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Record W2072969029 · doi:10.1063/1.1846143

Blue-violet InGaN laser diodes grown on bulk GaN substrates by plasma-assisted molecular-beam epitaxy

2004· article· en· W2072969029 on OpenAlexaff
C. Skierbiszewski, Z. R. Wasilewski, M. Siekacz, A. Feduniewicz, P. Perlin, P. Wiśniewski, J. Borysiuk, I. Grzegory, M. Leszczyński, T. Suski, S. Porowski

Bibliographic record

VenueApplied Physics Letters · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsInstitute for Microstructural Sciences
Fundersnot available
KeywordsMolecular beam epitaxyMaterials scienceOptoelectronicsDiodeLaserEpitaxyPlasmaOpticsLayer (electronics)Nanotechnology

Abstract

fetched live from OpenAlex

We report on the InGaN multiquantum laser diodes (LDs) made by rf plasma-assisted molecular beam epitaxy (PAMBE). The laser operation at 408nm is demonstrated at room temperature with pulsed current injections using 50ns pulses at 0.25% duty cycle. The threshold current density and voltage for the LDs with cleaved uncoated mirrors are 12kA∕cm2 (900mA) and 9V, respectively. High output power of 0.83W is obtained during pulse operation at 3.6A and 9.6V bias with the slope efficiency of 0.35W∕A. The laser structures are deposited on the high-pressure-grown low dislocation bulk GaN substrates taking full advantage of the adlayer enhanced lateral diffusion channel for adatoms below the dynamic metallic cover. Our devices compare very favorably to the early laser diodes fabricated using the metalorganic vapor phase epitaxy technique, providing evidence that the relatively low growth temperatures used in this process pose no intrinsic limitations on the quality of the blue optoelectronic components that can be fabricated using PAMBE.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.212
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), 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

Citations70
Published2004
Admission routes1
Has abstractyes

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