Bi/In thermal resist for both Si anisotropic wet etching and Si/SiO 2 plasma etching
Bibliographic record
Abstract
Bi/In thermal resist is a bilayer structure of Bi over In films which can be exposed by laser with a wide range of wavelengths and can be developed by diluted RCA2 solutions. Current research shows bimetallic resist can work as etch masking layer for both dry plasma etching and wet anisotropic etching. It can act as both patterning and masking layers for Si and SiO2 with plasma “dry” etch using CF4/CHF3. The etching condition is CF4 flow rate 50 sccm, pressure 150 mTorr, and RF power 100 - 600W. The profile of etched structures can be tuned by adding CHF3 and other gases such as Ar, and by changing the CF4/CHF3 ratio. Depending on the fluorocarbon plasma etching recipe the etch rate of laser exposed Bi/In can be as low as 0.1 nm/min, 500 times lower than organic photoresists. O2 plasma ashing has little etching effect on exposed Bi/In. Bi/In also creates etch masking layers for alkaline-based (KOH, TMAH and EDP) “wet” anisotropic bulk Si etch without the need of SiO2 masking steps. The laser exposed Bi/In etches two times more slowly than SiO2. Experiment result shows that single metal Indium film exhibits thermal resist characteristics but at twice the exposure levels. It can be developed in diluted RCA2 solution and used as an etch mask layer for Si anisotropic etch. X-ray diffraction analysis shows that laser exposure causes both Bi and In single film to oxidize. In film may become amorphous when exposed to high laser power.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".