Creating direct-write gray-scale photomasks with bimetallic thin film thermal resists
Bibliographic record
Abstract
New types of analog gray-scale laser direct-write masks have been created using bimetallic thermal resists and a direct- write laser process. Bimetallic resists consist of two layers of thin films, eg. Bi over In or Sn over In, which react to form a low temperature alloy when a laser raises the films above the eutectic temperature. Depending on the exposure energy, resulting alloyed layers appear to become oxides, causing a change of absorption at 365nm from >3OD to <0.3OD. The thermal resists show near wavelength invariance from IR to UV. The Sn/In films, each layer ~40 nm thick, were DC-sputtered onto glass slides or quartz substrates. To make gray-scale photomasks the samples were placed on a computer-controlled high accuracy X-Y table. A bitmap gray-scale pattern was raster-scanned with a CW Argon laser (514 nm) beam. An optical shutter controlled the actual laser power applied onto the thermal resist film according to the gray-scale value. When exposed to a laser beam greater than 0.6 W, the Sn/In film became nearly transparent (0.22OD) at I-line (365nm) wavelength. Sn/In and Bi/In photomasks have been used together with a standard mask aligner to successfully pattern Shipley SPR2FX-1.3 photoresist. CF<sub>4</sub>/O<sub>2</sub> plasma etching has been used to transfer the three-dimensional pattern to SiO<sub>2</sub> and Si substrates. Also a 160 beam laser diode thermal imaging tool was used to create BiIn direct-write binary masks.
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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 | 0.000 | 0.000 |
| 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".