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Record W2148482442 · doi:10.1109/tns.2007.910873

Enhanced Degradation in Power MOSFET Devices Due to Heavy Ion Irradiation

2007· article· en· W2148482442 on OpenAlexfundno aff
James A. Felix, M.R. Shaneyfelt, James R. Schwank, Scott M. Dalton, P.E. Dodd, Joseph Brandon Witcher

Bibliographic record

VenueIEEE Transactions on Nuclear Science · 2007
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsnot available
FundersSandia National LaboratoriesTRIUMF
KeywordsIrradiationMaterials scienceTransistorIonLinear energy transferMOSFETOptoelectronicsAbsorbed dosePower MOSFETElectronSwift heavy ionThreshold voltageAtomic physicsVoltageElectrical engineeringPhysicsNuclear physicsEngineering

Abstract

fetched live from OpenAlex

Large, unexpected shifts in the current-voltage (IV) characteristics of commercial power MOSFETs irradiated with heavy ions have been observed. The shifts can be more than sixty-five times larger than the shifts resulting from total dose irradiation with gamma rays or electrons, and are shown to strongly depend on both the irradiation bias and the ion linear energy transfer (LET). These large shifts are a significant concern for devices intended to operate in low power space applications because it is shown that they can lead to off-state leakage currents greater than 1 A. The data are consistent with the formation of parasitic transistors resulting from the microdose deposited in the gate oxides of these devices by the heavy ions. These results have significant implications for hardness assurance testing of MOS devices for use in space.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.236
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), 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

Citations74
Published2007
Admission routes1
Has abstractyes

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