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A Correlative Study of Silicon Carbide Power Devices Using Atom Probe Tomography and Transmission Electron Microscopy

2023· article· en· W4388494366 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings - International Symposium for Testing and Failure Analysis · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials Characterization Techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAtom probeDopantMaterials scienceTransmission electron microscopySilicon carbideSemiconductor deviceSiliconOptoelectronicsTransistorMolecular physicsNanotechnologyDopingChemistryElectrical engineeringMetallurgy

Abstract

fetched live from OpenAlex

Abstract Atom probe tomography is used to characterize the 3D Al dopant distribution within the gate diffusion region of a deconstructed SiC n-channel junction field effect transistor. The data reveals extensive inhomogeneities in the dopant distribution, which manifests as large Al clusters - some of which are ring-shaped and indicative of dopant segregation to lattice defects in the SiC. The presence of defects in the SiC is confirmed by transmission electron microscopy of an identical region. Factors that may impact the atom probe data quality and consequently complicate data interpretation are considered, and their severity evaluated. The possible origin of the lattice defects in the SiC and the corresponding implications for device performance and reliability are also discussed. Overall, the utility of atom probe tomography and correlative transmission electron microscopy for revealing potential failure mechanisms of next-generation semiconductor devices is demonstrated.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.288
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.270
Teacher spread0.260 · 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