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Record W2080005809 · doi:10.1109/led.2013.2295976

Low Dark Current Amorphous Silicon Metal-Semiconductor-Metal Photodetector for Digital Imaging Applications

2014· article· en· W2080005809 on OpenAlexaff
Sina Ghanbarzadeh, Shiva Abbaszadeh, Karim S. Karim

Bibliographic record

VenueIEEE Electron Device Letters · 2014
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPhotodetectorDark currentPhotodiodeOptoelectronicsMaterials scienceSiliconAmorphous siliconOrganic semiconductorQuantum efficiencySemiconductorCrystalline silicon

Abstract

fetched live from OpenAlex

A novel lateral amorphous silicon (a-Si) metal-semiconductor-metal photodetector architecture is proposed using an organic blocking layer. Fabricated devices exhibit low dark-current, high dynamic range, and a measured external quantum efficiency of 65%, which represents a considerable improvement over previously reported designs. The higher performance is enabled by the introduction of a thin organic blocking layer and subsequently operating at high electric-fields. Unlike industry standard p-i-n photodiodes, our high performance lateral photosensor does not require doped p+/n+layers. Thus, the reported device is compatible with current and previous generation a-Si thin film transistor display fabrication process making it promising for low-cost optical touch panel or diagnostic medical imaging applications.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.007
GPT teacher head0.215
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations29
Published2014
Admission routes1
Has abstractyes

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