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Record W1958452064 · doi:10.1002/pssc.201200427

Organic/inorganic hybrid optical upconversion devices for near‐infrared imaging

2012· article· en· W1958452064 on OpenAlexafffund
Dayan Ban, Jun Chen, Jianchen Tao, Michael G. Helander, Zhibin Wang, Jacky Qiu, Zheng‐Hong Lu

Bibliographic record

VenuePhysica status solidi. C, Conferences and critical reviews/Physica status solidi. C, Current topics in solid state physics · 2012
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of TorontoUniversity of Waterloo
FundersCMC Microsystems
KeywordsPhoton upconversionOptoelectronicsNear-infrared spectroscopyMaterials sciencePhotodetectorPixelInfraredDiodeImage resolutionDetectorImage sensorOpticsDopingPhysics

Abstract

fetched live from OpenAlex

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)

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.794
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.313
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2012
Admission routes2
Has abstractyes

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