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Study of gain, noise, and collection efficiency of GaAs SAM-APDs single pixel

2022· article· en· W4313418356 on OpenAlexaff
Matija Colja, Marco Cautero, R.H. Menk, Pierpaolo Palestri, Alessandra Gianoncelli, M. Antonelli, G. Biasiol, Simone Dal Zilio, T. Steinhartova, C. Nichetti, F. Arfelli, Dario De Angelis, F. Driussi, Valentina Bonanni, A. Pilotto, Gianluca Gariani, Sergio Carrato, G. Cautero

Bibliographic record

VenueJournal of Instrumentation · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAPDSOptoelectronicsAvalanche photodiodeMaterials scienceAbsorption (acoustics)SemiconductorDiodeGallium arsenideMolecular beam epitaxyNoise (video)PhotodetectorSiliconOpticsDetectorEpitaxyNanotechnologyPhysicsComputer science

Abstract

fetched live from OpenAlex

Abstract III-V-compound semiconductors offer many advantages over silicon-based technologies traditionally used in solid-state photodetectors, especially in hard X-ray applications that require high detection efficiency and short response times. Amongst them, gallium arsenide (GaAs) has very promising characteristics in terms of X-ray absorption and high carrier velocity. Furthermore, implementing charge-multiplication mechanisms within the sensor may become of critical importance in cases where the photogenerated signal needs an intrinsic amplification before being acquired by the front-end electronics. This work reports on the experimental characterization by means of lasers and synchrotron radiation of gain, noise, and charge collection efficiencies of GaAs avalanche photodiodes (APDs), realized by molecular beam epitaxy (MBE), featuring separate absorption and multiplication regions (SAM) and different absorption region thicknesses. These devices have been fabricated to investigate the role of the thickness of the absorption region and of possible traps or defects at the metal-semiconductor interfaces in the collection efficiency in order to lay the groundwork for the future development of thicker GaAs devices for detection of hard X-rays.

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 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 score0.266
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

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 science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.237
Teacher spread0.226 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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