The Fabrication of the Infrared CdSe Doped With Cu Photodetector
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".