Atomic defect classification of the H–Si(100) surface through multi-mode scanning probe microscopy
Why this work is in the frame
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Bibliographic record
Abstract
The combination of scanning tunnelling microscopy (STM) and non-contact atomic force microscopy (nc-AFM) allows enhanced extraction and correlation of properties not readily available via a single imaging mode. We demonstrate this through the characterization and classification of several commonly found defects of the hydrogen-terminated silicon (100)-2 × 1 surface (H-Si(100)-2 × 1) by using six unique imaging modes. The H-Si surface was chosen as it provides a promising platform for the development of atom scale devices, with recent work showing their creation through precise desorption or placement of surface hydrogen atoms. While samples with relatively large areas of the H-Si surface are routinely created using an in situ methodology, surface defects are inevitably formed reducing the area available for patterning. By probing the surface using the different interactivity afforded by either hydrogen- or silicon-terminated tips, we are able to extract new insights regarding the atomic and electronic structure of these defects. This allows for the confirmation of literature assignments of several commonly found defects, as well as proposed classifications of previously unreported and unassigned defects. By combining insights from multiple imaging modes, better understanding of their successes and shortcomings in identifying defect structures and origins is achieved. With this, we take the first steps toward enabling the creation of superior H-Si surfaces through an improved understanding of surface defects, ultimately leading to more consistent and reliable fabrication of atom scale devices.
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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 | 0.001 | 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 it