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Record W1862491301 · doi:10.1109/eeis.1995.513840

Influence of high fluence neutron irradiation on forward current of semiconductor detectors

2002· article· en· W1862491301 on OpenAlexaff
M. Acciarri, N. Croitoru, C. Leroy, S. Pensotti, P.G. Rancoita, M. Redaelli, A. Seidman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadiation Effects in Electronics
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsIrradiationOmegaFluenceElectrical resistivity and conductivityDiodePhysicsNeutronAnalytical Chemistry (journal)SiliconNeutron fluxAtomic physicsMaterials scienceOptoelectronicsNuclear physicsChemistry

Abstract

fetched live from OpenAlex

Forward current-voltage (I-V) characteristics of non-irradiated and irradiated p/sup +/-n-n/sup +/ detectors, at neutron fluences (/spl phi/) up to 10/sup 14/ n/cm/sup 2/, were measured and the obtained data were analyzed. The I/sub f/-V/sub f/ characteristics confirmed the existence of a critical fluences (/spl phi//sub c/>5/spl times/10/sup 11/ n/cm/sup 2/), where abrupt changes in the dependence of forward current on voltage appear. We found that at /spl phi/>/spl phi//sub c/ a similar abrupt drop of the rectification ratio (Rec) appears, but a large reverse voltage could, nevertheless, be applied. The series resistivity (/spl rho/), calculated from the I/sub f/-V/sub f/ characteristics, for both non-irradiated (NI) and irradiated detectors, shows that for NI detectors the resistivity is of the same order of magnitude like that of the silicon bulk (1200 /spl Omega/ cm). The resistivity (/spl rho/) for irradiated devices increases (with increasing values of /spl phi/, of up to 10/sup 14/ n/cm/sup 2/), up to about 4/spl times/10/sup 3/ /spl Omega/ cm. As a result of the increase of /spl rho/ with /spl phi/, the initial p/sup +/-n-n/sup +/ diode becomes a p/sup +/-/spl nu/-n/sup +/ device. This study explains the rather good charge collection efficiency in spite of strongly affected physical properties.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.503

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 head0.007
GPT teacher head0.204
Teacher spread0.197 · 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 designSimulation or modeling
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
Published2002
Admission routes1
Has abstractyes

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