Oxidation sharpening of silicon tips in the atmospheric environment
Bibliographic record
Abstract
Sharp tips are essential for high-resolution atomic force microscopy (AFM) imaging and high-performance electron emitters in vacuum microelectronic devices. Thermal oxidation at high temperature followed by oxide removal is widely used in the nanofabrication of sharp silicon AFM/emitter tips. This method relies on the fact that oxide grows slower on areas with a smaller radius of curvature. Thermal oxidation is commonly carried out in a dedicated oxidation furnace that is costly, and the tips or wafer of tips must be cleaned thoroughly using Radio Corporation of America (RCA) cleaning. Here, the authors report that oxidation sharpening can also be attained using a very low-cost generic box furnace in the atmospheric environment that does not require the tips to go through an RCA cleaning process. As is apparent, such cleaning is not convenient for millimeter-scale AFM probes. The minimum tip apex radius of 2.5 nm was obtained by oxidation at 950 °C in the atmospheric environment. The obvious application of this approach is the regeneration of sharp tips out of worn out and thus blunt AFM probes at very low cost.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".