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Record W4401006013 · doi:10.1093/mam/ozae044.133

Atomically Resolved Secondary Electron Imaging for Bulk Materials

2024· article· en· W4401006013 on OpenAlexaff
Sooyeon Hwang, Lijun Wu, Kim Kisslinger, Judith C. Yang, R.F. Egerton, Yimei Zhu

Bibliographic record

VenueMicroscopy and Microanalysis · 2024
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceElectronSecondary electronsNanotechnologyPhysicsNuclear physics

Abstract

fetched live from OpenAlex

The development of electron microscopes has revolutionized our ability to study materials at high resolution, providing us with new insights into the relationship between a material's structure and its properties. Among these techniques, the scanning electron microscope is widely used in both academic and industrial research and has played a major role in the development and analysis of new materials and devices. SEMs primarily reveal the surface topography of a material by imaging secondary electrons (SEs) that are generated within a shallow depth of the surface. In general, the resolution of these images is as high as a few nanometers, but it has been shown that atomic level visualization can be achieved [1-3] using a spherical aberration-corrected scanning transmission electron microscopy (STEM). Notably, these achievements have been accomplished with samples that were thin enough to be electron-transparent, which raises the question of whether atomic-resolution SE imaging can be achieved with thick, bulk samples. Traditional (S)TEM imaging necessitates specimen thinning to mitigate signal degradation from multiple scattering. Nonetheless, the interest in thick samples persists due to their closer representation of real material properties. In this study, we evaluated the feasibility of atomically resolving SE imaging for bulk samples. As a model system, a silicon specimen, shaped as an isosceles right-angle triangle with a maximum thickness of 18 μm, was fabricated from a (100) Si wafer substrate utilizing a focused ion beam in situ lift-out technique. The distinctive geometry of the specimen (Figure 1A) enables precise measurement of sample thickness along the beam trajectory, as it mirrors the lateral distance from the thin edge of the sample. SE images were obtained using the Hitachi HD2700C dedicated STEM equipped with a spherical aberration corrector, operating at 200 kV with a convergence semi-angle of 22 mrad. Figure 1B showcases a series of SE images obtained from different sample thicknesses, illustrating the atomic structure with discernible "dumbbells" (pairs of Si atoms positioned around 0.14 nm apart) in general. It is noteworthy that the overall intensity of the averaged image increases with the thickness of the sample. We employ a Gaussian function to quantify and compare the signal and background intensities based on the atomic positions within the SE image intensity profile (Figure 2A): where IPk, x0, w and IBkg are the amplitude, position, width (full width at half maximum) of the peak and background intensity, respectively. Figure 2B shows the peak intensity remains generally constant while the background intensity increases across varying sample thicknesses. Signal-to-background ratio (IPk/IBkg) slightly decreases with sample thickness (Figure 2C), primarily attributed to the increase of the background intensity with increasing thickness. This study showcases the ability to acquire SE images of atomic columns from a thick (bulk) sample using a 200 kV aberration-corrected STEM. These findings unveil a new avenue for investigating the atomic-scale structure of bulk materials without the necessity of thin samples [4]. (A) a scanning electron microscopy image of Si wedge sample acquired from a 52° angled direction. (B) SE images of Si from different thicknesses. Each image displayed here is an averaged results from 5x5 unit cells to improve the signal-to-noise ratio and remove scanning distortion. The upper and lower intensity limits were manually adjusted using the same window settings to facilitate a comparative analysis of background and signal variations corresponding to the sample thicknesses. (A) Intensity profile along the red line in the inset, presenting the averaged SE image at a sample thickness of 1 μm. Experimental data points are depicted as dots, with a red curve indicating the Gaussian-fitting results. ISi and Iv indicate image intensities at Si column and at the valley between Si dumbbells, respectively. (B) IPk (= ISi-Iv) and IBkg (≈ Iv) at different sample thicknesses. (C) Signal-to-background ratio as a function of sample thickness.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.006
GPT teacher head0.280
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractno

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