Direct Detection of Inhomogeneity in CVD-Grown 2D TMD Materials via K-Means Clustering Raman Analysis

Abstract

It is known that complex growth environments often induce inhomogeneity in two-dimensional (2D) materials and significantly restrict their applications. In this paper, we proposed an efficient method to analyze the inhomogeneity of 2D materials by combination of Raman spectroscopy and unsupervised k-means clustering analysis. Taking advantage of k-means analysis, it can provide not only the characteristic Raman spectrum for each cluster but also the cluster spatial maps. It has been demonstrated that inhomogeneities and their spatial distributions are simultaneously revealed in all CVD-grown MoS2, WS2 and WSe2 samples. Uniform p-type doping and varied tensile strain were found in polycrystalline monolayer MoS2 from the grain boundary and edges to the grain center (single crystal). The bilayer MoS2 with AA and AB stacking are shown to have relatively uniform p-doping but a gradual increase of compressive strain from center to the periphery. Irregular distribution of 2LA(M)/E2g1 mode in WS2 and E2g1 mode in WSe2 is revealed due to defect and strain, respectively. All the inhomogeneity could be directly characterized in color-coded Raman imaging with correlated characteristic spectra. Moreover, the influence of strain and doping in the MoS2 can be well decoupled and be spatially verified by correlating with the clustered maps. Our k-means clustering Raman analysis can dramatically simplify the inhomogeneity analysis for large Raman data in 2D materials, paving the way towards direct evaluation for high quality 2D materials.

In Operando Study of Charge Modulation in MoS2 Transistors by Excitonic Reflection Microscopy

“…Charge inhomogeneities are frequent in 2D materials and notably result from trapped charges in the substrate, and adsorbates, which create μm-sized variations in the 2D material's E c . 26,27 The same is supported by the pink ROI (located in the "valley") which at 1.8 V has caught up to the orange ROI while being offset in prior time frames. Up to here, the presented images corresponded to a reflectivity change.…”

A simple KPFM-based approach for electrostatic- free topographic measurements: the case of MoS2 on SiO2

“…Typical defects include atomic scale defects (in particular sulfur vacancies) and grain boundaries [20,21], charge fluctuations associated with the former defects and with trapped charges in the substrate, symmetry breaking at edges [24], oxidation sites [25], adsorbates, growth nucleation sites and/or multilayer areas, molecules intercalated between the TMD and substrate and mechanical strain [26]. Studies using Raman and photoluminescence mapping [21,22,25,[27][28][29][30][31][32] often report that The β area displays a lower conductivity σ (and therefore a lower C' z ) than the flake interior (α area). Therefore, the flake interior (higher σ and C' z ) appears 'lifted' with respect to its periphery (β area) under the influence of the electrostatic force (i.e.…”

“…the edges and tips of crystalline TMD flakes behave differently from the core region. In particular, in CVD-based samples, it is well-established that mechanical strain resulting from the difference in thermal coefficient of the TMD and the substrate is a major cause of intra-flake inhomogeneity of the optical properties [27,[30][31][32]. This strain, accumulated during the cooling stage of the CVD synthesis, is differentially relaxed at edges, tips and grain boundaries.…”

Automated statistical analysis of raman spectra of nanomaterials

“…58 Deep-learning techniques have been reported for automatic denoise of Raman spectra of graphene, 59 fit specific Raman bands and isolate the most informative Raman features to extract crystallinity or functionalization. 60 Similarly, the thickness of TMDCs and inhomogeneity can be automatically classified using neural networks 61 and k -means clustering analysis, 62 respectively. These tools have been developed to meet specific user requirements, mostly related to the automatic assessment of material quality for industrial applications.…”