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Characterisation Stress Tests of Low Gain Avalanche Diodes

2022· article· en· W4391249923 on OpenAlexaff
P. Azzarello, G. Barone, D. Boye, W. Chen, G. D’amen, J. Roloff, G. Giacomini, X. Wu, Ping Xie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsInstitute of Particle Physics
FundersBrookhaven National LaboratoryU.S. Department of Energy
KeywordsDiodeOptoelectronicsAvalanche diodeResistive touchscreenAvalanche photodiodeStress (linguistics)Materials scienceLayer (electronics)Single-photon avalanche diodeOperating temperatureDepletion regionSiliconDetectorElectrical engineeringSemiconductorOpticsPhysicsVoltageNanotechnologyEngineeringBreakdown voltage

Abstract

fetched live from OpenAlex

Devices with internal gain, such as Low Gain Avalanche Diodes (LGADs) demonstrate O(30) ps timing resolution and they play a crucial role in High Energy Physics (HEP) experiment, among other applications. Similarly, resistive silicon devices, such as AC-coupled Low Gain Avalanche Diodes sensors achieve a fine spatial resolution while maintaining the LGADs timing resolution. Devices of both types, with varying gain-layer width and doping characteristics, are produced at Brookhaven National Laboratory. In view of their application to space-based HEP experiments, they are stress-tested against various operating conditions. The performance of these devices is compared with that produced by Hamamatsu. We study how different gain layer widths impact the expected performance of these devices. The challenging operating conditions in outer space impose challenging constraints on the operation performance, against temperature fluctuations, for example. Therefore, devices with different depletion layers and implantation characteristics are stress tested to understand their performance.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.997

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)0.0040.000

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 head0.012
GPT teacher head0.232
Teacher spread0.220 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

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