MétaCan
Menu
Back to cohort
Record W4364376271 · doi:10.1116/6.0002494

Effect of substrate rotation speed on AlGaN nanowire deep ultraviolet light-emitting diodes by molecular beam epitaxy

2023· article· en· W4364376271 on OpenAlexafffund
Mohammad Fazel Vafadar, Rezoana Bente Arif, Qihua Zhang, Songrui Zhao

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceMolecular beam epitaxyNanowireOptoelectronicsSubstrate (aquarium)Light-emitting diodeDiodeUltravioletGallium nitrideRotation (mathematics)SemiconductorEpitaxyNanotechnologyLayer (electronics)

Abstract

fetched live from OpenAlex

Aluminum gallium nitride (AlGaN) nanowires by molecular beam epitaxy (MBE) have become an emerging platform for semiconductor deep ultraviolet (UV) light-emitting diodes (LEDs). Despite of the progress, much less attention has been paid to the effect of substrate rotation speed on the device performance. Herein, we investigate the effect of the substrate rotation speed on the nanowire height and diameter uniformity, as well as the electrical and optical performance of MBE-grown AlGaN nanowire deep UV LED structures with low and high substrate rotation speeds. It is found that by increasing the substrate rotation speed from 4 revolutions per minute (rpm) to 15 rpm, the statistical variation of the nanowire height and diameter is reduced significantly. Increasing the substrate rotation speed also improves the device electrical performance, with a factor of 4 reduction on the device series resistance. This improved electrical performance further transfers to the improved optical performance. The underlying mechanisms for these improvements are also discussed.

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)0.003
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 score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.007
GPT teacher head0.235
Teacher spread0.229 · 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.

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

Citations2
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and PhenomenaSame topicGaN-based semiconductor devices and materialsFrench-language works237,207