Mitigating Re-Entrant Etch Profile Undercut in Au Etch with an Aqua Regia Variant
Bibliographic record
Abstract
We investigate the re-entrant undercut profile resulting from Au wet etching for patterning micron range thick films using an aqua regia-based solution in comparison with an iodine-iodide-based commercial etchant. Our work discriminates between two undercutting mechanisms: galvanic acceleration of etch rate at the Au adhesion or barrier layer, and delamination-based undercutting. We tracked etch outcomes of feature size reduction from photoresist size, undercut Au in cross-section and lift-off of small (5–10 μ m) features. Results indicate that galvanic undercutting is well-mitigated by the aqua regia solution compared to commercial etchant results. Good Au adhesion eliminates undercut for 500 nm-thick Au and mitigates it by ∼80% for 1 μ m-thick Au. We discuss the electrochemical origin of this mitigated galvanic undercut.
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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".