A Wafer‐Scale Nanoporous 2D Active Pixel Image Sensor Matrix with High Uniformity, High Sensitivity, and Rapid Switching
Bibliographic record
Abstract
Abstract 2D transition‐metal dichalcogenides (TMDs) have been successfully developed as novel ubiquitous optoelectronics owing to their excellent electrical and optical characteristics. However, active‐matrix image sensors based on TMDs have limitations owing to the difficulty of fabricating large‐area integrated circuitry and achieving high optical sensitivity. Herein, a large‐area uniform, highly sensitive, and robust image sensor matrix with active pixels consisting of nanoporous molybdenum disulfide (MoS 2 ) phototransistors and indium–gallium–zinc oxide (IGZO) switching transistors is reported. Large‐area uniform 4‐inch wafer‐scale bilayer MoS 2 films are synthesized by radio‐frequency (RF) magnetron sputtering and sulfurization processes and patterned to be a nanoporous structure consisting of an array of periodic nanopores on the MoS 2 surface via block copolymer lithography. Edge exposure on the nanoporous bilayer MoS 2 induces the formation of subgap states, which promotes a photogating effect to obtain an exceptionally high photoresponsivity of 5.2 × 10 4 A W −1 . A 4‐inch‐wafer‐scale image mapping is successively achieved using this active‐matrix image sensor by controlling the device sensing and switching states. The high‐performance active‐matrix image sensor is state‐of‐the‐art in 2D material‐based integrated circuitry and pixel image sensor applications.
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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".