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Record W3037867998 · doi:10.1088/0256-307x/37/6/068503

A Novel Oxygen-Based Digital Etching Technique for p-GaN/AlGaN Structures without Etch-Stop Layers*

2020· article· en· W3037867998 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChinese Physics Letters · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceEtching (microfabrication)Dry etchingOptoelectronicsEpitaxyInductively coupled plasmaPlasmaLayer (electronics)Plasma etchingEtch pit densityReactive-ion etchingSurface roughnessSurface finishAnalytical Chemistry (journal)NanotechnologyComposite materialChemistry

Abstract

fetched live from OpenAlex

A novel O 2 plasma-based digital etching technology for p-GaN/AlGaN structures without any etch-stop layer was investigated using an inductively coupled plasma (ICP) etcher, with 100 W ICP power and 40 W rf bias power. Under 40 sccm O 2 flow and 3 min oxidation time, the p-GaN etch depth was 3.62 nm per circle. The surface roughness improved from 0.499 to 0.452 nm after digital etching, meaning that no observable damages were caused by this process. Compared to the dry etch only methods with Cl 2 /Ar/O 2 or BCl 3 /SF 6 plasma, this technique smoothed the surface and could efficiently control the etch depth due to its self-limiting characteristic. Furthermore, compared to other digital etching processes with an etch-stop layer, this approach was performed using ICP etcher and less demanding on the epitaxial growth. It was proved to be effective in precisely controlling p-GaN etch depth and surface damages required for high performance p-GaN gate high electron mobility transistors.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.256
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it