Metal and organic nanostructure fabrication by electron beam lithography and dry liftoff
Bibliographic record
Abstract
Liftoff and direct etch are the two most popular pattern transfer methods used for nanofabrication. The latter is limited by the etching rate selectivity between the resist and the substrate material, which is usually on the order of unity for dry plasma etching. Moreover, some metals including most noble metals cannot be dry etched. Thus liftoff is often the preferred pattern transfer method. Liftoff is typically carried out using a solvent that dissolves the resist. A strong solvent aided by ultrasonic agitation and/or heating is sometimes needed if the resist is difficult to dissolve due to, for example, cross-linking by exposure to electron beam or plasma. Another serious issue with conventional liftoff process is that the metal debris may stay at the active device area after drying. To avoid the above issues, dry liftoff using mechanical approach may be utilized. Here we report a simple dry liftoff technique using a scotch tape to peel off the resist film coated with the material to lift off. We obtained high resolution (down to 50 nm) liftoff of metal and organic materials polystyrene and Alq3, with PMMA as electron beam resist coated on a substrate treated with a low energy surfactant.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".