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Radiative defect state identification in semi-insulating GaAs using photo-carrier Radiometry

2009· article· en· W2072323881 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSemiconductor Science and Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicThermography and Photoacoustic Techniques
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPhotoluminescenceRadiometryDiffusionRadiative transferSpontaneous emissionMaterials scienceOptoelectronicsQuantum efficiencyOpticsCarrier lifetimeChemistryAnalytical Chemistry (journal)PhysicsSiliconLaser

Abstract

fetched live from OpenAlex

The photo-carrier radiometry (PCR) technique has been applied to a semi-insulating GaAs wafer for the detection and identification of radiative defects. Due to the ultrafast free carrier recombination lifetime, the conventional carrier-diffusion-wave-based PCR theory was modified to reflect the signal domination by trap emission and capture rates in the absence of diffusion. Defect photoluminescence with photon energies from 0.7 to 1.24 eV was collected and analyzed using photo-thermal temperature spectra and resonant (rate-window) detection combined with frequency scans. Five defect levels were identified self-consistently from the combined rate-window and PCR phase data, and the temperature dependence of the defect photoluminescence quantum efficiency was determined through multi-parameter best fits of the PCR rate theory to the experimental data.

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.

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 score0.040
Threshold uncertainty score0.595

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
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 head0.010
GPT teacher head0.245
Teacher spread0.236 · 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