Active pixel image sensor array for dual vision using large‐area bilayer <scp>WS<sub>2</sub></scp>
Bibliographic record
Abstract
Abstract Transition metal dichalcogenides (TMDs) are a promising candidate for developing advanced sensors, particularly for day and night vision systems in vehicles, drones, and security surveillance. While traditional systems rely on separate sensors for different lighting conditions, TMDs can absorb light across a broad‐spectrum range. In this study, a dual vision active pixel image sensor array based on bilayer WS2 phototransistors was implemented. The bilayer WS2 film was synthesized using a combined process of radio‐frequency sputtering and chemical vapor deposition. The WS2‐based thin‐film transistors (TFTs) exhibit high average mobility, excellent Ion/Ioff, and uniform electrical properties. The optoelectronic properties of the TFTs array exhibited consistent behavior and can detect visible to near‐infrared light with the highest responsivity of 1821 A W−1 (at a wavelength of 405 nm) owing to the photogating effect. Finally, red, green, blue, and near‐infrared image sensing capabilities of active pixel image sensor array utilizing light stencil projection were demonstrated. The proposed image sensor array utilizing WS2 phototransistors has the potential to revolutionize the field of vision sensing, which could lead to a range of new opportunities in various applications, including night vision, pedestrian detection, various surveillance, and security systems. image
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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".