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Record W2939695839 · doi:10.1116/1.5088967

Fabrication of grating coupled GaAs/AlGaAs quantum well infrared photodetector on an Si substrate

2019· article· en· W2939695839 on OpenAlexafffund
Seung-Yeop Ahn, Z. R. Wasilewski

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsUniversity of Waterloo
FundersWaterloo Institute for Nanotechnology, University of WaterlooNatural Sciences and Engineering Research Council of CanadaKorea Institute of Science and Technology
KeywordsQuantum well infrared photodetectorMaterials scienceGratingOptoelectronicsPhotocurrentQuantum wellOpticsDark currentDiffraction gratingPhotodetectorPhysicsLaser

Abstract

fetched live from OpenAlex

The grating coupled GaAs/AlGaAs quantum well infrared photodetectors (QWIPs) are integrated onto Si substrates using metal wafer bonding and epitaxial lift-off process. The 1 μm depth of hexagonal hole structure of grating was formed. The energy-dispersive x-ray spectroscopy results confirmed that the grating coupled QWIP is successfully mounted on an Si substrate. By evaluating the Raman spectra, PL, and surface roughness of bonded QWIP samples, the authors found that the grating does not induce any change in the optical or structural characteristics of actual QWIP layers. The dark current–voltage characteristics show a nearly identical dark current level between grating coupled QWIP and nongrating QWIP. The photocurrent spectrum shows that the peak photocurrent intensity of grating coupled QWIP is about 16 times higher than that of nongrating QWIP. This indicates that the grating effectively contributes to an increase in the light absorption of QWIP, showing large room for improvement of QWIP performance by further optimization of a grating structure.

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)0.001
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 score0.028
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.011
GPT teacher head0.238
Teacher spread0.226 · 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

Citations9
Published2019
Admission routes2
Has abstractyes

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