Characterization of WSe <sub>2</sub> Films Using Reflection Kikuchi Diffraction in the Scanning Electron Microscope and Multivariate Statistical Analyses
Bibliographic record
Abstract
The study of thin films and two-dimensional (2D) materials, including transition metal dichalcogenides such as WSe 2 offers opportunities to leverage their properties in advanced sensors, quantum technologies, and devices to optimize functional performance. In this work, we characterize thin WSe 2 samples with variable thicknesses using scanning electron microscope (SEM)-based techniques focused on analysis of the backscattered electron signal and Kikuchi diffraction patterns. These data were collected via a pixelated electron-counting direct electron detector positioned below the pole piece primarily configured for reflection Kikuchi diffraction (RKD), and a similar detector placed in the more conventional electron backscatter diffraction (EBSD) geometry. In addition to conventional pattern analysis for orientation microscopy, multivariate statistical methods (MSA) based on principal component analysis were applied to analyze diffraction patterns and differentiate thickness variations and crystal orientations within the thin films through data clustering. These results were compared with atomic force microscopy to validate thickness measurements. Our findings indicate that RKD combined with MSA is highly effective for characterizing 2D materials, enabling simultaneous assessment of thickness and crystallographic orientation. Systematic acceleration voltage variations in RKD experiments and comparisons with EBSD data suggest that the thickness dependency arises from inelastic scattering of diffracted electrons, which affects pattern contrast in the thin-film regime. Collection and analysis of patterns obtained from monolayer, bilayer, and trilayer of WSe 2 are also demonstrated. This work reinforces the utility of SEM-based techniques, such as RKD, as valuable tools for the materials characterization toolkit, particularly for thin films and 2D materials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".