Laser engineering of ITO/ZnO/ITO structures for photodetector applications
Bibliographic record
Abstract
ZnO nanomaterials have received much attention due to their suitability for applications such as gas sensors, UV detectors, and solar collectors. However, the functionality of ZnO in optical applications is often limited by its wide bandgap (3.15 eV) which restricts the response to shorter wavelengths. In view of this limitation, there has been much interest in tuning the optical properties of ZnO through defect engineering. In this work, we show that processing ZnO thin films with nanosecond (ns) laser irradiation is a simple and effective way to introduce interband defects lowering the bandgap and increasing the sensitivity of ITO/ZnO/ITO photodetector structures at longer wavelengths. In particular, we show that the concentration of oxygen vacancies in ZnO is proportional to laser fluence below 700 mJ/cm2, but that an increase in laser fluence above this value results in thermal heating that anneals the film and lowers the relative abundance of oxygen vacancies. On the other hand, the surface morphology of ZnO does not change significantly even though the fluence reaches 700 mJ/cm2. To illustrate the way in which laser processing can be utilized to improve the optical properties of ZnO films in photodetector applications, we have fabricated transparent ITO/ZnO/ITO stacked structures and measured their response at various optical wavelengths. We find that processing with ns laser radiation is effective in enhancing the responsivity and detectivity of these devices at blue (460 nm) and UV (390 nm) wavelengths. The response of the photodetector is also increased at green wavelengths (570 nm) and red wavelengths (620 nm) when processed with laser fluences in the 480–600 mJ/cm2 range.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".