<title>Prototype laser-activated bimetallic thermal resist for microfabrication</title>
Bibliographic record
Abstract
The Thermal Resist Enhanced Optical Lithography (TREOL) process models an optical system to double device resolution by exploiting non-reciprocal laser activated processes. A possible prototype thermal resist consists of stacked bismuth on indium layers sputter deposited on a glass/quartz substrate with thickness ratios matching the eutectic alloy (Bi 53%). Laser radiation locally melts the metals which alloy upon cooling. BiIn resist is relatively wavelength insensitive because its UV optical characteristics vary modestly. Reflection and energy absorption/cc calculations indicate the best arrangement is a 30-45-nm total thickness bilayer with bismuth on indium. Exposing the highly absorbing BiIn with CW argon (514/488 nm) or 4-ns Nd:YAG pulses at 533 nm (40 mJ/cm2 for 300-nm thick) and 266 nm transforms the resist to a weakly absorbing alloy with a visually identifiable pattern. 30-nm thick converted film transmission changes from 1.0OD to 0.35OD (830-350 nm) until a 350-nm absorption edge. Profilometry and SEM showed no signs of ablation or oxide growth in exposed areas. The resist was developed with HNO3:CH3COOH:H2O etch, preferentially removing unexposed areas, leaving written patterns of alloyed lines seen both in profilometry and SEM images. Thus BiIn forms a complete thermal alloying resist with selectively etched exposed patterns that can be stripped in an HCl:H2O2:H2O bath.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".