Direct-write three-dimensional nanofabrication of nanopyramids and nanocones on Si by nanotumefaction using a helium ion microscope
Bibliographic record
Abstract
The recently commercialized helium ion microscope (HIM) has already demonstrated its outstanding imaging capabilities in terms of resolution, surface sensitivity, depth of field and ease of charge compensation. Here, we show its exceptional patterning capabilities by fabricating dense lines and three-dimensional (3D) nanostructures on a Si substrate. Small focusing spot size and confined ion-Si interaction volume of a high-energy helium ion beam account for the high resolution in HIM patterning. We demonstrate that a set of resolvable parallel lines with a half pitch as small as 3.5 nm can be achieved. During helium ion bombardment of the Si surface, implantation outperforms milling due to the small mass of the helium ions, which produces tumefaction instead of depression in the Si surface. The Si surface tumefaction is the result of different kinetic processes including diffusion, coalescence and nanobubble formation of the implanted ions, and is found to be very stable structurally at room temperature. Under appropriate conditions, a linear dependence of the surface swollen height on the ion doses can be observed. This relation has enabled us to fabricate nanopyramids and nanocones, thus demonstrating that HIM patterning provides a new 'bottom-up' approach to fabricate 3D nanostructures. This surface tumefaction method is direct, both positioning and height accurate, and free of resist, etch, mode and precursor, and it promises new applications in nanoimprint mold fabrication and photomask clear defect reparation.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".