Organic/inorganic hybrid optical upconversion devices for near‐infrared imaging
Bibliographic record
Abstract
Abstract Traditional near infrared (NIR) imaging is realized by a two dimensional InGaAs photodetector (PD) array integrated with a Si readout integrated circuit active matrix. The integration between the two different semiconductor arrays seriously restrains the device size‐scalability and leads to high manufacture cost. One alternative approach is to up‐convert infrared photons to a shorter wavelength (e.g., 1 µm or below) that can be effectively detected by a conventional Si detector. Herein, we report a highly simplified single‐mesa (pixel‐less) hybrid organic/inorganic up‐conversion imaging device through the integration of a large area inorganic PD with an organic light emitting diode, which can up‐convert a NIR scene to a visible‐light image. By combining the fabricated device with a commercially available camera, we demonstrate the first time pixel‐less up‐conversion NIR imaging with a spatial resolution of better than 6 µm. This device has great potential for making low‐cost, large‐area and high resolution NIR cameras (© 2012 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".