Wavelength invariant Bi/In thermal resist as a Si anisotropic etch masking layer and direct-write photomask material
Bibliographic record
Abstract
Bilayer Bi/In thin film thermal resists are Bi and In films which form an etch resistant material at ~7 mJ/cm2 laser exposures with near wavelength invariance from visible to EUV. New simulations predicted that Bi/In film of 15/15nm absorbs substantially at 1 nm, which projects single pulse exposure sensitivity of ~16 mJ/cm2, hence suggesting good sensitivity to X-ray range. Thermal modeling has confirmed the exposure time/optical energy requirements for Bi/In. Exposed and developed Bi/In resist etches slower than silicon dioxide in alkaline-based silicon etchants TMAH, KOH, and EDP, making it a better masking layer for anisotropic Si etching. Also Bi/In has been used to create a direct-write photomask as its optical transmission changes from OD>2.9 before laser exposure to OD<0.26 after exposure. Both Bi/In anisotropic etching and direct write masks have been combined to successfully build test photocells with V-groove surface textures by using Bi/In masked silicon anisotropic etching and the other layers created using regular lithography but with Bi/In masks. These devices showed no operational differences from those created with regular resist processes. Investigation of resist interactions with Silicon after laser exposure and strip were done with Auger surface analysis which showed no detectable Bi or In contamination on substrates and no substrate sheet resistance change. X-ray diffraction and Rutherford back scattering tests suggest that the converted Bi/In may involve oxides.
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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".