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Record W3089033973 · doi:10.1149/09805.0447ecst

(Invited) Probing Semiconductor Heterostructures from the Atomic to the Micrometer Scale

2020· article· en· W3089033973 on OpenAlexaff
Sebastian Koelling, Simone Assali, Mahmoud R. M. Atalla, Aashish Kumar, Anis Attiaoui, Mario Lodari, Amir Sammak, Giordano Scappucci, Oussama Moutanabbir

Bibliographic record

VenueECS Transactions · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials Characterization Techniques
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsHeterojunctionMaterials scienceSemiconductorMicrometerNanotechnologyNanometreAtom probeDopantNanowireOptoelectronicsPhotonicsNanoscopic scaleTransistorDopingOpticsPhysics

Abstract

fetched live from OpenAlex

Atom Probe Tomography (APT) has significantly advanced our ability to characterize buried interfaces and to map the three-dimensional distribution of atomic constituents like dopants in modern day electronic nano-devices. This precise mapping of the chemical composition and buried interfaces is also highly sought-after in the implementation and optimization of a variety of emerging photonic devices with dimensions of up to several micrometers as opposed to the few ten nanometers of modern day transistors. Herein, we show that APT is poised to contribute to the development of these devices by adapting the Focused Ion beam based preparation of APT specimen, typically used for preparing semiconductor samples. Using in-situ grown nanowires as sacrificial masks during specimen preparation, we demonstrate APT analyses of micrometer-sized volumes of semiconductor heterostructures.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.095

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0290.018

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 head0.012
GPT teacher head0.200
Teacher spread0.188 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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