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Record W2806023802 · doi:10.1038/s41598-018-25416-6

Engineering of III-Nitride Semiconductors on Low Temperature Co-fired Ceramics

2018· article· en· W2806023802 on OpenAlexaff
José Mánuel, J. Jiménez, Francisco M. Morales, Bertrand Lacroix, Antonio J. Santos, R. Garcı́a, E. Blanco, M. Domı́nguez, María de la Luz Ramírez, Ana M. Beltrán, Dimiter Alexandrov, Jonny Tot, Robert Dubreuil, Valentin Videkov, Svetozar Andreev, Boriana Tzaneva, Heike Bartsch, Jonas Breiling, J. Pezoldt, M. Fischer, Jens Müller

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsLakehead University
FundersServicios Centrales de Investigación Científica y TecnológicaAlexander von Humboldt-Stiftung
KeywordsCeramicMaterials scienceSubstrate (aquarium)HeterojunctionChemical vapor depositionNitrideOptoelectronicsDeposition (geology)TemplateSemiconductorNanotechnologyComposite materialLayer (electronics)

Abstract

fetched live from OpenAlex

This work presents results in the field of advanced substrate solutions in order to achieve high crystalline quality group-III nitrides based heterostructures for high frequency and power devices or for sensor applications. With that objective, Low Temperature Co-fired Ceramics has been used, as a non-crystalline substrate. Structures like these have never been developed before, and for economic reasons will represent a groundbreaking material in these fields of Electronic. In this sense, the report presents the characterization through various techniques of three series of specimens where GaN was deposited on this ceramic composite, using different buffer layers, and a singular metal-organic chemical vapor deposition related technique for low temperature deposition. Other single crystalline ceramic-based templates were also utilized as substrate materials, for comparison purposes.

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 categoriesInsufficient payload (model declined to judge)
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.236
Teacher spread0.226 · 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.

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

Citations10
Published2018
Admission routes1
Has abstractyes

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