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Record W2974218128 · doi:10.1116/1.5115427

Oxygen-based digital etching of AlGaN/GaN structures with AlN as etch-stop layers

2019· article· en· W2974218128 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

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsUniversity of British Columbia
FundersGuangdong Science and Technology Department
KeywordsMaterials scienceEtching (microfabrication)EpitaxyLayer (electronics)Etch pit densityOptoelectronicsInductively coupled plasmaDry etchingSurface roughnessSurface finishSiliconPlasmaReactive-ion etchingComposite material

Abstract

fetched live from OpenAlex

O2 plasma-based digital etching of Al0.25Ga0.75N with a 0.8 nm AlN spacer on GaN was investigated using an inductively coupled plasma etcher. Silicon oxide layer was used as the hard mask. At 40 W RF bias power and 40 sccm oxygen flow, the etch depth of Al0.25Ga0.75N was 5.7 nm per cycle. The 0.8 nm AlN spacer layer acted as an etch-stop layer in three cycles. The surface roughness improved from 0.66 to 0.33 nm after the three and seven digital etch cycles. Compared to the dry etch only approach, this technique smoothed the surface instead of causing surface roughening. Compared to the selective thermal oxidation with a wet etch approach, this method is less demanding on the epitaxial growth and saves the oxidation process. It was shown to be effective in precisely controlling the AlGaN etch depth required for recessed-AlGaN HEMTs.

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 categoriesnone
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 score0.115
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.005
GPT teacher head0.231
Teacher spread0.225 · 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