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Record W4391908605 · doi:10.1117/12.3009196

Enhancing temporal performance of a-Se detectors using a low-temperature hole-blocking bilayer design

2024· article· en· W4391908605 on OpenAlexaff
Sahar Adnani, Abdollah Pil-Ali, Karim S. Karim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicDigital Radiography and Breast Imaging
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBilayerMaterials scienceDetectorOptoelectronicsAmorphous solidDark currentOpticsPhotodetectorPhysicsChemistry

Abstract

fetched live from OpenAlex

Over the past few decades, amorphous selenium (a-Se) X-ray detectors have gained widespread use in mammography due to their remarkable spatial resolution capabilities. However, these devices encounter challenges in applications requiring lower radiation exposures and dynamic imaging, such as dynamic mammography tomosynthesis. The potential enhancement of sensitivity and temporal performance by increasing the applied voltage across the selenium layer is counteracted by concerns about dark current. Furthermore, certain applications necessitate the placement of a low-temperature hole-blocking layer on the top surface of a-Se to enable high-voltage mode in hole collecting mode. Although the use of SU-8 as a top layer has demonstrated satisfactory temporal performance, there remains room for improvement. Additionally, the use of SU-8 at the bottom has revealed interface compatibility issues. In this research, we address the compatibility challenges associated with the positioning of the SU-8 layer at the bottom by introducing a novel bilayer configuration. This bilayer setup was evaluated in both top and bottom positions. Our results indicate that when the bilayer configuration is located at the top, it combines the strengths of both layers, merging the high signal level in a Cs-doped a-Se device with the low dark current characteristics of the SU-8 layer. Notably, among the samples incorporating hole-blocking layers, the bilayer positioned at the top exhibits the most favorable lag performance, measuring below 0.5% after the 7th exposure, and a sensitivity reduction of 14% after 20 exposures (equivalent of 0.175 Gy). Moreover, employing this bilayer arrangement at the bottom enhances sensitivity by 16.1% compared to devices utilizing only the SU-8 hole-blocking layer. This improvement underscores the effective mitigation of interface challenges through the utilization of Cs-doped a-Se when SU-8 is placed at the bottom.

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 score0.001
Threshold uncertainty score0.003

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.023
GPT teacher head0.270
Teacher spread0.247 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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