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Record W2046123741 · doi:10.1109/jsen.2012.2234450

Characterization of Low Dark-Current Lateral Amorphous-Selenium Metal-Semiconductor-Metal Photodetectors

2013· article· en· W2046123741 on OpenAlexaff
Shiva Abbaszadeh, Nicholas Allec, KarimS. Karim

Bibliographic record

VenueIEEE Sensors Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDark currentMaterials sciencePhotodetectorOptoelectronicsPhotocurrentElectrodePolyimideContact resistanceCurrent densityOpticsLayer (electronics)NanotechnologyChemistryPhysics

Abstract

fetched live from OpenAlex

We report a lateral amorphous-selenium (a-Se) metal-semiconductor-metal photodetector with a blocking contact. The blocking contact, a polyimide layer, is shown to significantly reduce the dark current even at high applied biases that result in high photo-to-dark-current ratios, thus leading to wide dynamic range and high signal-to-noise ratio. The use of the polyimide blocking contact prevents the injection of both holes and electrons and improves considerably upon the dark current of previously reported lateral a-Se detectors. The presence of charge trapping at the polyimide/a-Se interface is found to be negligible through the use of pulsed light experiments. The effects of electrode spacing and electrode width on device performance are investigated through experiment and simulation for device optimization. From the devices that are fabricated, it is found that the dark current is strongly dependent on the comb fingers density while the same trend is not observed for the photocurrent. It is found that the device with 10- μm electrode spacing and 10-μm electrode width has the best performance in terms of photo and dark current. This paper demonstrates the promise of low-cost lateral a-Se devices for use in indirect conversion large area digital medical X-ray imaging applications, such as chest radiography, real-time fluoroscopy, and cone beam computed tomography.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.010
GPT teacher head0.200
Teacher spread0.189 · 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 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

Citations14
Published2013
Admission routes1
Has abstractyes

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