Bibliographic record
Abstract
The development of deep-ultraviolet (DUV) photodetectors has gained significant attention due to their broad applications. With the increasing demand for high-performance detectors that can operate in harsh environments, research on optimizing their materials and structural design has become a critical focus. This paper introduces the basic working principle, structural composition, and performance evaluation criteria of deep-ultraviolet (DUV) photodetectors, focusing on the analysis of the optimization of materials and structure to improve the performance of detectors. Deep ultraviolet photodetectors are based on the photovoltaic effect of semiconductor materials to realize the conversion of photoelectric signals. It is shown that the photo responsiveness and stability of the photodetector can be effectively improved by introducing a composite film of the rare earth element cerium tungstate (Ce-WO3). In addition, the structural optimization of graphene-β-Ga2O3 heterojunction and n-Ga2O3/p-GaN heterojunction is employed to significantly improve the spectral selectivity, responsivity and long-time stability of the detector. This paper also explores the potential of these improvements for applications in the fields of UV communications, UV optoelectronic integrated circuits, environmental monitoring and military spaceflight.
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 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".