Creating precise 3D microstructures using laser direct-write bimetallic thermal resist grayscale photomasks
Bibliographic record
Abstract
Previous research demonstrated Sn/In and Bi/In bimetallic thermal resists are promising new materials for direct-write analogue grayscale photomask processes. These materials turn transparent with increased laser exposure power and their optical density changes smoothly from 3 OD when unexposed to less than 0.22 OD when fully exposed. The transparency is the result of an oxidation process that is controllable with exposure to generate the grayscale levels in the photomask. In order to produce precise 3D structures in regular photoresists, the steps involved in microlithography must be quantified and examined. The lithographic process includes drawing 8-bit grayscale bitmap patterns, computer-aided laser writing photomasks on bimetallic films, and regular photoresist exposure using a mask-aligner. Compensation during the mask-writing process was necessary since the relationship between the optical density of the exposed bimetallic films and the laser writing power was not completely linear. In addition, the response of the photoresists to the mask exposure time was also a non-linear relationship. To investigate the resolution limit for Bi/In and Sn/In bimetallic thermal resists as a masking material, we used a modified form of interference lithography to expose and develop structures in Bi/In resists with widths that are less than 200 nm. As a result of the lithography, we were able to create structures in the Bi/In films that are up to 20 times smaller than previously obtained using the direct-write method.
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 | 0.000 |
| 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".