Investigating the detection limit of subsurface holes under graphite with atomic force acoustic microscopy
Bibliographic record
Abstract
The subsurface imaging capabilities of atomic force acoustic microscopy (AFAM) was investigated by imaging graphite flakes suspended over holes in a silicon dioxide substrate. The graphite thickness and the hole size were varied to determine the detection limit on the maximum graphite thickness and the smallest detectable hole size. Parameters including operating frequency, eigenmode, contact force, and cantilever stiffness were investigated for their influence of defect detection. AFAM was reliably able to detect 2.5 μm diameter holes through a maximum graphite thickness of 570 nm and sub 100 nm holes through 140 nm of graphite. The smallest detectable defect size was a 50 nm hole covered by an 80 nm thick graphite flake. Increasing the graphite thickness and decreasing the hole size both resulted in a decrease in subsurface contrast. However, the non-linear trend observed from increasing the graphite thickness indicates thickness has a greater effect on subsurface defect detection than variations in defect size. Through investigating various parameters, we have found certain cases to increase the observed contrast of the embedded subsurface holes, however the smallest detectable defect size remained the same. This technique's ability to reveal sub 100 nm defects buried under graphite has previously only been demonstrated in much softer polymer systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.001 | 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 source (direct Gemma or distilled Codex), 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".