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Polarization charge properties of low-temperature atomic layer deposition of AlN on GaN

2014· article· en· W2004547698 on OpenAlexaff
Kevin Voon, Kyle M. Bothe, Pouyan Motamedi, Ken Cadien, Douglas W. Barlage

Bibliographic record

VenueJournal of Physics D Applied Physics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAtomic layer depositionMaterials scienceOptoelectronicsLayer (electronics)Deposition (geology)Charge (physics)NanotechnologyPhysicsGeology

Abstract

fetched live from OpenAlex

Low-temperature (<250 °C) plasma-enhanced atomic layer deposition (PEALD) is used to fabricate aluminum nitride (AlN)/gallium nitride (GaN) heterojunctions for tunnelling contacts in devices where selective contacts are required, such as GaN metal–oxide semiconductor field-effect transistors. AlN is grown on GaN templates with via low-temperature plasma-enhanced ALD, and compared in order to extract their surface two-dimensional electron gas concentration. A peak electron density of >2 × 1013 cm−2 was observed for an approximately 4.5 nm AlN thickness on GaN wafers with a higher initial doping concentration while for the lightly doped GaN samples, a peak carrier concentration of 1.9 × 1013 cm−2 was observed for a thickness of 5.7 nm AlN. Polarization is strongest near the AlN critical thickness of strain relaxation for low-temperature deposition methods. This suggests that greater initial dopant concentrations are more conducive to enhanced polarization characteristics under these low growth temperatures because the critical thickness is realized at lower thicknesses. This approach also yields a low contact resistance of 0.45 Ω mm with Al contacts.

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 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 score0.049
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.213
Teacher spread0.202 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2014
Admission routes1
Has abstractyes

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