Helium ion beam lithography and liftoff
Bibliographic record
Abstract
Abstract We introduce a helium ion beam lithography and liftoff process to fabricate arbitrary nanostructures. Exploiting existing high-resolution positive tone resists such as poly (methyl methacrylate) (PMMA), the process offers three significant advantages over electron beam lithography: (a) the exposing helium ion beam produces a high secondary electron yield leading to fast patterning, (b) proximity effects are negligible due to the low count of backscattered helium ions from the substrate, and (c) the process is transferrable with minimal alteration among different types of substrates (e.g. silicon, fused silica). The process can be used to pattern any material compatible with liftoff such as evaporated metals or dielectrics, and allows overlay of nanostructures precision-aligned to microstructures realised beforehand on the same substrate. The process is demonstrated for several PMMA thicknesses to liftoff different thicknesses of deposited material. Resolution trials are conducted to determine the limits of the process for each PMMA thickness. Isolated lines as narrow as 14 nm, and line-space gratings of 40 nm pitch (50% duty cycle), are produced as resolution tests by lifting off a 20 nm thick Au film. Nanostructures of aspect ratio up to ∼3:1 have been realised. Plasmonic nanoantenna arrays overlaid to microscale contacts are produced as device demonstrators, for which optical measurements are in excellent agreement with theory.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".