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Record W3213363402 · doi:10.1063/5.0062824

Sub-50 cm/s surface recombination velocity in InGaAsP/InP ridges

2021· article· en· W3213363402 on OpenAlexaboutno aff
Nicolas M. Andrade, Sean Hooten, Yunjo Kim, Jeehwan Kim, Eli Yablonovitch, Ming C. Wu

Bibliographic record

VenueApplied Physics Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsnot available
FundersDivision of Electrical, Communications and Cyber SystemsBasic Energy SciencesTeknologi og Produktion, Det Frie ForskningsrådU.S. Department of EnergyNational Science Foundation
KeywordsPassivationOptoelectronicsMaterials scienceNanophotonicsAnnealing (glass)Gallium arsenideAtomic layer depositionPhotoluminescenceDopingPhotonicsIndium gallium arsenideSpontaneous emissionIndium phosphideOpticsLayer (electronics)NanotechnologyLaserPhysics

Abstract

fetched live from OpenAlex

The III–V InP/InGaAsP/InGaAs material family is important for photonic devices due to its optical emission and absorption in the 1.55 and 1.3 μm telecommunication bands for optical interconnects. However, InGaAsP/InGaAs generally suffer from relatively high surface recombination velocity—compared to Si [Das et al., in 2020 47th IEEE Photovoltaic Specialists Conference (PVSC) (IEEE, Calgary, AB, 2020), pp. 1167–1170] and InP [Joyce et al., Nano Lett. 12, 5325–5330 (2012)], which reduces the efficiency and can increase the noise in nanophotonic devices. Here, we demonstrate an efficient method to passivate the surface using a combination of sulfur-saturated ammonium sulfide and atomic layer deposition. After annealing, the surface passivation led to a surface recombination velocity as low as 45 cm/s, corresponding to a >180× increase in the photoluminesence of a nanoscale light-emitting device with 200 nm width.

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 score0.002
Threshold uncertainty score0.005

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 science0.0010.000
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 head0.199
Teacher spread0.190 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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Same venueApplied Physics LettersSame topicPhotonic and Optical DevicesFrench-language works237,207