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Record W1982281303 · doi:10.1117/12.475401

Resonant cavity enhanced GaAs/AlGaAs IR detectors

2003· article· lv· W1982281303 on OpenAlexaff
Dmitrii G. Esaev, S. G. Matsik, M. B. M. Rinzan, A. G. U. Perera, Hui C. Liu, Z. R. Wasilewski, M. Buchanan

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2003
Typearticle
Languagelv
FieldEngineering
TopicAdvanced Semiconductor Detectors and Materials
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsAbsorption (acoustics)OptoelectronicsMaterials scienceDetectorResonance (particle physics)Common emitterOpticsWavelengthGallium arsenideHeterojunctionReflection (computer programming)PhysicsAtomic physics

Abstract

fetched live from OpenAlex

HEterojunction Interfacial Workfunction Internal Photoemission (HEIWIP) detectors have been demonstrated with cutoff wavelengths λc up to 92 μm. One method of increasing the response in a desired range is to employ the cavity effect to create resonant maxima. Results are reported here confirming the presence of cavity enhancements in both the absorption and the detector response of HEIWIP structures at the 3λ/4 resonance. The detectors consisted of 13, 19 and 30 Be doped GaAs emitter and undoped Al0.02Ga0.98As barrier layers. Transmission and reflection spectra for multilayer GaAs/AlGaAs IR detectors in the range 2000-100 cm-1 at room temperature are presented. Comparisons with the calculated results based on free carrier absorption and interaction with optical phonons model are reported. It is shown that the absorption can be maximized by using the resonant cavity effect. The use of the resonant cavity effect should allow the design of detectors with increased response in the desired wavelength ranges.

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 communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.229
Teacher spread0.217 · 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

Citations1
Published2003
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdvanced Semiconductor Detectors and MaterialsFrench-language works237,207