Calibrating grayscale direct write bimetallic photomasks to create 3D photoresist structures
Bibliographic record
Abstract
Bimetallic thin films were previously shown to create laser direct write binary and analog gray scale photomasks. DC-sputtered Sn/In (5at.% Sn, 80 nm) oxidize under laser exposure, modifying the optical density at 365 nm from >3OD to <0.22OD. Bimetallic Sn/In thin film grayscale photomasks have been successfully used to create concave and convex 3D structures using mask aligners with Shipley photoresists. To produce precise 3D structures in the organic photoresists, every mask making step was studied. Compensations during the mask making process were necessary because that the relationship between the optical density of the exposed bimetallic films and the laser writing power is not accurately linear, and also that the response of the photoresists is not linear to the exposure. V-grooves with straight slope profile were produced with calibrations taken into account. X-ray diffraction analysis indicates that structure of laser exposed Sn/In bimetallic films is similar to that of ITO films, suggesting new directions for improvement of bimetallic film optical properties, and that the theoretical maximum transmission should approach pure ITO’s ~0.05OD in the visible wavelength.
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 | 0.001 |
| 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".