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Investigating the impact of the diode edge geometry on the effective active area of silicon sensors using AREA-X

2025· article· en· W4410367719 on OpenAlexafffund
A. Poley, G. I. Dyckes, J. Fernández-Tejero, C. Fleta, D. Sperlich, M. Ullán

Bibliographic record

VenueJournal of Instrumentation · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsSimon Fraser UniversityTRIUMF
FundersAgencia Estatal de InvestigaciónEuropean Regional Development FundNatural Sciences and Engineering Research Council of CanadaDiamond Light SourceAlexander von Humboldt-Stiftung
KeywordsSiliconEnhanced Data Rates for GSM EvolutionMaterials scienceDiodeOptoelectronicsGeometryPIN diodeEngineering physicsOpticsElectrical engineeringComputer sciencePhysicsTelecommunicationsEngineeringMathematics

Abstract

fetched live from OpenAlex

Abstract Sensors for particle tracking detectors are required to provide a maximum active area in addition to fulfilling performance criteria concerning radiation hardness, charge collection and operating conditions (e.g. leakage current, depletion voltage and breakdown voltage). While the requirement for optimised coverage within the tracking detector necessitates a slim sensor edge between active region and physical sensor edge, wider edge regions were found to be beneficial for the sensor performance during an early prototyping phase. In order to study the impact of differently sized edge regions, test structures were used to compare their individual active regions. Measurements of each diode were performed using a micro-focused X-ray beam to map its respective active area. This paper presents measurements of these test structures using AREA-X showing that the active area of a silicon particle tracking sensor does not only depend on the size of its bias ring, but also the size and configuration of its edge structure.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.153

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 head0.021
GPT teacher head0.293
Teacher spread0.273 · 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 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
Published2025
Admission routes2
Has abstractyes

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