Ultraviolet photoenhanced wet etching of GaN in K2S2O8 solution
Bibliographic record
Abstract
The mechanism of the UV photoenhanced wet etching of GaN is determined. The UV photoenhanced wet etching does not require an electrical contact to be made to the sample, and nitrides deposited on insulating substrates (such as sapphire) can be etched, unlike photoelectrochemical (PEC) wet etching. The present technique relies on adding an appropriate oxidizing agent, in this case, peroxydisulfate (S2O82−), to KOH solutions. In a similar mechanism to PEC wet etching, the regions of low defect density are preferentially etched, leaving regions of high electron recombination such as threading dislocations relatively intact. The threading dislocations may be physically broken off, either by stirring or by a postetch sonication of the sample in KOH solution. Smoothly etched surfaces can be obtained under the proper conditions. A noble metal mask acts in a catalytic manner, yielding etch rates approximately one order of magnitude greater than those observed using inert masks. The essential role of the free radicals, originating from the peroxydisulfate ion, in the etching reaction is confirmed. The etching reaction is more rapid for more heavily n-type doped samples, and insulating C-doped layers act as an etch stop layer.
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".