalline grain structure and texture of monolayer WS<sub>2</sub> films using scanning nanobeam electron diffraction coupled with multivariate statistical analysis of the resulting data. Our ...[more]

PMID: 32665582

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