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Record W2355610118 · doi:10.5539/apr.v8n3p96

The Fabrication of the Infrared CdSe Doped With Cu Photodetector

2016· article· en· W2355610118 on OpenAlexvenueno aff
Hassan H. Mohammed, S. K. J. Al‐Ani

Bibliographic record

VenueApplied Physics Research · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Semiconductor Detectors and Materials
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceDopingOptoelectronicsPhotodetectorAnnealing (glass)InfraredQuantum dotFabricationDetectorOpticsPhysics

Abstract

fetched live from OpenAlex

In this work, the implementation method of the CdSe doped with Cu (CdSe: Cu) photodetector is presented. This detector is prepared by vacuum evaporation of CdSe films on glass substrate followed by vacuum annealing under an argon atmosphere for doping with copper. This detector is found, for the first time, to cover a wide range of the infrared besides the visible region of the electromagnetic spectrum. This finding of the wavelength tuning is due to the localized energy states of copper atoms inside the band gap of the CdSe. This tuning is compared with recent work in the corresponding colloidal CdSe-ZnS core shell quantum dots and with the quantum well (QWIR) and quantum dots (QDIR) infrared detectors. The major significance of this developed detector is in its synthesis simplicity and its fabrication processes costs in comparison with that of the (QWIR) and (QDIR) detectors. The structural analysis results demonstrated that the vacuum annealing in competition with the doping concentration improves significantly the film structure. Better crystalline structure was reported at 5 wt% of Cu concentration and at annealing temperature of 350 ºC. Besides the measured detectivity at room temperature is D*=2.31×108 cm Hz1/2W-1. This value approaches the detectivity of the state of art mercury cadmium telluride (MCT). This result paves the way for further investigations and improvements.

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)0.000
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 score0.016
Threshold uncertainty score0.156

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.031
GPT teacher head0.280
Teacher spread0.250 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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