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Record W4401486145 · doi:10.1016/j.nima.2024.169729

TCAD simulations of humidity-induced breakdown of silicon sensors

2024· article· en· W4401486145 on OpenAlexafffund
I. Ninca, I. Bloch, B. Brüers, V. Fadeyev, J. Fernández-Tejero, C. E. Jessiman, J. Keller, C. Klein, T. Koffas, H. Lacker, Peng Li, C. Scharf, E. J. Staats, M. Ullán, Y. Unno

Bibliographic record

VenueNuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsSimon Fraser UniversityCarleton UniversityTRIUMF
FundersEuropean Regional Development FundNatural Sciences and Engineering Research Council of CanadaAgencia Estatal de InvestigaciónSimon Fraser UniversityCERNCanada Foundation for InnovationTRIUMFCarleton UniversityU.S. Department of Energy
KeywordsRelative humidityMaterials scienceHumidityBreakdown voltageSiliconOptoelectronicsElectric fieldTransient (computer programming)VoltageElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

The breakdown voltage of silicon sensors is known to be affected by the ambient humidity. To understand the sensor’s humidity sensitivity , Synopsys TCAD was used to simulate n-in-p sensors for different effective relative humidities . Photon emission of hot electrons was imaged with a microscope to locate breakdown in the edge-region of the sensor. The Top-Transient Current Technique was used to measure charge transport near the surface in the breakdown region of the sensor. Using the measurements and simulations, the evolution of the electric field with relative humidity and the carrier densities towards breakdown in the periphery of p-bulk silicon sensors are investigated.

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)0.001
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 score0.094
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.065
GPT teacher head0.383
Teacher spread0.317 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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