Neon and helium focused ion beam etching of resist patterns
Bibliographic record
Abstract
Abstract Helium ion microscopy has attracted many applications in imaging, nanofabrication and analysis. One important field of study in nanofabrication using ion beam is the milling or etching of materials using a helium or neon focused ion beam (FIB), with and without chemical gas assistance. In particular, the neon FIB has a relatively high sputtering rate with a lower probability of swelling and less re-deposition issues compared to a helium FIB. Here, both neon and helium FIB etchings are investigated for milling and repairing electron-beam lithography (EBL) defined hydrogen silsesquioxane (HSQ) and polymethyl methacrylate (PMMA) resist patterns. Different dosages of neon FIB etching result in distinct etching profiles. Using the appropriate doses, arrays of uniform gap with aspect ratio more than 20 can be achieved on HSQ nanostructures. The neon FIB etching has a resolution of 20 nm on HSQ patterns. With XeF 2 assistance, neon FIB etching can be enhanced for etching depth by a factor of ∼1.2. Whereas, helium FIB can also etch thick HSQ patterns, with much lower etch rates. But with XeF 2 assistance, helium FIB etching depth can be enhanced significantly by a factor of around 5. Furthermore, both helium and neon FIB etching methods have been employed to selectively remove residual particles in deep and narrow trenches without affecting the resist patterns. The chemical analysis of these residual particle composition and resist patterns can be also performed using helium ion microscopy coupled with secondary ion mass spectrometry (SIMS) using neon FIB. Besides, a neon FIB can also effectively etch PMMA patterns which are commonly used in nanofabrication and the unwanted connections can be etched away.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".