Influence of tetramethylammonium hydroxide on niobium nitride thin films
Bibliographic record
Abstract
Functionality of superconducting thin-film devices such as superconducting nanowire single photon detectors stems from the geometric effects that take place at the nanoscale. The engineering of these technologies requires high-resolution patterning, often achieved with electron beam lithography. Common lithography processes using hydrogen silsesquioxane (HSQ) as the electron beam resist rely on tetramethylammonium hydroxide (TMAH) as both a developer and a resist adhesion promoter. Despite the strong role played by TMAH in the fabrication of superconducting devices, its potential influence on the superconducting films themselves has not yet been reported. In this work, the authors demonstrate that a 25% TMAH developer damages niobium nitride (NbN) thin films by modifying the surface chemistry and creating an etch contaminant that slows reactive ion etching in CF4. They also show how the identity of the contaminant may be revealed through characterization including measurement of the superconducting film properties and Fourier transform infrared spectroscopy. Although workarounds may be available, the results reveal that processes using 25% TMAH as an adhesion promoter are not preferred for NbN films and that changes to the typical HSQ fabrication procedure will need to be made in order to prevent damage of NbN nanoscale devices.
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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 | 0.000 | 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".